Proceedings of the 31st International Conference on Computational Linguistics
PreAct: Prediction Enhances Agent’s Planning Ability (2025.coling-main)
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| Challenge: | Existing methods to analyze Markov decision processes (MDPs) are based on chain-of-thought (COT) and historical thought, action, and observation. |
| Approach: | They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions. |
| Outcome: | The proposed model outperforms the ReAct method in completing complex tasks and is more efficient when paired with other memory or selection strategy techniques. |
The PRECOM-SM Corpus: Gambling in Spanish Social Media (2025.coling-main)
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| Challenge: | ESTUDES de Sanidad: 20.1% of youngsters between 14 and 18 years old have gambled money online . ESTUDES: 2021: 17.9% of students who have gamble would be predisposed to gambling-related problems. |
| Approach: | This paper collects text from online Spanish-speaking communities and analyses it to detect gambling addiction problem. |
| Outcome: | The proposed corpus collects text from Spanish-speaking communities and analyzes it . it finds patterns in written language from frequent and infrequent users . 20.1% of youngsters between 14 and 18 years old have gambled money in person or online . |
How Well Can a Long Sequence Model Model Long Sequences? Comparing Architectural Inductive Biases on Long-Context Abilities (2025.coling-main)
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| Challenge: | Recent advances in system engineering and model design have enabled extended context models. |
| Approach: | They propose to scale up models that are purported to support extended contexts . they show that recurrent models still suffer in the same settings as long-context LLMs if attention is given to them . |
| Outcome: | The proposed models can extend to infinite sequence length, but they suffer in the same settings as long-context models with attention. |
Sequential Fusion of Text-close and Text-far Representations for Multimodal Sentiment Analysis (2025.coling-main)
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| Challenge: | Multimodal Sentiment Analysis (MSA) aims to identify human attitudes from diverse modalities such as visual, audio and text. |
| Approach: | They propose a framework to combine text-close and text-far representations to refine multimodal representations from multimodal data. |
| Outcome: | The proposed framework explores similarities and differences between text and audio/visual modalities and fuses extracted representations more effectively. |
PoemBERT: A Dynamic Masking Content and Ratio Based Semantic Language Model For Chinese Poem Generation (2025.coling-main)
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| Challenge: | Despite the lack of pre-trained models for ancient Chinese poetry, the unique artistry and structural nuances of Chinese poetry present complex challenges for machine learning in creative applications. |
| Approach: | They propose a BERT-based model incorporating sentiment and pinyin embeddings into the model, enhancing its sensitivity to emotional information and addressing challenges posed by the phenomenon of multiple pronunciations for the same Chinese character. |
| Outcome: | The proposed model outperforms existing models on poem generation and sentiment classification tasks and is state-of-the-art in automatic and manual evaluations. |
CDAˆ2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis (2025.coling-main)
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| Challenge: | Domain adaptation is widely employed in cross-domain sentiment analysis, but concerns have been raised regarding their robustness and sensitivity to data distribution shift. |
| Approach: | They propose a framework CDA2 for cross-domain adaptation in low-resource sentiment analysis which employs counterfactual diffusion augmentation. |
| Outcome: | The proposed framework generates high-quality counterfactual target samples and achieves state-of-the-art performance on benchmark datasets. |
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)
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| Challenge: | Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. |
| Approach: | They propose a benchmark to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation. |
| Outcome: | The proposed benchmark evaluates 12 well-known large language models to determine the correctness of provided code solutions. |
Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching (2025.coling-main)
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| Challenge: | Entity matching (EM) is a critical step in entity resolution (ER). |
| Approach: | They propose a method that incorporates record interactions from different perspectives. |
| Outcome: | The proposed framework improves on 8 ER datasets and 10 LLMs and achieves higher efficiency and effectiveness. |
InstructGEC: Enhancing Unsupervised Grammatical Error Correction with Instruction Tuning (2025.coling-main)
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| Challenge: | Recent studies have proposed methods of generating synthetic data for unsupervised GEC . however, the cost of such methods is high and the quality of the data is poor . |
| Approach: | They propose a method to generate synthetic data automatically for unsupervised GEC . they use a masking strategy to mask an erroneous sentence and the instruction consistently . |
| Outcome: | The proposed method outperforms state-of-the-art unsupervised methods on English and Chinese GEC datasets. |
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)
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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang
| Challenge: | Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses. |
| Approach: | They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses. |
| Outcome: | The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. |
Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)
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| Challenge: | Current efforts to integrate MMKG with pretraining are scarce. |
| Approach: | They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking. |
| Outcome: | The proposed method achieves SOTA performance across ten datasets. |
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios (2025.coling-main)
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Junjie Ye, Guanyu Li, SongYang Gao, Caishuang Huang, Yilong Wu, Sixian Li, Xiaoran Fan, Shihan Dou, Tao Ji, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing evaluations of tool learning focus on validation of tools for large language models with expected outcomes, but this focus ignores the complex capabilities required for LLMs to effectively use tools. |
| Approach: | They propose a fine-grained system for evaluation of large language models’ tool learning capabilities in authentic scenarios. |
| Outcome: | The proposed system examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. |
Federated Incremental Named Entity Recognition (2025.coling-main)
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| Challenge: | Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting. |
| Approach: | They propose a model which allows for catastrophic forgetting of old entity types . they propose adaptive pseudo labeling and a prototypical relation distillation loss . |
| Outcome: | The proposed model overcomes catastrophic forgetting problem on old entity types with semantic shift. |
Large Language Models are Good Annotators for Type-aware Data Augmentation in Grammatical Error Correction (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated outstanding performance in many downstream tasks due to their emergent and in-context learning abilities. |
| Approach: | They propose a method that considers LLMs as annotators for type-aware data augmentation in GEC tasks. |
| Outcome: | The proposed method can generate consistent and typeaware data, which could improve the performance of large language models. |
Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication (2025.coling-main)
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| Challenge: | Existing research indicates that disfluencies can constitute up to 5.9% of words in spontaneous speech, with repetitions accounting for over half of these disfluency. |
| Approach: | They propose to use a dataset to analyze reduplication and repetition in speech using computational linguistics to evaluate transformer-based models. |
| Outcome: | The proposed models achieve macro F1 scores of up to 85.62% in Hindi, 83.95% in Telugu, and 84.82% in Marathi for reduplication-repetition classification. |
Learning to Verify Summary Facts with Fine-Grained LLM Feedback (2025.coling-main)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced the text summarization performance, but hallucination issues still occur in summaries. |
| Approach: | They propose a large-scale dataset containing fine-grained factual feedback on summaries that can be fine tuned by using Large Language Models (LLMs) they employ 10 distinct LLMs for diverse summary generation and Llama-3-70B-Instruct for feedback. |
| Outcome: | The proposed model outperforms models trained on smaller human-annotated datasets while maintaining high performance. |
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models (2025.coling-main)
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| Challenge: | Recent research in large language models (LLMs) has focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLM to small language models at downstream clients. |
| Approach: | They propose a parameter-efficient federated mutual knowledge transfer framework for large and small language models that allows for token alignment and selective knowledge transfer between client-side LLMs and a server-side SLM. |
| Outcome: | The proposed framework enhances the performance of both LLMs and SLMs with clients' unique domain insights while preserving the server's LLM and client's unique domain insight. |
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation (2025.coling-main)
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| Challenge: | Existing graph-based multimodal emotion recognition methods fail to capture dynamic changes in emotions. |
| Approach: | They propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) which combines dynamic changes of emotions to capture temporal dependencies of speakers’ emotions. |
| Outcome: | The proposed model can capture the temporal dependencies caused by dynamic changes in emotions and can improve on two publicly available multimodal emotion recognition datasets. |
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding (2025.coling-main)
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| Challenge: | Object categories are typically organized into a multi-granularity taxonomic hierarchy . traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. |
| Approach: | They propose a framework that combines vision-language models with a deeper exploitation of the hierarchy. |
| Outcome: | The proposed framework shows significant improvements on 11 diverse visual recognition benchmarks. |
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025.coling-main)
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Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, Heng Ji
| Challenge: | Existing research has focused on enhancing the retrieval stage and optimizing the representation of the database. |
| Approach: | They propose a framework to improve generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
| Outcome: | The proposed framework improves generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
Style Over Substance: Evaluation Biases for Large Language Models (2025.coling-main)
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| Challenge: | Ranking the relative performance of large language models based on Elo ratings is gaining popularity . however, the extent to which humans and LLMs are capable evaluators remains uncertain . |
| Approach: | They propose to evaluate machine-generated text across multiple dimensions using the Elo rating system . they propose to use crowd-sourced and expert annotators to rank models based on Elo ratings . |
| Outcome: | The proposed method improves the quality of LLM-based evaluations, but there is no improvement in crowd-sourced evaluations. |
Multimodal Aspect-Based Sentiment Analysis under Conditional Relation (2025.coling-main)
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| Challenge: | Existing methods to analyze social media sentiments rely on image-based aspects. |
| Approach: | They propose a multi-task framework to extract aspect terms from text-image pairs and identify their sentiments. |
| Outcome: | The proposed framework outperforms existing methods on a text-image dataset. |
Semantic Role Labeling of NomBank Partitives (2025.coling-main)
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| Challenge: | Semantic role labeling (SRL) is a way to represent semantic concepts via labeled predicate/argument pairs. |
| Approach: | They describe a semantic role labeling task that uses a set of predicate/argument pairs to represent semantic concepts. |
| Outcome: | The highest scoring system achieves an F1 of 91.74% using “gold” parses from the Penn Treebank and 91.12% when using the Berkeley Neural parser. |
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation (2025.coling-main)
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| Challenge: | Recent advances in large language models have enabled in-context learning (ICL)-based methods to outperform fine-tuning approaches for text-to-SQL tasks. |
| Approach: | They propose a method that leverages multiple prompts to explore a broader search space for possible answers and effectively aggregate them. |
| Outcome: | The proposed method achieves execution accuracies of 65.5% and 89.6% on BIRD and Spider benchmarks. |
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) can attain professional-level proficiency in specific domains through fine-tuning. |
| Approach: | They propose a multi-modal LLM that aligns molecular structures with natural language via an instruction-tuning approach. |
| Outcome: | InstructMol surpasses existing models and reduces the gap with specialists in drug discovery tasks. |
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information. |
| Approach: | They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset. |
AdminSet and AdminBERT: a Dataset and a Pre-trained Language Model to Explore the Unstructured Maze of French Administrative Documents (2025.coling-main)
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| Challenge: | Pre-trained language models are used to analyze documents but administrative texts are unstructured and do not perform well. |
| Approach: | They propose a French pre-trained language model for the administrative domain . they compare it with a general domain language model and a large language model . |
| Outcome: | The proposed model improves performance on administrative and general domains. |
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context LLMs focus on generic tasks that are not necessarily aligned with real-world applications. |
| Approach: | They propose to augment existing ELITR corpus by adding 271 manually crafted questions with their ground-truth answers and noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
| Outcome: | The proposed benchmark augments the existing ELITR corpus by adding 271 manually crafted questions with ground-truth answers, as well as noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
Positive Text Reframing under Multi-strategy Optimization (2025.coling-main)
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| Challenge: | Existing positive reframing models can be fine-tuned to achieve acceptable results, but generating fluent, diverse text remains a challenge. |
| Approach: | They propose a positive reframing sentiment reward and content preservation reward framework . they propose re-ranking methods that optimize for style and consistency . |
| Outcome: | The proposed framework improves on unconstrained and controlled positive reframing tasks. |
RAM2C: A Liberal Arts Educational Chatbot based on Retrieval-augmented Multi-role Multi-expert Collaboration (2025.coling-main)
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| Challenge: | Empirical evaluations indicate that RAM2C-empowered LLMs excel in Chinese reading teaching, offering more personalized, and ethically safe teaching response. |
| Approach: | They propose a framework to retrieve large language models into educational dialogues and organize them into multi-experts groups with distinct roles to generate the data. |
| Outcome: | Empirical evaluations show that RAM2C-empowered LLMs excel in Chinese reading teaching, offering more personalized, and ethically safe teaching response. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)
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| Challenge: | Existing mPLMs that handle non-transliterated data are not sufficient to train crosslingual models. |
| Approach: | They propose a framework to transliterate related languages into a common script by exploiting existing mPLMs and their tokenizer without any training. |
| Outcome: | The proposed framework can create strong baselines for data that is transliterated into a common script by exploiting an existing mPLM and its tokenizer without any training. |
Two-stage Incomplete Utterance Rewriting on Editing Operation (2025.coling-main)
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| Challenge: | Existing methods to generate rewritten utterances based on dialogue context ignore coreference and ellipsis in dialogues. |
| Approach: | They propose a framework where the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations. |
| Outcome: | The proposed framework outperforms the existing models on three IUR datasets. |
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics. |
| Approach: | They propose a system that captures relevant information within a fixed window size and provides precise answers to queries. |
| Outcome: | The proposed system can read Harry Potter within 30s and accurately answer the questions. |
SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)
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| Challenge: | Recent advances in graph neural networks have made it difficult to capture user preferences. |
| Approach: | They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage. |
| Outcome: | The proposed model reduces dimensionality and denoises the original data. |
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL (2025.coling-main)
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Bing Wang, Changyu Ren, Jian Yang, Xinnian Liang, Jiaqi Bai, LinZheng Chai, Zhao Yan, Qian-Wen Zhang, Di Yin, Xing Sun, Zhoujun Li
| Challenge: | Recent LLM-based Text-to-SQL methods suffer from performance degradation on “huge” databases and complex user questions that require multi-step reasoning. |
| Approach: | They propose a framework that integrates a decomposer agent and auxiliary agents to generate SQL queries from natural language text. |
| Outcome: | The proposed framework achieves comparable execution accuracy on SQL-Llama tasks compared to the baseline model. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
Knowledge Graph Entity Typing with Curriculum Contrastive Learning (2025.coling-main)
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| Challenge: | Existing knowledge graphs suffer from incomplete type annotations because they are manually constructed by domain experts. |
| Approach: | They propose a CCLET model using the Curriculum Contrastive Learning strategy for KGET to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) they define the difficulty of the course by controlling the level of added noise and aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. |
| Outcome: | The proposed model outperforms state-of-the-art models and is highly accurate across multiple learning environments. |
The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities, but their security implications have been overlooked. |
| Approach: | They propose a “jailbreak function” attack method that exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters. |
| Outcome: | The proposed attack exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters on six state-of-the-art LLMs. |
Adapters Selector: Cross-domains and Multi-tasks LoRA Modules Integration Usage Method (2025.coling-main)
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| Challenge: | Parameter-Efficient fine-tuning (PEFT) adapts large language models to specific domains by updating only a small portion of the parameters. |
| Approach: | They propose a framework for better integrating usage of multiple adapters by training a middleman adapter to select the appropriate adapter for inference. |
| Outcome: | The proposed framework can perform cross-domain multi-tasks effectively through the utilization of a compact model in combination with multiple LoRA modules. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
Debiasing by obfuscating with 007-classifiers promotes fairness in multi-community settings (2025.coling-main)
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| Challenge: | a number of studies have focused on the mitigation of biases in text classifiers. |
| Approach: | They propose an obfuscation-based data augmentation debiasing approach to reduce bias . they add to the training data *obfuses* versions of *all* false positive instances . |
| Outcome: | The proposed approach reduces bias for almost all of the tests without sacrificing false positive rates or F1 scores for minority or majority communities. |
Graph Representation Learning in Hyperbolic Space via Dual-Masked (2025.coling-main)
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| Challenge: | Existing MR-based methods do not fully consider deep node and structural information. |
| Approach: | They propose a graph dual-masked self-supervised graph representation learning framework in hyperbolic space that masks nodes and edges and performs node aggregation. |
| Outcome: | The proposed method is superior in downstream tasks such as node classification and link prediction. |
Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)
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| Challenge: | Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance . |
| Approach: | They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs . |
| Outcome: | The proposed model outperforms benchmark models on multiple public datasets. |
Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) generate redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences. |
| Approach: | They propose a framework that enhances reranking for recommendation with large language models through user preference retrieval. |
| Outcome: | The proposed framework improves reranking for recommendation with large language models through user preference retrieval on three real-world public datasets. |
SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task (2025.coling-main)
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| Challenge: | Existing efforts to bolster LLMs’ proficiency in Cypher generation are hindered by the lack of annotated datasets of Query-Cypher pairs. |
| Approach: | They propose a method for constructing a synthetic Query-Cypher pair dataset using LLM prompting and template-filling. |
| Outcome: | The proposed method enhances the performance of LLMs on Text2Cypher task via SFT. |
Language Models Encode the Value of Numbers Linearly (2025.coling-main)
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| Challenge: | Existing studies show that large language models encode the value of numbers linearly. |
| Approach: | They construct a large language model and use linear probes to read out input numbers from hidden states. |
| Outcome: | The proposed model encodes the value of numbers linearly, and can store the outputs via simple vector additions. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding (2025.coling-main)
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| Challenge: | Current approaches for fine-tuning datasets rely on expensive sample ranking processes . data set pruning aims to select a subset of a dataset for efficient model training . |
| Approach: | They propose a method that uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance. |
| Outcome: | The proposed method significantly reduces training and storage costs while maintaining model effectiveness. |
SLARD: A Chinese Superior Legal Article Retrieval Dataset (2025.coling-main)
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| Challenge: | Existing retrieval methods struggle to achieve ideal results, a study finds . existing large language models lack prior knowledge of the content of superior legal articles . |
| Approach: | They propose to use a Chinese superior legal article retrieval dataset to find relevant articles with higher legal effectiveness. |
| Outcome: | The proposed dataset shows that existing retrieval methods struggle to achieve ideal results. |
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations (2025.coling-main)
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| Challenge: | Existing retrieval-based methods for long-term conversations face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. |
| Approach: | They propose a framework that eschews traditional retrieval modules and memory databases and adopts a “One-for-All” approach to manage memory generation, compression, and response generation. |
| Outcome: | The proposed framework produces more nuanced and human-like experiences than retrieval-based methods. |
Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach (2025.coling-main)
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| Challenge: | errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems. |
| Approach: | They propose a method to assess end-to-end grammatical error correction systems using alignment-based alignment methods that reproduce and improve results from existing evaluation tools. |
| Outcome: | The proposed method reproduces and improves results from existing evaluation tools, such as errant, even when applied to raw text input. |
LLMs on interactive feature collections with implicit dynamic decision strategy (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle to efficiently narrow down the search space . external engineered systems may not fully utilize the inherent problem-solving capabilities of LLMs . |
| Approach: | They propose to implicitly guide Large Language Models to enhance their interactive feature collection abilities within a single prompt. |
| Outcome: | The proposed approach improves the performance of large language models in real-world scenarios. |
Pre-trained Semantic Interaction based Inductive Graph Neural Networks for Text Classification (2025.coling-main)
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| Challenge: | Existing methods for text classification have vanishing or exploding gradients when dealing with long sequences, making it difficult to handle long-distance dependencies. |
| Approach: | They propose a graph neural network based on pre-trained semantic interaction called PaSIG . they construct a text-word heterogeneity graph and use context representation capability . |
| Outcome: | The proposed model outperforms existing methods on five datasets and achieves state-of-the-art performance. |
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM (2025.coling-main)
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| Challenge: | Existing methods for generating follow-up questions are limited to shallow contextual questions that are uninspiring and have a large gap to the human level. |
| Approach: | They propose a three-stage external knowledge-enhanced follow-up question generation method which generates questions by identifying contextual topics, building a knowledge graph online, and finally combining these with a large language model to generate the final question. |
| Outcome: | The proposed method generates questions by identifying contextual topics, building a knowledge graph (KG) online, and finally combining these with a large language model to generate the final question. |
AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification (2025.coling-main)
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| Challenge: | Aspect-level Sentiment Classification (ALSC) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of a review text toward each corresponding aspect. |
| Approach: | They propose a novel Aspect Graph Construction and Learning method that harnesses aspect connections to construct a domain aspect graph and iteratively updates it to enhance its domain expertise. |
| Outcome: | The proposed method can bridge unseen aspects with seen aspects, enhancing the model's generalization capability. |
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks. |
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)
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| Challenge: | E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications . |
| Approach: | They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding . |
| Outcome: | The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. |
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations (2025.coling-main)
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Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
| Challenge: | Lack of training data leads to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. |
| Approach: | They propose a tree-based LLM recommendation framework which structures all items into an item tree to improve the efficiency of LLM’s item retrieval. |
| Outcome: | The proposed framework outperforms the baseline model in the A/B test on Huawei industrial system. |
Collaborative Document Simplification Using Multi-Agent Systems (2025.coling-main)
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| Challenge: | Document simplification requires complex factors such as technical terminology, metaphors, and overall coherence. |
| Approach: | They propose a multi-agent framework for document simplification based on large language models that emulates the collaborative process of a human expert team through the roles played by multiple agents. |
| Outcome: | The proposed framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification. |
Distilling Rule-based Knowledge into Large Language Models (2025.coling-main)
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| Challenge: | Recent advances in large language models have broadened their applicability across diverse realworld scenarios. |
| Approach: | They propose to encode rule-based knowledge into large language models by using strong in-context abilities to extract the knowledge from the textual rules and then explicitly encode it into the parameters of LLMs. |
| Outcome: | The proposed learning paradigm is much more efficient than example-based learning in both sample size and generalization ability. |
Exploring Backdoor Vulnerabilities of Chat Models (2025.coling-main)
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| Challenge: | Recent studies show that Large Language Models (LLMs) are susceptible to a security threat known as Backdoor Attack. |
| Approach: | They propose a backdoor attack method that distributes trigger scenarios across user inputs in different rounds and makes the backdoor be triggered only when all trigger scenarios have appeared in the historical conversations. |
| Outcome: | The proposed method achieves high attack success rates on chat models while maintaining normal capabilities on providing helpful responses to benign user requests. |
Towards the Machine Translation of Scientific Neologisms (2025.coling-main)
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| Challenge: | Scientific research continually discovers and invents new concepts, which are then referred to by new terms, neologisms, or nenonyms. |
| Approach: | They propose to leverage term definitions to translate neologisms with Large Language Models . they find that LLMs generate terms from co-hyponyms and terms sharing the same derivation paradigm . |
| Outcome: | The proposed model can generate terms from co-hyponyms and terms sharing the same derivation paradigm. |
HyperIDP: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction (2025.coling-main)
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| Challenge: | Existing studies on information diffusion prediction have focused on both macroscopic and microscopic scales. |
| Approach: | They propose a hypergraph-based model that manages both macroscopic and microscopic diffusion predictions. |
| Outcome: | The proposed model outperforms baseline models on both macroscopic and microscopic tasks. |
Enhancing multi-modal Relation Extraction with Reinforcement Learning Guided Graph Diffusion Framework (2025.coling-main)
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| Challenge: | Existing methods for cross-modal relation extraction focus on single-modal data, which limits their use in real-world situations. |
| Approach: | They propose a framework that leverages pre-trained models to encode multi-modal data into scene graphs and combine them into a cross-modal graph. |
| Outcome: | The proposed model outperforms existing methods on multi-modal relation extraction tasks. |
Non-Emotion-Centric Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy is a social psychology theory that enables individuals to comprehend each other's experiences and emotions, thereby fostering more intimate interpersonal relationships. |
| Approach: | They propose a framework for empathetic dialogue generation based on contrastive learning and context-sensitive entity and social commonsense that punishes responses with incorrect emotions and improves the quality of emotions. |
| Outcome: | The proposed framework improves the quality of empathetic generation and generates more diverse responses in comparison with the state-of-the-art baselines. |
Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)
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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader. |
| Approach: | They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers. |
| Outcome: | The proposed method improves the quality of evidence passages under zero-shot scenarios. |
Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding (2025.coling-main)
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| Challenge: | Existing methods analyze training data with member and non-member contexts, overlooking potential insights from both member and not-member. |
| Approach: | They propose a method that leverages asymmetric distributional shifts induced by member and non-member contexts through contrastive decoding to enhance membership inference. |
| Outcome: | The proposed approach outperforms the current state-of-the-art on the WikiMIA benchmark and is robust against various text manipulation techniques. |
Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields (2025.coling-main)
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| Challenge: | citation age is a key factor in determining whether older works are cited in scientific journals or not. |
| Approach: | They examine the tendency of NLP to cite older work across 20 fields of study over 43 years (1980–2023) . they put NLP’s propensity to citation older work in the context of these 20 other fields to see whether differences can be observed . |
| Outcome: | The trend is strongest in NLP and ML research (-12.8% and -5.5% in citation age from previous peaks) |
Low-Resource Fast Text Classification Based on Intra-Class and Inter-Class Distance Calculation (2025.coling-main)
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| Challenge: | Existing methods based on neural networks and pre-trained models consume substantial memory for training and text-graph construction. Existing models require access to the test dataset during the training phase, which means that when encountering new text data, the existing model needs to be retrained. |
| Approach: | They propose a low-resource and fast text classification model called LFTC to address these challenges by mining regularity information within intra-class data. |
| Outcome: | The proposed model improves performance and processing time under limited computational and data resources on 9 publicly available datasets. |
Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)
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| Challenge: | Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails. |
| Approach: | They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks. |
| Outcome: | The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses. |
LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks (2025.coling-main)
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| Challenge: | Existing methods emphasize contextual semantics while others pay more attention to explicit logical features. Existing models utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings. |
| Approach: | They propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph) they employ conjunction and punctuation marks as two types of edges to construct a dual graph. |
| Outcome: | The proposed method improves the GCN and employs conjunction and punctuation marks as two types of edges to construct a dual graph. |
Explaining Relationships Among Research Papers (2025.coling-main)
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| Challenge: | Existing literature reviews focus on summarizing individual papers without addressing the need for expository and transition sentences to explain the relationships among multiple papers. |
| Approach: | They propose a feature-based, LLM-prompting approach to generate richer citation texts . they propose to use related work sections of scientific articles as proxy for the kind of short, customized, daily feed summaries . |
| Outcome: | The proposed approach captures complex relationships among multiple papers while generating richer citation texts. |
From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)
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| Challenge: | Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry. |
| Approach: | They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools. |
| Outcome: | The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research. |
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
| Outcome: | The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method. |
Read Before Grounding: Scene Knowledge Visual Grounding via Multi-step Parsing (2025.coling-main)
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| Challenge: | Existing VG datasets use simple textual descriptions with limited attribute and spatial information between images and text. |
| Approach: | They propose a method that transforms visual knowledge into concise, information-dense visual descriptions. |
| Outcome: | The proposed method significantly improves performance of multimodal grounding models. |
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem (2025.coling-main)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
| Approach: | They propose a role-modeling approach that employs two LLMs as generator and critic to generate and refine NLEs. |
| Outcome: | The proposed model outperforms self-refine and can perform with less powerful LLMs. |
BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation (2025.coling-main)
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| Challenge: | Knowledge distillation (KD) is a method for reducing model size while preserving performance. |
| Approach: | They propose a method to distill large language models at the logit level by transferring knowledge from a large teacher model to a smaller student model. |
| Outcome: | The proposed method outperforms supervised fine-tuning, vanilla KL loss and five other distillation methods on 13 datasets. |
Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs (2025.coling-main)
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Tamzeed Mahfuz, Satak Kumar Dey, Ruwad Naswan, Hasnaen Adil, Khondker Salman Sayeed, Haz Sameen Shahgir
| Challenge: | a new generation of English-oriented Large Language Models significantly outperforms older LLMs on low-resource languages. |
| Approach: | They compare Bengali-oriented LLMs with open-weight and closed-source LLM models . they conclude that there is a need for a Bengali model, but lacks high-quality pretraining data . |
| Outcome: | The proposed model outperforms existing models on Bengali on low-resource languages . the results highlight biases in machine-translated datasets used for Bengali NLP tasks . |
Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs (2025.coling-main)
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| Challenge: | Language ideologies are evaluative ideas or beliefs about language, such as ideas about what is "correct", "natural" or "articulate". |
| Approach: | They use gender-neutral variants more often when more explicit metalinguistic context is provided. |
| Outcome: | The findings show that language ideologies in LLMs can vary, which may be unexpected to users. |
T-MES: Trait-Aware Mix-of-Experts Representation Learning for Multi-trait Essay Scoring (2025.coling-main)
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| Challenge: | Existing methods for automatic essay scoring fail to learn trait representations and ignore correlations between trait scores. |
| Approach: | They propose a multi-trait essay scoring method based on Trait-Aware Mix-of-Experts Representation Learning. |
| Outcome: | The proposed method improves on existing methods and improves in computational efficiency. |
A Graph Interaction Framework on Relevance for Multimodal Named Entity Recognition with Multiple Images (2025.coling-main)
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| Challenge: | Existing methods to determine whether images are related to named entities are not effective in multi-image scenarios. |
| Approach: | They propose a graph interaction framework on relevance for Multimodal Named Entity Recognition with multiple images to integrate human abilities into the model. |
| Outcome: | The proposed framework achieves state-of-the-art on benchmark datasets and compares with CLIP and CLIP-based approaches. |
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
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| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
A Compressive Memory-based Retrieval Approach for Event Argument Extraction (2025.coling-main)
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| Challenge: | Existing retrieval-based EAE methods have input length constraints and the gap between the retriever and the inference model. |
| Approach: | They propose a retrieval-based retrieval mechanism that overcomes input length constraints . they use compressive memory to cache retrieved information and support continuous updates . |
| Outcome: | The proposed method outperforms retrieval-based methods on three public datasets. |
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics (2025.coling-main)
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| Challenge: | Despite the success of fine-tuning Pre-trained Language Models, they remain susceptible to out-of-distribution input. |
| Approach: | They propose a novel approach that fine-tunes Pre-trained Language Models by transFerring Training dynamics (FTFT) FTFT uses more efficient reference models and aggressive early stopping . |
| Outcome: | The proposed approach improves the robustness of fine-tuned PLMs while reducing training costs. |
PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation (2025.coling-main)
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| Challenge: | Pre-trained sequence-to-sequence models are typically pretrained on extensive raw text corpora and fine-tuned on task-specific data. |
| Approach: | They introduce a pre-trained sequence-to-sequence model trained from scratch for Khmer using carefully curated Khmer and English corpora. |
| Outcome: | The proposed model outperforms existing models on three generative tasks and is data-efficient and effective in enhancing performance across various natural language generation tasks. |
Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning (2025.coling-main)
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| Challenge: | Current knowledge graph models focus on embedding entities and relations, overlooking the broader structure of the entire knowledge graph. |
| Approach: | They propose a Temporal Knowledge Graph Reasoning model that embeds relation embeddings into the TKG. |
| Outcome: | The proposed model outperforms state-of-the-art models on five public datasets . it uses relation-aware attention mechanisms to learn relation embeddings based on query relations . |
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)
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| Challenge: | Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened. |
| Approach: | They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context. |
| Outcome: | The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps. |
Dying or Departing? Euphemism Detection for Death Discourse in Historical Texts (2025.coling-main)
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Ali Al-Laith, Alexander Conroy, Jens Bjerring-Hansen, Bolette Pedersen, Carsten Levisen, Daniel Hershcovich
| Challenge: | euphemisms are a linguistic device used to soften discussions of uncomfortable topics . euphorias are used to refer to death in a less direct manner during a period of secularization . |
| Approach: | They propose to use a corpus of Danish and Norwegian novels to detect death-related euphemisms . they use pre-trained language models to detect euphoric and literal references to death . |
| Outcome: | The proposed method improves on state-of-the-art language models. |
ITERATE: Image-Text Enhancement, Retrieval, and Alignment for Transmodal Evolution with LLMs (2025.coling-main)
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| Challenge: | a new framework for visual annotation of text-based questions is needed to improve performance . obtaining corresponding images through manual annotation often entails high costs . |
| Approach: | They propose a framework that uses visual modality to enhance the performance of text-based questions. |
| Outcome: | The proposed framework improves the alignment between text and images by using search engines or web scraping techniques. |
Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation (2025.coling-main)
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| Challenge: | Existing methods rely on user actions within the current session, overlooking the wealth of auxiliary information available. |
| Approach: | They propose a session-based recommendation model that leverages the current session graph and similar session graphs to capture the intrinsic relationships between items. |
| Outcome: | The proposed model improves on the Diginetica dataset by 2.00% and 10.70% respectively. |
CAST: Cross-modal Alignment Similarity Test for Vision Language Models (2025.coling-main)
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| Challenge: | Vision Language Models (VLMs) are typically evaluated with Visual Question Answering tasks which assess a model’s understanding of scenes. |
| Approach: | They propose to use visual question answering (VQA) to assess a model's understanding of scenes to probe for self-consistency across modalities. |
| Outcome: | The proposed test does not focus on objective accuracy but rather on whether VLMs are internally consistent in their outputs. |
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
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| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
| Approach: | They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs. |
| Outcome: | Experiments show that the proposed approach performs better than previous approaches on various benchmarks. |
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes (2025.coling-main)
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| Challenge: | Existing studies on the meaning of contextualised word embeddings (SCWEs) have not shown how meaning changes are encoded in the embeddable space. |
| Approach: | They compare pre-trained and fine-tuned contextualised word embeddings on contextual and temporal semantic change detection benchmarks. |
| Outcome: | The pre-trained and fine-tuned versions of (SCWE) and their fine- tuned versions on contextual and temporal semantic change detection benchmarks show that they represent semantic changes across all dimensions when fine--and that they are more efficient than ICA. |
Uncertainty Modelling in Under-Represented Languages with Bayesian Deep Gaussian Processes (2025.coling-main)
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| Challenge: | Existing methods for NLP modeling underrepresented languages are limited due to lack of training data and language complexities. |
| Approach: | They propose a new method that integrates prior knowledge and leverages kernel functions to quantify uncertainty in under-represented languages. |
| Outcome: | The proposed method improves prediction accuracy and measurement of uncertainty in under-represented languages. |
Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
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| Challenge: | despite the large amount of labeled datasets, there is an imbalance in data availability across languages. |
| Approach: | They explore cross-lingual knowledge transfer methods avoiding manual data curation . they use large multilingual encoders and translation systems, LLMs, and language adapters . |
| Outcome: | The proposed approaches are tested on three text classification tasks in Ukrainian . the authors show that the proposed approaches avoid manual data curation . |
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots (2025.coling-main)
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| Challenge: | Large language models have shown significant potential for robotics tasks, but a gap remains in personalization of LLMs to household preferences. |
| Approach: | They propose a framework to personalize LLM planners for household robotics . they use imitation learning and reinforced self-training to personalise the planner . |
| Outcome: | The proposed framework performs iterative planning in multi-room, partially-observable household environments, utilizing a scene graph built dynamically from local observations. |
CEHA: A Dataset of Conflict Events in the Horn of Africa (2025.coling-main)
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Rui Bai, Di Lu, Shihao Ran, Elizabeth M. Olson, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing datasets categorizing conflict events do not cover all of the fine-grained types of conflict relevant to areas like the Horn of Africa. |
| Approach: | They propose to use online news articles to categorize violent conflict events . they propose to extract event-relevance and event-types from 500 English event descriptions . |
| Outcome: | The proposed dataset categorizes conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. |
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval (2025.coling-main)
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| Challenge: | Existing methods for Statutory Article Retrieval (SAR) are vague and underspecified . however, a new approach is needed to bridge the gap between legal expertise and public understanding . |
| Approach: | They propose a framework for statutory article retrieval that leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. |
| Outcome: | The proposed framework overcomes the semantic mismatch problem when modeling each query-article pair in isolation. |
Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods for few-shot relation extraction are limited to labeled instances and rely on data labeling. |
| Approach: | They propose a partial order-centered hyperbolic representation learning framework which imposes constraints on relations on instances by modeling partial order in hyperbolical space. |
| Outcome: | The proposed framework outperforms baseline methods on three benchmark datasets on 1-shot settings lacking relation descriptions. |
Taxonomy-Guided Zero-Shot Recommendations with LLMs (2025.coling-main)
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| Challenge: | Existing approaches to deploy large language models (LLMs) into RecSys have limited prompt length, unstructured item information, and un-constrained generation of recommendations. |
| Approach: | They propose a taxonomy-guided recommendation framework that empowers LLMs with category information in a systematic approach. |
| Outcome: | The proposed framework significantly improves recommendation quality compared to zero-shot approaches. |
Enhancing Multi-party Dialogue Discourse Parsing with Explanation Generation (2025.coling-main)
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| Challenge: | Multi-party dialogue discourse parsing is an important and challenging task in natural language processing. |
| Approach: | They propose a model to integrate external knowledge from Large Language Models to analyze dialogue discourse structures and semantic relations between utterances in multi-party conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) models on two public datasets. |
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)
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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng
| Challenge: | Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. |
| Approach: | They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. |
| Outcome: | The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard. |
Polysemy Interpretation and Transformer Language Models: A Case of Korean Adverbial Postposition -(u)lo (2025.coling-main)
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| Challenge: | -(u)lo is a polysemy of the Korean adverbial postposition. |
| Approach: | They analysed attention weights of a Korean pre-trained BERT model and a fine-tuned version of -(u)lo to determine their attention weight. |
| Outcome: | The attention weights of a Korean pre-trained BERT model and a fine-tuned version show a general reduction in attention weighting and changes in the lexico-phrasal information used depending on the specific function of -(u)lo. |
A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation (2025.coling-main)
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Ekai Hashimoto, Mikio Nakano, Takayoshi Sakurai, Shun Shiramatsu, Toshitake Komazaki, Shiho Tsuchiya
| Challenge: | a slot-filling-based interview dialogue system is limited in the flexibility of information collection . authors propose a method that leverages large language models to generate new slots according to the flow of the dialogue . |
| Approach: | They propose a slot-filling dialogue system that collects information on staff careers . they incorporate abduction into the slot generation process to enable more natural conversations . |
| Outcome: | The proposed method improves the efficiency and quality of career interviews conducted by nursing managers. |
A Simple-Yet-Efficient Instruction Augmentation Method for Zero-Shot Sentiment Classification (2025.coling-main)
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| Challenge: | Existing studies have used labeled sentiment instances to instruction tune LLMs, improving zero-shot sentiment classification performance. |
| Approach: | They propose a simple-yet-efficient method which does not rely on actual labeled sentiment instances. |
| Outcome: | The proposed method outperforms LLMs tuned with more complex instruction tuning methods by 5.1 points and increases scores by 30 points. |
Improving Explainable Fact-Checking with Claim-Evidence Correlations (2025.coling-main)
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| Challenge: | Existing fact-checking systems that employ large language models fail to reveal reasoning principles behind their decision-making for the claim verdict. |
| Approach: | They propose an LLM-based fact-checking system that simulates human reasoning principles . they propose a test set to evaluate the CorXFact system in real-world and closed-domain scenarios . |
| Outcome: | The proposed system outperforms four strong fact-checking baselines in claim authenticity prediction and verdict explanation. |
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
| Approach: | They propose a framework for how semantic shifts occur over multiple time periods by using word embeddings. |
| Outcome: | The proposed framework can analyze semantic shifts over multiple time periods using word embeddings. |
A Testset for Context-Aware LLM Translation in Korean-to-English Discourse Level Translation (2025.coling-main)
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| Challenge: | Recent studies indicate that for high-resource languages, LLM surpasses encoder-decoder neural machine translation (NMT) models. |
| Approach: | They propose to construct a Korean-English discourse-level corpus with 600 text instances featuring six linguistic phenomena: lexical ambiguity, zero anaphora, slang, idiom, figurative language, and implicature. |
| Outcome: | The proposed corpus of 600 text instances features six linguistic phenomena, including lexical ambiguity, zero anaphora, slang, idiom, figurative language, and implicature. |
MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning (2025.coling-main)
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| Challenge: | LoRA is a key technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. |
| Approach: | They propose a mixture-of-shared-LoRAs model with a dropout strategy . they propose to share the upper projection matrix among different experts . |
| Outcome: | The proposed model exhibits excellent performance in both single-task and multi-task scenarios with robust out-of-domain generalization capabilities. |
A Combinatorial Approach to Neural Emergent Communication (2025.coling-main)
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| Challenge: | Existing research on emergent communication uses the Lewis signaling game . however, the training data is limited and the messages are often ineffective . |
| Approach: | They propose a combinatorial algorithm to solve the symbolic complexity for classification, which is the minimum number of symbols in the message for successful communication. |
| Outcome: | The proposed algorithm increases the number of effective symbols in the emergent language. |
Multi-perspective Preference Alignment of LLMs for Programming-Community Question Answering (2025.coling-main)
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| Challenge: | Extensive experiments on a high-quality, real-world PCQA dataset validate its accuracy and preference. |
| Approach: | They propose a multi-perspective preference alignment for programming-community question answering to generate user-centric responses. |
| Outcome: | Experiments on a high-quality, real-world PCQA dataset validate the proposed model's accuracy and preference. |
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks. |
| Approach: | They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario. |
| Outcome: | The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities. |
Exploring Unified Training Framework for Multimodal User Profiling (2025.coling-main)
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| Challenge: | Recent studies on user profiling focus on extracting multiple aspects of user attributes from textual reviews, but these studies do not fully exploit the potential of the rich multimodal data at hand. |
| Approach: | They propose a task that utilizes both review texts and their accompanying images to generate comprehensive user profiles. |
| Outcome: | The proposed training framework incorporates historical review texts and images for user profile generation. |
Acquiring Bidirectionality via Large and Small Language Models (2025.coling-main)
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| Challenge: | Existing unidirectional language models are still used for token-level classification tasks, but they lack bidirectionality. |
| Approach: | They propose to use bidirectional language models to train a small backward LM and concatenate its representations to those of an existing LM for downstream tasks. |
| Outcome: | The proposed model improves performance by more than 10 points in token-classification tasks and in rare domains. |
Enhancing One-Shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism (2025.coling-main)
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Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Yixing Xu, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum
| Challenge: | Pre-trained language models (PLMs) are robust in contextual understanding but their considerable size incurs significant computational and storage costs. |
| Approach: | They propose a Sparse-Dense-Sparse pruning framework to prune PLMs . they prune less critical connections using conventional pruning methods . |
| Outcome: | The proposed pruning framework outperforms SparseGPT and Wanda under identical sparsity. |
Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts (2025.coling-main)
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| Challenge: | Existing studies on LMs lack systematic studies on their structured reasoning capabilities over the infused knowledge. |
| Approach: | They investigate how LMs of different sizes can store world knowledge of different frequencies in a large-scale KB after training on the abundant world knowledge triplets. |
| Outcome: | The proposed models can store and respond to natural language queries with flexibility and reasoning abilities, but they need to be enhanced to fully realize their potential. |
Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)
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Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu
| Challenge: | Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments. |
| Approach: | They propose a method that integrates multimodal incongruities via contrastive learning for multimodal sarcasm detection by using three views to drive multi-view learning. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and shows that it is more generalizable than existing methods. |
Cognitive Biases, Task Complexity, and Result Intepretability in Large Language Models (2025.coling-main)
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| Challenge: | Recent work shows that cognitive biases occur frequently in language models . a cognitive bias is a systematic deviation in judgment that simplifies complex decisions . |
| Approach: | They evaluate the performance of different groups of models for each type of cognitive bias . they find that task complexity plays a part in eliciting stronger effects for some biases . |
| Outcome: | The proposed models perform better for each type of bias in different settings . the results show that task complexity plays a part in eliciting stronger effects . |
Robustness Evaluation of the German Extractive Question Answering Task (2025.coling-main)
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| Challenge: | Existing evaluation benchmarks for Question Answering systems only include EM and F1 scores, but they overlook critical factors for the deployment of QA systems. |
| Approach: | They propose to define an evaluation method specifically tailored to the German language to evaluate the robustness of German QA models. |
| Outcome: | The proposed method extends existing methods to German language . it shows that all models are vulnerable to character-level perturbations . |
Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)
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| Challenge: | Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context. |
| Approach: | They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image . |
| Outcome: | The proposed method can be integrated into existing models and demonstrate consistent performance improvements. |
Bridging Modality Gap for Effective Multimodal Sentiment Analysis in Fashion-related Social Media (2025.coling-main)
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| Challenge: | Existing sentiment analysis tasks focus on text comprehension, but visual content is important for emotional expression. |
| Approach: | They propose a multimodal framework that integrates information from various modalities for sentiment classification of fashion posts. |
| Outcome: | The proposed framework outperforms existing unimodal and multimodal baselines on a comprehensive dataset and significantly outperformed existing unilmodal and multiple modal frameworks. |
Quality Beyond A Glance: Revealing Large Quality Differences Between Web-Crawled Parallel Corpora (2025.coling-main)
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| Challenge: | Parallel corpora play a vital role in advanced multilingual natural language processing tasks, notably in machine translation (MT). |
| Approach: | They manually and automatically evaluated four well-known publicly available parallel corpora across eleven language pairs. |
| Outcome: | The results show that the four well-known parallel corpora have a substantial amount of noisy sentence pairs, while CCMatrix and CCAligned have low quality sentences. |
MLLM-I2W: Harnessing Multimodal Large Language Model for Zero-Shot Composed Image Retrieval (2025.coling-main)
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| Challenge: | Existing methods for combining image retrieval are supervised and zero-shot . however, the challenge of mapping pseudo-words to images within the joint image-text embedding space is still a challenge. |
| Approach: | They propose a novel image-text mapping network which converts description-related image information into pseudo-word markers for precise ZS-CIR. |
| Outcome: | The proposed model improves on COCO, CIRR, and Fashion-IQ benchmarks. |
Linguistic Features Extracted by GPT-4 Improve Alzheimer’s Disease Detection based on Spontaneous Speech (2025.coling-main)
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| Challenge: | Large language models (LLMs) have enabled powerful new possibilities for semantic text analysis. |
| Approach: | They leverage GPT-4 to extract five semantic features from transcripts of spontaneous patient speech. |
| Outcome: | The proposed model significantly improves detection of AD in manually transcribed and automatically generated transcripts. |
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners? (2025.coling-main)
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| Challenge: | Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. |
| Approach: | They investigate the advantage of grounded language acquisition over visual input to improve syntactic generalization. |
| Outcome: | The proposed model is less efficient than humans in language acquisition . it shows that visual input helps syntactic generalization, but not vision . |
Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in comprehending and analyzing lengthy sequential inputs. |
| Approach: | They propose to implement ad-hoc solutions that enhance LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to address this limitation, they propose to use three datasets and two tasks to analyze news categorization and sentence analysis to evaluate their models. |
| Outcome: | The proposed solutions significantly improve LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to 93% and 50%, respectively. |
MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions (2025.coling-main)
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| Challenge: | Existing studies focus on summarization and question-answering tasks, but neglect logical coherence within stories. |
| Approach: | They propose a model that leverages large language models to identify narrative gaps and generate coherent sentences that integrate seamlessly with the story’s emotional and logical flow. |
| Outcome: | The proposed model enhances narrative understanding and story generation, highlighting LLMs’ potential as effective logic checkers in story writing with logical coherence and emotional consistency. |
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization (2025.coling-main)
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| Challenge: | Existing methods to reduce the adverse effect of annotation errors are time-consuming because they require many trained models to detect errors. |
| Approach: | They propose a method that uses a tokenization technique called subword regularization to simulate multiple error detection models for detecting errors. |
| Outcome: | The proposed method performs weighting weighting four to five times faster than existing methods and improves in document classification and named entity recognition tasks. |
Rethinking Long Context Generation from the Continual Learning Perspective (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle with processing long contexts due to the limited context window. |
| Approach: | They propose to combine a limited context window with a continual learning perspective to improve LLMs' efficiency in processing long contexts. |
| Outcome: | The proposed models improve the performance of Large Language Models (LLMs) by integrating learning strategies with existing approaches. |
LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
| Approach: | They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions. |
| Outcome: | The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods. |
Are Your Keywords Like My Queries? A Corpus-Wide Evaluation of Keyword Extractors with Real Searches (2025.coling-main)
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| Challenge: | Keyword Extraction (KE) is essential in Natural Language Processing (NLP) for identifying key terms that represent the main themes of a text. |
| Approach: | They propose to use real query data from Google Trends to evaluate keywords extracted from a text to capture users' top queries. |
| Outcome: | The proposed method can be used with both supervised and unsupervised KE approaches and shows that KeyBERT is the most effective in capturing users’ top queries. |
NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers (2025.coling-main)
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| Challenge: | Large Language Models have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. |
| Approach: | They propose to penalize quick, intuitive "System 1" thinking by combining linguistic isolation with resistance to intuitive shortcuts to assess model's reasoning abilities. |
| Outcome: | The proposed model penalizes quick, intuitive “System 1” thinking, isolating fundamental reasoning skills. |
How Well Can Large Language Models Reflect? A Human Evaluation of LLM-generated Reflections for Motivational Interviewing Dialogues (2025.coling-main)
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Erkan Basar, Xin Sun, Iris Hendrickx, Jan de Wit, Tibor Bosse, Gert-Jan De Bruijn, Jos A. Bosch, Emiel Krahmer
| Challenge: | Motivational Interviewing (MI) is a counseling technique that promotes behavioral change through reflective responses to mirror or refine client statements. |
| Approach: | They assess the potential of Large Language Models (LLMs) to generate MI reflections via three LLMs: GPT-4, Llama-2, and BLOOM. |
| Outcome: | The proposed models generate meaningful reflections comparable to human therapists, but significant challenges remain. |
Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies (2025.coling-main)
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| Challenge: | Motivational interviewing (MI) is a client-centered counseling technique that encourages individuals to change behaviors through emphatic conversations. |
| Approach: | They propose to use large language models to generate more controllable dialogues with explainability by prompting LLMs to predict appropriate strategies as reasoning and utilizing these strategies to guide dialogue generation. |
| Outcome: | The proposed model generates more controllable and explainable dialogues with a set of MI skills. |
Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection (2025.coling-main)
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Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa, Parag Agrawal, Sandipan Dandapat
| Challenge: | Existing methods for chain-of-thought (CoT) prompting are limited by handcrafted demonstrations and trigger phrases are prone to inaccuracies. |
| Approach: | They propose a method that generates rationales using a trigger phrase to select effective demonstrations without accessing model parameters. |
| Outcome: | The proposed method outperforms existing methods across four reasoning benchmarks and is robust and scalable. |
Word-level Cross-lingual Structure in Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional performance across a broad spectrum of cross-lingual Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use Word-level Cross-lingual Structure to prove that the word-level embedding on the hidden layers isomorphic between languages. |
| Outcome: | The proposed method significantly improves on two representative LLM foundations, LLaMA2 and BLOOM. |
Trucidator: Document-level Event Factuality Identification via Hallucination Enhancement and Cross-Document Inference (2025.coling-main)
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| Challenge: | Document-level event factuality identification (DEFI) assesses the veracity degree to which an event mentioned in a document has happened. |
| Approach: | They propose a document-level event factuality identification framework with hallucination features . they propose factualusion corpus that integrates both genuine and hallucinous false information . |
| Outcome: | The proposed framework outperforms baselines in document event factuality identification. |
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation (2025.coling-main)
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Andrei-Marius Avram, Mircea Timpuriu, Andreea Iuga, Vlad-Cristian Matei, Iulian-Marius Taiatu, Tudor Găină, Dumitru-Clementin Cercel, Mihaela-Claudia Cercel, Florin Pop
| Challenge: | Using supervised automatic summarization requires sufficient corpora that include pairs of documents and their summaries. |
| Approach: | They propose a large-scale summarization dataset for the Romanian language that is crawled from publicly available news websites. |
| Outcome: | The proposed system performs well in abstractive summarization, which involves generating new sentences that capture the essence of the original text rather than extracting and rephrasing existing sentences. |
From Detection to Explanation: Effective Learning Strategies for LLMs in Online Abusive Language Research (2025.coling-main)
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Chiara Di Bonaventura, Lucia Siciliani, Pierpaolo Basile, Albert Merono Penuela, Barbara McGillivray
| Challenge: | Abusive language detection requires commonsense reasoning, world knowledge and linguistic nuances that evolve over time. |
| Approach: | They propose a knowledge-guided version of Llama-2 instruction fine-tuned for multi-class abusive language detection and explanation generation that mitigates bias and generates explanations that are relevant to the text and coherent with human reasoning. |
| Outcome: | The proposed model mitigates bias and generates explanations that are relevant to the text and coherent with human reasoning, with an average 48.76% better alignment with human judgment. |
TEEMIL : Towards Educational MCQ Difficulty Estimation in Indic Languages (2025.coling-main)
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| Challenge: | Traditionally, educators manually create and calibrate MCQs, a process that is time-consuming and subjective. |
| Approach: | They propose to use TEEMIL-H and TEIMEL-K to create a dataset with manually annotated difficulty labels for MCQs in Hindi and Kannada. |
| Outcome: | The proposed datasets contain 4689 and 4215 MCQs with manually annotated difficulty labels. |
What’s Wrong? Refining Meeting Summaries with LLM Feedback (2025.coling-main)
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| Challenge: | Existing methods for meeting summarization are limited and lack the robustness and context-based accuracy needed to maintain relevance. |
| Approach: | They propose a multi-LLM correction approach for meeting summarization using a two-phase process that mimics the human review process: mistake identification and summary refinement. |
| Outcome: | The proposed approach improves the quality of a given meeting summarization measured by relevance, informativeness, conciseness, and coherence. |
Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption Network (SGD-RSCCN) (2025.coling-main)
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| Challenge: | Remote sensing images are used for disaster assessment, urban planning and disaster response. |
| Approach: | They propose a Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption Network to improve the accuracy and naturalness of extracting and describing change information from remote sensing images. |
| Outcome: | The proposed method improves the naturalness and accuracy of extracting and describing change information from remote sensing images. |
Looking at the Unseen: Effective Sampling of Non-Related Propositions for Argument Mining (2025.coling-main)
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| Challenge: | Argument mining is the task of automatically identifying argumentative structures in natural language documents. |
| Approach: | They propose to use context and semantic similarity to sample non-related propositions . argument mining is the task of automatically identifying argumentative structures in natural language documents . |
| Outcome: | The proposed sampling strategies improve the performance of argument mining tasks. |
“Not Aligned” is Not “Malicious”: Being Careful about Hallucinations of Large Language Models’ Jailbreak (2025.coling-main)
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| Challenge: | “Jailbreak” is a major safety concern of Large Language Models (LLMs). |
| Approach: | They propose a benchmarking framework to evaluate "jailbreak" outputs . they propose specialized validation framework to ensure outputs are useful malicious instructions . |
| Outcome: | The proposed framework enhances existing benchmarks to ensure outputs are useful . it also aims to evaluate the true potential of jailbroken outputs to cause harm to human society. |
From Form to Meaning: The Case of Particles within the Prague Dependency Treebank Annotation Scheme (2025.coling-main)
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| Challenge: | Discussions on an appropriate annotation scheme for large and complex information are ongoing . multi-layer system allows a comprehensive description of relations between morphological properties, syntactic function and expressed meaning. |
| Approach: | They propose a multi-layer annotation scheme for the Prague Dependency Treebank . they propose morphological properties, syntactic function and expressed meaning as multi-layered systems . |
| Outcome: | The proposed scheme is sound and serves well for complex annotations. |
Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis (2025.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. |
| Approach: | They propose a method to enhance long-range dependencies between aspect and opinion words in ABSA by combining attention mechanisms with a syntax-based Graph Convolutional Network and a Mamba-Transformer module. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization (2025.coling-main)
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| Challenge: | Scientific Query-Focused Summarization (Sci-QFS) has lagged in development due to the lack of data. |
| Approach: | They propose a method to take advantage of existing academic papers to obtain large-scale datasets for this task automatically. |
| Outcome: | The proposed method outperforms existing models on the datasets and shows that it is relatively straightforward for humans. |
Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning (2025.coling-main)
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| Challenge: | Existing subgraph-based models focus on predicting missing relations in knowledge graphs . a new meta-learning model extracts concepts from entities to construct commonsense subgraphs based on semantic information . |
| Approach: | They propose a commonsense subgraph meta-learning model that extracts concepts from entities to construct commonsensible subgraphs. |
| Outcome: | The proposed model outperforms existing models in inductive reasoning tasks and in few-shot scenarios. |
Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback (2025.coling-main)
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| Challenge: | Recent studies on fine-grained intent detection have focused on collecting large-scale and high-quality samples via crowdsourcing resulting in data scarcity. |
| Approach: | They propose an iterative differential generation framework with contrastive feedback to generate high-quality pseudo samples and accurately capture the crucial nuances in target class distribution. |
| Outcome: | The proposed framework generates high-quality pseudo samples and captures crucial nuances in target class distribution. |
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)
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| Challenge: | Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsensense inferences during response generation. |
| Approach: | They propose to separate commonsense reasoning into explicit steps for generating, selecting, and integrating commonsensense into dialogue responses. |
| Outcome: | The proposed model infers commonsense knowledge from dialogue contexts to improve response quality and naturalness of dialogue interactions. |
Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)
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| Challenge: | Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations. |
| Approach: | They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on three CDR tasks. |
Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions (2025.coling-main)
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Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos
| Challenge: | Multiple choice questions (MCQs) are often used in employee selection and training, but their creation is resource-intensive and requires significant effort and investment. |
| Approach: | They propose to use large language models and prompt engineering techniques to automate the generation and validation of MCQs. |
| Outcome: | The proposed system reduces the burden on human resources and enables scalable, cost-effective MCQ generation. |
Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition (2025.coling-main)
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| Challenge: | Existing work mainly learns to map text into questions, lacking a mechanism to control results with desired complexity. |
| Approach: | They propose a novel controllable framework to generate QGs with desired complexity using contextual and commonsense clues from text. |
| Outcome: | The proposed framework can generate complex questions with desired complexity levels. |
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are scalable and economical evaluators, but how reliable they are is still under-explored. |
| Approach: | They propose a framework which breaks down the evaluation process into decomposition and aggregation stages based on pedagogical practices and provides an interpretable window for how well LLMs evaluate . |
| Outcome: | The proposed framework improves performance on a variety of meta-evaluation benchmarks by providing an interpretable window for how well LLMs evaluate . |
Towards Faithful Multi-step Reasoning through Fine-Grained Causal-aware Attribution Reasoning Distillation (2025.coling-main)
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| Challenge: | Recent advances have witnessed large language models (LLMs) achieving significant milestones across various domains of natural language processing. |
| Approach: | They introduce fine-grained attribution reasoning distillation (FARD) which incorporates grounded citations to consolidate the relationships between reasoning steps. |
| Outcome: | The proposed method outperforms CoT distillation methods on mathematical and general reasoning benchmarks. |
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations (2025.coling-main)
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| Challenge: | Large language models require substantial storage space to perform tasks such as text generation and video generation. |
| Approach: | They propose to compress large language models using integer replacements for floating-point numbers, in a process known as Quantization. |
| Outcome: | The proposed model allows for quantization of up to 75% decoder layers with 1 bit while maintaining performance levels comparable to those of the models with floating parameters. |
E-Bench: Towards Evaluating the Ease-of-Use of Large Language Models (2025.coling-main)
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| Challenge: | E-Bench is a framework for easy-to-use research on large language models. |
| Approach: | They propose to evaluate the ease-of-use of large language models and construct an E-Bench . they simulate human use from synonymous and typographical perturbations . |
| Outcome: | The proposed model is able to resist synonymous expressions and typos and improves performance. |
Enhancing Online Grooming Detection via Backtranslation Augmentation (2025.coling-main)
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| Challenge: | Existing models to detect predatory conversations for online conversation platforms are lacking in real-world applications due to sparse distribution of predatory conversation data. |
| Approach: | They propose backtranslation augmentation to augment training datasets with more predatory conversations by using 3 neural translators to augment them. |
| Outcome: | The proposed model improves with fewer training epochs for better classification efficacy on 8 languages from 4 language families and shows that it is more efficient than previous models. |
CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems (2025.coling-main)
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| Challenge: | Existing metrics for dialogue quality evaluation show low correlation with human judgements . current metrics do not accurately evaluate dialogue responses based on dialogue history . |
| Approach: | They propose a new metric measuring causal strength between dialogue histories and responses . they collect a dialogue dataset with human-annotated causal relations and pairwise human judgements . |
| Outcome: | The proposed metric outperforms existing state-of-the-art metrics in human judgements . it is based on a dialogue dataset with human-annotated causal relations and human judgement sets . |
Exploring the Impact of Language Switching on Personality Traits in LLMs (2025.coling-main)
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| Challenge: | Using three personality tests, we examine the extent to which LLMs align with humans when personality shifts are associated with language changes. |
| Approach: | They propose to use the Eysenck Personality Questionnaire-Revised to examine whether LLMs align with humans when personality shifts are associated with language changes. |
| Outcome: | The results show that language-switching affects personality traits in multilingual individuals, and that it is not translation-related. |
LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing solutions to improve the accuracy of RAG are based on retrieval-augmented generation . however, RAG still faces several challenges in tackling complex multi-hop queries . |
| Approach: | They propose a Missing Information Guided Retrieve-Extraction-Solving paradigm that leverages the identification of missing information to generate a targeted query. |
| Outcome: | The proposed method can extract information from retrieved knowledge and know what is still missing. |
Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models (2025.coling-main)
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| Challenge: | Existing approaches to enhancing large language models fail to emphasize specific constraints and unlock the underlying knowledge. |
| Approach: | They propose a method that emphasizes specific constraints and unlocks knowledge within LLMs by iteratively emphasising on specific constraints. |
| Outcome: | The proposed method outperforms existing methods in enhancing generated content, especially in terms of specificity. |
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)
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| Challenge: | Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. |
| Approach: | They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM). |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification. |
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering (2025.coling-main)
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| Challenge: | XAI aims to make machine learning models more transparent, but interpretable approaches are relatively rare. |
| Approach: | They integrate discrete subset sampling methods into a graph-based visual question answering system to evaluate their interpretability. |
| Outcome: | The proposed methods mitigate trade-off between interpretability and answer accuracy while achieving strong co-occurrences between answer and question tokens. |
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic (2025.coling-main)
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Teresa Lynn, Malik H. Altakrori, Samar M. Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Kirill Chirkunov, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash
| Challenge: | Recent years have brought about very fast developments in Natural Language Processing (NLP), but many other languages are overlooked due to limited resources. |
| Approach: | They propose to repurpose a multilingual BELEBELE dataset for a task of extractive QA in the style of machine reading comprehension. |
| Outcome: | The proposed approach could be used to extract QA in the style of machine reading comprehension. |
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment (2025.coling-main)
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| Challenge: | Existing knowledge distillation techniques for large language models are causing difficulties for student models to learn multi-modal probability distributions. |
| Approach: | They propose a ranking loss-based knowledge distillation method that encourages consistency of the ranking of peak predictions between teacher and student models. |
| Outcome: | The proposed method improves student models' ability to learn multi-modal distributions. |
DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning for Emotion Recognition in Conversations (2025.coling-main)
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| Challenge: | Existing ERC methods fail to handle emotional cues from both visual sources and discourse structures due to the complexity of visual scenes and contextual dependencies in conversations. |
| Approach: | They propose a framework for Emotion Recognition in conversations that utilizes multi-task instruction tuning to enhance the model's understanding of multi-modal dialogue scenes. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on three benchmark ERC datasets and is based on a video-language connector and a chain-of thought strategy. |
Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)
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Jianyang Zhai, Zi-Feng Mai, Dongyi Zheng, Chang-Dong Wang, Xiawu Zheng, Hui Li, Feidiao Yang, Yonghong Tian
| Challenge: | Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR. |
| Approach: | They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items. |
| Outcome: | The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively. |
Aligning Large Language Models with Human Opinions through Persona Selection and Value–Belief–Norm Reasoning (2025.coling-main)
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| Challenge: | Current methods for reasoning and predicting human opinions employ role-playing with personae but face two major issues: LLMs are sensitive to even a single irrelevant persona, skewing predictions by up to 30%; and LLM fail to reason strategically over personas. |
| Approach: | They propose a four-step solution modeling which and how to reason with personae, inspired by the Value–Belief–Norm theory. |
| Outcome: | The proposed model improves existing methods by up to 4% by fine-tuning them with COO's data. |
MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning (2025.coling-main)
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| Challenge: | Existing benchmarks for table reasoning are incomplete due to the complexity of the tables and user questions in real-world applications. |
| Approach: | They propose a Multi-scale spreadsheet benchmark with Meta operations for Table reasoning that incorporates two key features and a new criterion with six categories of meta operations for measuring the difficulty of each question. |
| Outcome: | The proposed model outperforms Claude-3.5-Sonnet with 77.4% accuracy on the existing benchmarks. |
Implicit Discourse Relation Classification For Nigerian Pidgin (2025.coling-main)
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| Challenge: | Existing discourse parsing tools are not available for Nigerian Pidgin (NP) this task requires supervised training and requires prompting. |
| Approach: | They propose to use implicit discourse relation classification (IDRC) for Nigerian Pidgin, which requires supervised training. |
| Outcome: | The proposed framework outperforms baseline and NP IDR classifiers in f1 scores. |
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM (2025.coling-main)
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| Challenge: | Many large language models (LLMs) support many languages, while others only support a few, e.g. the Llama series. |
| Approach: | They present a case study on BLOOM to understand three pertinent factors affecting performance: the number of languages, language exposure, and similarity between training and test languages. |
| Outcome: | The proposed model can be used to perform multilingual tasks on 1 to 52 languages. |
Gradient Inversion Attack in Federated Learning: Exposing Text Data through Discrete Optimization (2025.coling-main)
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| Challenge: | federated learning could overcome the bottleneck of public text data in large language models . a novel attack method is proposed to fully expose text data from gradients . |
| Approach: | They propose a method to fully expose text data from gradients by using a network of clients and a server. |
| Outcome: | The proposed method shows it is possible to Fully Expose Text data from gradients. |
Simulating Dual-Process Thinking in Dialogue Topic Shift Detection (2025.coling-main)
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| Challenge: | Existing methods for topic shift detection focus on shallow local reasoning, overlooking the importance of considering the global historical structure and local details to elucidate the underlying causes of topic shift. |
| Approach: | They propose a dual-process theory for dialogue topic shift detection that employs Large Language Models to extract and store the global topic structure of historical dialogue, while a reasoning module introduces a LLM to generate reasoning samples between the response and the most recent topic of historical dialog. |
| Outcome: | The proposed framework outperforms the state-of-the-art on three public datasets and is based on a dual-process theory. |
A Compliance Checking Framework Based on Retrieval Augmented Generation (2025.coling-main)
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| Challenge: | Existing text-based compliance checking methods are limited by their flexibility and lack structure. |
| Approach: | They propose a text-based compliance checking framework based on Retrieval-Augmented Generation that integrates a static layer for storing factual knowledge, a dynamic layer for retrieval and reasoning, and an eventic graph to structurally describe regulatory information. |
| Outcome: | The proposed framework consistently achieves state-of-the-art results across various scenarios surpassing baselines. |
MIDLM: Multi-Intent Detection with Bidirectional Large Language Models (2025.coling-main)
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| Challenge: | Existing models that use autoregressive architectures restrict the sharing of token information within a sentence. |
| Approach: | They propose a framework that integrates intent number detection and multi-intent selection to enable autoregressive LLMs to leverage bidirectional information awareness through post-training. |
| Outcome: | The proposed framework outperforms existing models and pretrained baselines in the multi-intent detection task. |
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models (2025.coling-main)
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Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
| Challenge: | Activation sparsity is a promising paradigm for accelerating model inference . few large language models achieve high activation spar and comparable performance . |
| Approach: | They propose a method to achieve activation sparsity and acceleration in large language models . they introduce ReLU activation and adopt progressive sparse regularization . |
| Outcome: | The proposed method achieves high activation sparsity and comparable model performance. |
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning (2025.coling-main)
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| Challenge: | Existing methods for zero-shot event-relational reasoning require large computational resources and lack interpretability. |
| Approach: | They propose a method for Reasoning-Oriented Locating and Editing which locates and edits key modules of the language model for reasoning about event relations. |
| Outcome: | The proposed method improves interpretability and efficiency with reduced computational cost and achieves SOTA results in zero-shot event-relational reasoning. |
Leveraging Language Models for Summarizing Mental State Examinations: A Comprehensive Evaluation and Dataset Release (2025.coling-main)
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| Challenge: | Mental health disorders affect a significant portion of the global population . access to mental health support is limited in developing countries . |
| Approach: | They evaluated a 12-item descriptive MSE questionnaire and five well-known summarization models . they found that language models can generate coherent MSE summaries for doctors . |
| Outcome: | The proposed model can generate coherent summaries from MSEs in a conversational format. |
Oddballness: universal anomaly detection with language models (2025.coling-main)
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| Challenge: | a new method to detect anomalies in texts uses a metric called oddballness . the method considers probabilities generated by a language model but not low-likelihood tokens . |
| Approach: | They propose a method to detect anomalies in texts using unsupervised language models . they define oddballness as a function that measures how strange a given token is . |
| Outcome: | The proposed method is better than state-of-the-art models for grammatical error detection tasks. |
CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models (2025.coling-main)
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Zhongzhi Li, Ming-Liang Zhang, Pei-Jie Wang, Jian Xu, Rui-Song Zhang, Yin Fei, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Jiaxin Zhang, Cheng-Lin Liu
| Challenge: | Large language models excel in various language tasks, while large multimodal models effectively handle visual-language problems. |
| Approach: | They propose to use a multimodal multimodal model evaluation benchmark to evaluate model performance in Chinese K12 classrooms. |
| Outcome: | The proposed model evaluation tool is integrated with the CMMaTH dataset. |
Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)
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Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang
| Challenge: | Recent large language models have made progress at interpreting and executing instructions. |
| Approach: | They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain . |
| Outcome: | The proposed method outperforms baseline methods on QA and mathematical reasoning domains. |
Enhancing Arabic NLP Tasks through Character-Level Models and Data Augmentation (2025.coling-main)
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| Challenge: | Using character-level models, natural language processing for Arabic is challenging due to its rich morphology, root-based word formation, flexible sentence structures, diacritical ambiguities, and orthographic variations. |
| Approach: | They propose a character-level approach specifically designed for Arabic NLP tasks that incorporates Convolutional Neural Networks (CNNs), pre-trained transformers (CANINE), and Bidirectional Long Short-Term Memory networks (BiLSTMs). |
| Outcome: | The proposed model outperforms existing models on Arabic privacy policy classification task and reports a micro-averaged F1 score of 93.8%, surpassing state-of-the-art models. |
The Gaps between Fine Tuning and In-context Learning in Bias Evaluation and Debiasing (2025.coling-main)
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| Challenge: | FT-based debiasing methods cause a performance degradation in downstream tasks . FT works by updating some or all parameters, while ICL uses prompts without modifying the model parameters. |
| Approach: | They propose to use ICL to customize PLMs to downstream tasks without parameter updates. |
| Outcome: | The proposed method lowers the performance degradation of FT-based debiasing methods compared to FT models . the proposed method improves performance on large datasets while allowing for smaller changes to PLMs . |
LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback (2025.coling-main)
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| Challenge: | Existing studies show that instruction-tuned LLMs under-predict positive classes . however, they are overly sensitive and can be applied for abuse detection without fine-tuning . |
| Approach: | They show that instruction-tuned LLMs tend to under-predict positive classes . they also show that label frequency in the prompt helps with the significant over-prediction . |
| Outcome: | The proposed models under-predict positive classes in social media, whereas they are overly sensitive. |
Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)
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| Challenge: | In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners. |
| Approach: | They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings. |
| Outcome: | The proposed framework can provide detailed feedback on 12-16% of all errors, revealing subtle differences in error patterns between L1 and L2 writings. |
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach (2025.coling-main)
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| Challenge: | Using language models (LMs) has increased in use, and the use of biases and stereotypes is creating social problems. |
| Approach: | They propose a method to mitigate LM biases by continual training on biased data . they use masked language modeling to construct a Bias Vector as the difference between biased LMs and pre-trained LM weights . |
| Outcome: | The proposed method improves on the GLUE and SEAT benchmarks. |
Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks (2025.coling-main)
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| Challenge: | Large language models (LLMs) are constrained to chaining immediate reasoning steps and relying solely on parametric knowledge. |
| Approach: | They propose a framework that activates retrieval only when necessary to improve answer accuracy. |
| Outcome: | Experiments show that the proposed framework improves performance in knowledge-intensive tasks. |
t-HNE: A Text-guided Hierarchical Noise Eliminator for Multimodal Sentiment Analysis (2025.coling-main)
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| Challenge: | Existing methods for multimodal sentiment analysis assume that all modalities contribute equally to model performance. |
| Approach: | They propose a text-guided Hierarchical Noise Eliminator model that extracts modality-consistent information from unimodal data and integrates it into multimodal representations for sentiment classification. |
| Outcome: | The proposed model reduces noise caused by modality inconsistency by maximizing mutual information between textual representations and visual and acoustic representations. |
ALYMPICS: LLM Agents Meet Game Theory (2025.coling-main)
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| Challenge: | Alympics provides a framework for simulating human-like strategic interactions with Large Language Model (LLM) agents. |
| Approach: | They propose a framework utilizing Large Language Models (LLM) agents for empirical game theory research. |
| Outcome: | The proposed framework can be used to study human-like strategic interactions with large language model (LLM) agents in a game on the multi-round auction of scarce survival resources. |
Towards Adaptive Mechanism Activation in Language Agent (2025.coling-main)
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| Challenge: | Existing Language Agents rely on a fixed mechanism or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. |
| Approach: | They propose to use language agents to learn to activate different mechanisms without relying on expert models to optimize their adaptation to different task solutions. |
| Outcome: | The proposed approach improves agent performance by enabling it to activate the appropriate mechanisms according to the potential characteristics of the task. |
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)
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| Challenge: | Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination. |
| Approach: | They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references. |
| Outcome: | Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting. |
Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)
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| Challenge: | Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question. |
| Approach: | They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context . |
| Outcome: | The proposed approach is evaluated in English and Chinese scenarios. |
Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models (2025.coling-main)
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| Challenge: | Existing methods to augment vocabularies ignore the disparities between model representation and frequency distributions. |
| Approach: | They propose an Entropy-Consistency Word Selection method which integrates semantic and frequency metrics for vocabulary augmentation. |
| Outcome: | The proposed method improves performance for low-resource languages compared to high-resourced ones . it integrates semantic and frequency metrics for vocabulary augmentation . |
Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs (2025.coling-main)
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| Challenge: | Temporal knowledge graph reasoning is a useful tool for many practical tasks. |
| Approach: | They propose a Hawkes process-based Evolutional Representation Learning Network model which learns structural information and evolutional patterns of a TKG simultaneously. |
| Outcome: | The proposed model learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying. |
Intention Analysis Makes LLMs A Good Jailbreak Defender (2025.coling-main)
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| Challenge: | Existing methods to align large language models with human values overlook the intrinsic nature of jailbreaks, which limits their effectiveness in complex scenarios. |
| Approach: | They propose a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). They show that IA suppresses LLM’s tendency to follow jailbreak prompts, thereby enhancing safety. |
| Outcome: | The proposed strategy reduces harmfulness of LLMs and outperforms GPT-3.5 in attack success rate. |
Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons (2025.coling-main)
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| Challenge: | Despite their superior multitask capabilities, the multitask learning mechanisms of large language models remain as an open question. |
| Approach: | They propose a method that fine-tunes current task-specific neurons during continuous learning by using gradient attribution on task-specified data. |
| Outcome: | The proposed method is highly correlated with the given task and solves two common problems in multi-task learning and continuous learning: Generalization and Catastrophic Forgetting. |
Do Large Language Models Mirror Cognitive Language Processing? (2025.coling-main)
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| Challenge: | Large language models have demonstrated remarkable abilities in text comprehension and logical reasoning. |
| Approach: | They employ Representational Similarity Analysis to measure alignment between 23 LLMs and fMRI signals of the brain. |
| Outcome: | The results show that training strategies affect the LLM-brain alignment. |
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration (2025.coling-main)
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Xin Guan, Nate Demchak, Saloni Gupta, Ze Wang, Ediz Ertekin Jr., Adriano Koshiyama, Emre Kazim, Zekun Wu
| Challenge: | Existing benchmarks for large language models fail to detect bias due to limited scope, contamination, and lack of a fairness baseline. |
| Approach: | They propose a benchmarking pipeline to detect biases in large language models . they use metrics for max disparity, impact ratio, and bias concentration to analyze disparity . |
| Outcome: | SAGED(bias) is the first holistic benchmarking pipeline to address biases in large language models. |
Learning to Reason via Self-Iterative Process Feedback for Small Language Models (2025.coling-main)
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| Challenge: | Existing methods for enhancing SLMs’ reasoning depend on costly external signals, resulting in SLM overly confident with limited supervision signals. |
| Approach: | They propose to fine-tune and align SLMs using positive and negative feedback signals and introduce process supervision for rewards in preference alignment by sampling-based inference simulation and process reward models. |
| Outcome: | The proposed method improves Gemma-2B's performance on GSM8K and MBPP, and out-of-domain generalization capabilities on MMLU_Math and HumanEval. |
Rethinking-based Code Summarization with Chain of Comments (2025.coling-main)
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| Challenge: | Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the heterogeneity gap between code and summary. |
| Approach: | They propose a framework that uses chain of comments as auxiliary intermediate information to bridge the gap between code and summaries. |
| Outcome: | The proposed framework outperforms baseline models and multiple code Large Language Models by a large margin. |
RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy. |
| Approach: | They propose a retrieval-generate-retrieve framework that uses a Retrieve-Generate framework to retrieve factual knowledge from a knowledge graph. |
| Outcome: | Experimental results show that RGR-KBQA improves on CWQ and WebQSP datasets. |
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)
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| Challenge: | Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation. |
| Approach: | They propose an AL strategy that combines uncertainty and diversity for sentence selection. |
| Outcome: | The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations. |
LLM Sensitivity Evaluation Framework for Clinical Diagnosis (2025.coling-main)
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| Challenge: | Existing studies on the sensitivity of Large Language Models (LLMs) to irrelevant contexts neglect the importance of key information. |
| Approach: | They investigate the sensitivity of large language models to key medical information by introducing different perturbation strategies to investigate their sensitivity. |
| Outcome: | The proposed models are based on three LLMs, namely GPT-3.5, GPT-4, Gemini, Claude3 and LLaMA2-7b, and demonstrate their reliability and sensitivity to medical information. |
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Multimodal large language models combine visual and textual data for tasks like image captioning and visual question answering. |
| Approach: | They propose temperature scaling and iterative prompt optimization to calibrate MLLMs and enhance model reliability. |
| Outcome: | The proposed techniques improve MLLMs and improve model reliability. |
Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning (2025.coling-main)
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| Challenge: | Entity alignment (EA) aims to match identical entities across knowledge graphs (KGs) Graph neural network-based entity alignment methods have achieved promising results in Euclidean space, but KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. |
| Approach: | They propose a method which unifies dual-space embedding to preserve the intrinsic structure of KGs. |
| Outcome: | The proposed method achieves state-of-the-art in structure-based EA on benchmark datasets. |
Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain (2025.coling-main)
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| Challenge: | Syntactic structures are crucial for capturing aspect-opinion relationships . syntactically based models struggle with linguistic complexities . |
| Approach: | They propose a syntactic-opinion-sentiment reasoning framework that leverages syntaktic information to improve ABSA performance. |
| Outcome: | The proposed framework improves ABSA performance, though smaller LLMs exhibit weaker performance. |
Reasoning with Trees: Faithful Question Answering over Knowledge Graph (2025.coling-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks. |
| Approach: | They propose a framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on two benchmark KGQA datasets and improves on the MCTS process. |
Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025.coling-main)
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Tianlong Li, Zhenghua Wang, Wenhao Liu, Muling Wu, Shihan Dou, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) however, limited research into the underlying mechanisms that make LLMs vulnerable to such attacks has been conducted. |
| Approach: | They propose that LLMs' self-safeguarding capability is linked to specific activity patterns within their representation space. |
| Outcome: | The proposed models can be detected with a few pairs of contrastive queries, and the robustness can be manipulated by weakening or strengthening these patterns. |
Lexicography Saves Lives (LSL): Automatically Translating Suicide-Related Language (2025.coling-main)
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| Challenge: | Recent years have seen a marked increase in research that aims to identify or predict risk, intention or ideation of suicide in the context of Western culture. |
| Approach: | They propose to translate an existing dictionary related to suicide into 200 different languages and conduct human evaluations on a subset of translated dictionaries. |
| Outcome: | The proposed project aims to identify or predict risk, intention or ideation of suicide in the context of Western culture and reduce suicide rate by 2030 is one of the UN’s Sustainable Development Goals. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
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| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have high computational costs and privacy concerns due to their high computational expenses and data privacy. |
| Approach: | They propose a method that empowers SLMs to internalize symbolic knowledge and few-shot examples gradually through a progressive fine-tuning process. |
| Outcome: | The proposed approach outperforms state-of-the-art baselines by over 5% while reducing inference costs by up to 4 across a wide range of SLMs in both in-domain (ID) and out-of domain (OOD) tasks. |
TermDiffuSum: A Term-guided Diffusion Model for Extractive Summarization of Legal Documents (2025.coling-main)
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| Challenge: | Recent studies have explored diffusion models for extractive summarization task, showcasing their remarkable capabilities. |
| Approach: | They propose a term-guided diffusion model for extractive summarization of legal documents that incorporates legal terminology into the model via a well-designed multifactor fusion noise weighting schedule. |
| Outcome: | The proposed model outperforms existing models on a self-constructed legal summarization dataset and achieves improvements of 3.10, 2.84, and 2.89 on three public datasets. |
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. |
| Approach: | They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text. |
| Outcome: | The proposed method improves performance on three benchmarks. |
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity (2025.coling-main)
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| Challenge: | Existing RAG frameworks either indiscriminately perform retrieval or rely on rigid single-label classifiers to select retrieval methods. |
| Approach: | They propose a framework that dynamically selects the most suitable retrieval strategy based on query complexity. |
| Outcome: | The proposed framework achieves state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. |
Improvement in Sign Language Translation Using Text CTC Alignment (2025.coling-main)
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| Challenge: | Current sign language translation (SLT) approaches rely on gloss-based supervision with Connectionist Temporal Classification (CTC) limiting their ability to handle non-monotonic alignments between sign language video and spoken text. |
| Approach: | They propose a method that integrates CTC/Attention with the attention mechanism during decoding and integrates it with the sign language video and spoken text. |
| Outcome: | The proposed method outperforms the pure-attention baseline and achieves comparable results to state-of-the-art methods. |
Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining (2025.coling-main)
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| Challenge: | Existing backdoor defense methods are ineffective for generative large language models . generative LLMs output sequences of high-dimensional token logits instead of low-dimensional classification logits . |
| Approach: | They propose a method that leverages sample-wise gradients to identify backdoor samples without retraining LLMs. |
| Outcome: | The proposed method outperforms baselines significantly in identifying backdoor samples without retraining LLMs. |
MQM-Chat: Multidimensional Quality Metrics for Chat Translation (2025.coling-main)
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| Challenge: | Existing methods for chat translation face challenges due to high levels of ambiguity and stylized contents. |
| Approach: | They propose a multidimensional quality metric for chat translation that includes seven error types . they use human annotations to analyze chat data generated by five translation models . |
| Outcome: | The proposed evaluation metric can qualify errors while highlighting chat-specific issues explicitly. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models have demonstrated remarkable performance across tasks. |
| Approach: | They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models. |
| Outcome: | The proposed framework extends existing benchmarks to extend models across tasks and tasks. |
Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have pushed the boundaries of natural language processing, but their consistency is often limited when applied to unfamiliar domains. |
| Approach: | They propose a method that controls which predictive indicators are used and which are excluded during classification. |
| Outcome: | The proposed method reduces the OOD gap by up to 20 percentage points in a few-shot setup. |
Finetuning LLMs for Comparative Assessment Tasks (2025.coling-main)
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| Challenge: | Automated assessment in natural language generation is a challenging task. |
| Approach: | They propose a framework for fine-tuning LLMs for comparative assessment to align the model’s output with the target distribution of comparative probabilities. |
| Outcome: | The proposed framework improves state-of-the-art performance while maintaining high performance with an efficient subset of comparisons. |
Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea (2025.coling-main)
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| Challenge: | Existing solutions to hallucination in large language models (LLMs) focus on aligning models with credible sources or improving how models communicate their confidence in outputs. |
| Approach: | They examine how best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests. |
| Outcome: | The best-performing models generate information in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |
AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information (2025.coling-main)
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| Challenge: | Existing glyph-based models neglect the relationship between pictorial elements and radicals for Named Entity Recognition (NER) tasks. |
| Approach: | They propose a model that integrates multi-source visual and phonetic information of Hanzi . they propose combining pictographic features with radicals to facilitate integration . |
| Outcome: | The proposed model improves performance on benchmark datasets. |
Edit-Wise Preference Optimization for Grammatical Error Correction (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been successful in grammatical error correction (GEC) but their strengths have yet to be fully demonstrated in GEC . |
| Approach: | They propose a method to optimize grammatical errors by assigning higher reward weights to edit tokens during preference optimization. |
| Outcome: | The proposed method outperforms baselines on English and Chinese datasets and achieves state-of-the-art performance. |
You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (2025.coling-main)
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| Challenge: | Existing rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Existing research mainly combats this with content and response-based detection methods. |
| Approach: | They propose a Large Language Model with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses. |
| Outcome: | The proposed approach can indicate the model’s uncertainty (i.e., reliability) of the results. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
| Outcome: | The proposed methods perform better on small data settings and on large datasets, but they are not as effective on large data sets. |
Context-Informed Machine Translation of Manga using Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Automated manga translation is a promising potential solution, but it is underdeveloped due to the need to incorporate visual elements into the translation process to resolve ambiguities. |
| Approach: | They propose a method that leverages the vision component of multimodal large language models to improve translation quality and evaluate the impact of translation unit size, context length, and propose 'token efficient' approach for manga translation. |
| Outcome: | The proposed method achieves state-of-the-art results for Japanese-English translation and sets a new standard for Japanese and Polish translation. |
Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales (2025.coling-main)
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| Challenge: | Extreme Zero-shot XMC uses lightweight bi-encoders to identify pseudo labels . state-of-the-art methods rely on suboptimal labels for training . |
| Approach: | They propose a framework that uses a lightweight bi-encoder to identify high-quality pseudo labels during training while employing a lightweight bi-coder for efficient inference. |
| Outcome: | The proposed framework achieves superior performance and efficiency over existing methods. |
NovAScore: A New Automated Metric for Evaluating Document Level Novelty (2025.coling-main)
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Lin Ai, Ziwei Gong, Harshsaiprasad Deshpande, Alexander Johnson, Emmy Phung, Ahmad Emami, Julia Hirschberg
| Challenge: | Recent research has focused on identifying text that introduces new, previously unknown information, but has seen a decline in novelty detection due to the rise of large language models. |
| Approach: | They propose a novel automated metric for evaluating document-level novelty that aggregates the novelty and salience scores of atomic information and provides high interpretability and a detailed analysis of a document's novelty. |
| Outcome: | The proposed metric scores high on the TAP-DLND 1.0 dataset and a human-annotated dataset. |
HLU: Human Vs LLM Generated Text Detection Dataset for Urdu at Multiple Granularities (2025.coling-main)
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| Challenge: | Using large language models (LLMs) to generate human-like text has raised concerns about misuse, especially in low-resource languages like Urdu. |
| Approach: | They propose a dataset that contains documents, paragraphs, and sentences . they conducted human evaluations and automated evaluations . |
| Outcome: | The proposed dataset shows that distinguishing between human and machine-generated text is challenging for both humans and LLMs. |
Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models (2025.coling-main)
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| Challenge: | Using a literary corpus that alternates between topics and styles, we compare language models across French and English. |
| Approach: | They analyze how writing style affects embedding spaces across multiple language models . they use a literary corpus that alternates between topics and styles to compare their results . |
| Outcome: | The proposed model is based on two established literary works in French and English. |
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding (2025.coling-main)
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Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov, Dietrich Klakow, Philip Slusallek, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Emotion classification is one of the most challenging tasks in large language models. |
| Approach: | They propose to use a multi-label emotion classification dataset for four Ethiopian languages to evaluate their ability to learn and reason. |
| Outcome: | The proposed model improves the understanding of emotions in language models and how people convey emotions through various languages. |
Knowledge Graph Unlearning with Schema (2025.coling-main)
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| Challenge: | Unlearning on knowledge graphs has not been extensively studied. |
| Approach: | They propose a new unlearning method based on schema for knowledge graph (KG) they update the representation of the deleted element’s neighborhood with an unlearning object that regulates the affinity between the affected neighborhood and the instances within the same schema. |
| Outcome: | The proposed method is evaluated on various KG embedding models with benchmark datasets. |
Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)
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| Challenge: | Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics. |
| Approach: | They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies . |
| Outcome: | The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans. |
Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)
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| Challenge: | Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios. |
| Approach: | They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization. |
| Outcome: | The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings. |
Improving Multilingual Sign Language Translation with Automatically Clustered Language Family Information (2025.coling-main)
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| Challenge: | Recent research has focused on bilingual translation models, but multilingual sign language translation presents unique challenges due to the diversity of sign languages across nations. |
| Approach: | They propose a method that leverages sign language families to improve MSLT performance. |
| Outcome: | The proposed approach can achieve balance between translation accuracy and computational cost by regulating the number of language families. |
Is Peer-Reviewing Worth the Effort? (2025.coling-main)
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| Challenge: | Using early returns and venue, we can predict which papers will be highly cited in the future. |
| Approach: | They ask whether early returns are predictive of papers' citations . |
| Outcome: | The authors show early returns are more predictive than venue . early returns also predicts which papers will be highly cited in the future . |
OptiPrune: Effective Pruning Approach for Every Target Sparsity (2025.coling-main)
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| Challenge: | Existing methods for model pruning only perform optimally within specific sparsity ranges. |
| Approach: | They propose a pruning method that reduces model size by eliminating redundant parameters . they compare it with OptiPrune, which adapts non-uniform sparsity with adaptive deviation . |
| Outcome: | The proposed method reduces model size and maintains performance despite large size and high computational demands. |
ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary (2025.coling-main)
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| Challenge: | Literature review is an indispensable step in the research process, but literature summary is challenging and time consuming. |
| Approach: | They propose an LLM agent with human workflow guidance for comparative literature summary . they use a human workflow to extract key elements from relevant literature and generate summaries . |
| Outcome: | The proposed method outperforms the CoT model in several dimensions. |
Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)
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| Challenge: | Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment. |
| Approach: | They propose an expression paraphrase strategy and a sentiment-consistent contrastive learning mechanism to learn the connections between implicit and explicit sentiment expressions and integrate them into the model. |
| Outcome: | The proposed method is effective on implicit sentiment analysis on public datasets. |
Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension (2025.coling-main)
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| Challenge: | Several studies have focused on predicting the surprisal of a word and its reading time, but only recently, attention has been given to other components, such as P600. |
| Approach: | They propose to model reading times and ERP amplitudes using surprisal and entropy . they also propose a metric based on semantic similarity for N400 and P600 . |
| Outcome: | The proposed metric predicts reading times and ERP amplitudes in Mandarin Chinese. |
Multilingual Supervision Improves Semantic Disambiguation of Adpositions (2025.coling-main)
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| Challenge: | a corpus-based cross-linguistic investigation into the lexical semantics of adpositions is conducted . a significant amount of ambiguity and flexibility in their meanings are present in a variety of languages . |
| Approach: | They conduct a corpus-based corpus analysis of adpositions using SNACS . they find distributional differences in a language's adequacy and disambiguation performance . |
| Outcome: | The proposed framework is suited for analyzing adpositions across languages . it provides a framework for a wide-coverage corpus annotation of high-level senses . |
Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
Investigating the Impact of Incremental Processing and Voice Activity Projection on Spoken Dialogue Systems (2025.coling-main)
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| Challenge: | a large language model (LLM) is gaining attention for its ability to model human-like turn-taking in human conversations. |
| Approach: | They developed a turn-taking model that can be trained in unsupervised manner using spoken dialogue data between two speakers. |
| Outcome: | The proposed model can be trained in unsupervised manner using spoken dialogue data between two speakers. |
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025.coling-main)
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| Challenge: | Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge, but it remains unclear how well they perceive their factual knowledge boundaries. |
| Approach: | They propose to use a retrieval augmentation approach to enhance LLMs' awareness of factual knowledge boundaries to analyze factual and factual information in open-domain question answering (QA) |
| Outcome: | The proposed method improves LLMs’ QA and judgemental capabilities by integrating supporting documents with the questions. |
Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels (2025.coling-main)
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| Challenge: | Pre-trained language models have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. |
| Approach: | They propose a new paradigm termed zero-to-strong generalization that prompts LLMs to annotate unlabeled data and retain high-quality labels by filtering. |
| Outcome: | The proposed framework outperforms pre-trained language models on extensive classification and reasoning tasks on multiple model sizes. |
Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models (2025.coling-main)
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Anmol Reddy Mekala, Vineeth Dorna, Shreya Dubey, Abhishek Lalwani, David Koleczek, Mukund Rungta, Sadid A. Hasan, Elita A.A Lobo
| Challenge: | Existing methods for large language models rely on negative feedback to suppress responses related to the forget set, which often results in nonsensical or inconsistent outputs, diminishing model utility and posing potential privacy risks. |
| Approach: | They propose an approach which combines negative feedback with in-domain positive feedback on the forget set and introduces new evaluation metrics to assess the quality of responses related to the forget sets. |
| Outcome: | The proposed approach avoids undesirable model behaviors while maintaining overall model performance. |
Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context language models have lagged behind . however, there is still room for improvement as the context window and complexity of the tasks increase. |
| Approach: | They propose a long-context benchmark to evaluate the performance of long-text language models. |
| Outcome: | The proposed benchmarks show that the models perform better in long-context environments as the context window increases and complexity increases. |
Personalized Large Language Model Assistant with Evolving Conditional Memory (2025.coling-main)
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| Challenge: | With the rapid development of large language models, personalized large language model assistants like ChatGPT are limited in personalized services. |
| Approach: | They propose a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory. |
| Outcome: | The proposed framework can preserve the knowledge and experience from the history dialogue with the user, which can be applied to future tailored responses that better align with the users' preferences. |
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training (2025.coling-main)
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| Challenge: | Recent approaches for visually-rich document understanding use manually annotated semantic groups. |
| Approach: | They propose a new variant of the VrDU task that does not use manually annotated semantic groups. |
| Outcome: | The proposed method improves on the existing methods while sacrificing performance. |
Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)
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| Challenge: | Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL. |
| Approach: | They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever . |
| Outcome: | The proposed method improves embedding-based retriever and reduces cost. |
Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025.coling-main)
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| Challenge: | Existing language models tend to rely heavily on sequential cues, but not always favoring the closest strings. |
| Approach: | They construct a dataset of 320 synthetic sentences and 360 natural sentences from the BCC corpus . they evaluate 21 language models against this dataset and compare their performance to native Mandarin speakers . |
| Outcome: | The proposed models do not replicate human-like judgments in Mandarin Chinese . the results show that existing models tend to rely heavily on sequential cues . |
HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025.coling-main)
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| Challenge: | Existing models for multimodal hate detection lack implicit hateful cues, cross-modal-induced hate, and diversity of hate target groups. |
| Approach: | They propose a hypergraph-based prompting fusion model that uses LLMs to generate hate cue prompts and hypergraph learning to merge multimodal hate features. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it can detect hate content across multiple modalities. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
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| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
Automated Progressive Red Teaming (2025.coling-main)
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| Challenge: | Automated red teaming (ART) is effective but time-consuming, costly and lacks scalability. |
| Approach: | They propose an automated red teaming framework that generates adversarial prompts to expose LLM vulnerabilities. |
| Outcome: | The proposed framework explores and exploits LLM vulnerabilities through multi-round interactions. |
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)
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| Challenge: | Existing methods for detecting rumors on social media neglect the temporal aspect of rumor propagation. |
| Approach: | They propose a method that incorporates temporal information by building a weighted propagation tree and a coding tree. |
| Outcome: | The proposed approach preserves essential structure of rumor propagation while reducing noise. |
Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)
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| Challenge: | Current research focuses on explicit abusive language, but subtler forms of IAL remain insufficiently studied. |
| Approach: | They evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. |
| Outcome: | The proposed models outperform the best previously reported methods in classifying sentences directly as IAL or benign and extracting linguistic features associated with IAL. |
Grading Massive Open Online Courses Using Large Language Models (2025.coling-main)
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| Challenge: | Massive open online courses (MOOCs) offer free education globally, but the massive enrollment in these courses makes it impractical for an instructor to assess every student’s writing assignment. |
| Approach: | They propose to use large language models to replace peer grading in MOOCs by using zero-shot chain-of-thought prompts to automate feedback process. |
| Outcome: | The proposed method automates the feedback process once the LLM assigns a score to an assignment. |
Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks (2025.coling-main)
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| Challenge: | Existing studies on social media echo chambers have been limited to numbers and formulas. |
| Approach: | They propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena. |
| Outcome: | The proposed model can simulate opinion dynamics and echo chambers using language-based simulations. |
Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace (2025.coling-main)
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| Challenge: | Existing methods for parameter-efficient fine-tuning have been proposed to reduce time and resource costs. |
| Approach: | They propose a parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-uning by 8 times. |
| Outcome: | The proposed method reduces the number of parameters required for fine-tuning by 8 times and achieves comparable or superior performance compared to existing models. |
StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models (2025.coling-main)
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| Challenge: | Existing models struggle to maintain temporal, spatial, and narrative coherence across image sequences . existing models lack depth and engagement of human-authored stories . |
| Approach: | They propose a topic-driven narrative optimizer that integrates image descriptions, topic generation, and GPT-4-based refinements. |
| Outcome: | The proposed framework outperforms existing models in visual relevance, coherence, and fluency. |
Aligning Complex Knowledge Graph Question Answering as Knowledge-Aware Constrained Code Generation (2025.coling-main)
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| Challenge: | Existing frameworks that generate LF using Large Language Models (LLMs) in a few-shot setting are limited due to little exposure to the LF during pre-training. |
| Approach: | They propose a framework that aligns the LF generation as code generation that incorporates LF-specific constraints. |
| Outcome: | The proposed framework surpasses all few-shot baselines on KQA Pro by 21%. |
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting (2025.coling-main)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Reddy Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, Amit Sheth
| Challenge: | Proportional analogies are used to assess linguistic and cognitive abilities. |
| Approach: | They propose a dataset for proportional analogy completion and evaluate its performance in large-scale learning environments. |
| Outcome: | The proposed model achieves 55% accuracy in knowledge-enhanced prompts. |
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements. |
| Approach: | They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements . |
| Outcome: | The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment. |
Claim veracity assessment for explainable fake news detection (2025.coling-main)
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| Challenge: | Recent approaches to fake news detection focus on textual features without external facts, which may lead to a misrepresentation of the truth. |
| Approach: | They propose a new fake news detection method that predicts the truth or false-hood of a claim based on relevant factual evidence or LLM’s inference mechanisms. |
| Outcome: | The proposed method produces the final synthesized prediction, along with well-founded facts or reasoning. |
ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models (2025.coling-main)
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| Challenge: | Existing metrics for multimodal large language models only focus on token overlap and may not align with human judgment. |
| Approach: | They propose an open-source model that assesses the question answering abilities of multimodal large language models. |
| Outcome: | Experiments show that the ACE-M3 model performs better than existing models and is more reliable than existing metrics. |
A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)
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| Challenge: | Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion. |
| Approach: | They propose a Dual Contrastive Learning Framework that enhances existing MERC models without additional data. |
| Outcome: | The proposed framework outperforms existing models on two MERC benchmark datasets and shows that it reduces label dependence and enhances emotion-sensitive independent modality features. |
Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization (2025.coling-main)
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| Challenge: | While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewrite, their application to claim optimization remains unexplored. |
| Approach: | They propose to use a sliding window mechanism to evaluate the performance of large language models in claim clarification tasks under different settings. |
| Outcome: | The proposed model improves the performance of three LLMs on the claim clarification task under zero-shot, few-shot and supervised fine-tuning settings. |
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
| Outcome: | The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios. |
C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering (2025.coling-main)
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| Challenge: | Existing sentence ordering datasets for non-English languages are unavailable. |
| Approach: | They propose a parameter-free sentence ordering dataset that provides genuinely unordered sentences without artificial segmentation cues. |
| Outcome: | The proposed method outperforms existing methods on the sentence ordering task. |
KIA: Knowledge-Guided Implicit Vision-Language Alignment for Chest X-Ray Report Generation (2025.coling-main)
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| Challenge: | Existing reports on medical images and reports lack fine-grained cross-modal interaction, leading to insufficient understanding of detailed information. |
| Approach: | They propose a framework for establishing cross-modal semantic alignment in radiology report pairs using knowledge-guided implicit vision-language alignment. |
| Outcome: | KIA improves understanding of medical images and reports by incorporating medical knowledge to enhance pathological observation and anatomical landm. |
On the Human-level Performance of Visual Question Answering (2025.coling-main)
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| Challenge: | Visual7W has been widely used in assessing multiple-choice visual question-answering systems. |
| Approach: | They replicated a human experiment on Visual7W to examine the human-level performance of VQA. |
| Outcome: | The results show that the better a model performs on Visual7W, the better it aligns with human-level intelligence. |
Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models (2025.coling-main)
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| Challenge: | Rapid advancements in large language models have highlighted the need for robust evaluation frameworks that assess their core capabilities. |
| Approach: | They propose two benchmarks to assess core capabilities of large language models . current benchmarks for Thai focus mainly on traditional NLP tasks . |
| Outcome: | The proposed benchmarks are based on evaluations of various LLMs with multi-lingual capabilities and are publicly available to encourage further research and development for Thai LLM. |
CONTRANS: Weak-to-Strong Alignment Engineering via Concept Transplantation (2025.coling-main)
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| Challenge: | Large language models behave consistently with human goals, values and intentions, but are computationally expensive. |
| Approach: | They propose a framework that enables weak-to-strong alignment transfer via concept transplantation. |
| Outcome: | The proposed framework surpasses instruction-tuned models in terms of truthfulness. |
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs (2025.coling-main)
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| Challenge: | Existing 3D AIGC methods don’t fully unleash human creativity. |
| Approach: | They propose a framework that generates 3D content from multimodal inputs . they propose 198 multimodal text inputs for 3D generation tasks . |
| Outcome: | The proposed framework generates 3D content from multimodal inputs without human intervention. |
Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research (2025.coling-main)
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| Challenge: | Using neurolinguistics and aphasiology, we examine the theoretical underpinnings of some influential linguistically motivated training approaches targeting the syntactic domain. |
| Approach: | They examine the theoretical underpinnings of linguistically motivated training approaches derived from neurolinguistics and aphasiology to develop human-like learning strategies for language models. |
| Outcome: | The proposed frameworks can be used to improve the recovery and generalization of linguistic skills in aphasia treatment and to develop human-like learning strategies. |
Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection (2025.coling-main)
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| Challenge: | Existing MFND methods conduct cross-modal information interaction at later stage, resulting in weak generalization ability. |
| Approach: | They propose an automatic multi-modal fake news detection method that exploits cross-modal information interaction at later stage. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three MFND benchmarks. |
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
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Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | a recent study has found that Arabic is underrepresented in Large Language Models, especially in dialectal variations. |
| Approach: | They propose a benchmark for Arabic Dialect and Cultural Evaluation that evaluates Arabic dialect comprehension and generation. |
| Outcome: | The proposed model outperforms multilingual models on dialect comprehension and generation, but significant challenges persist in dialect identification, generation, and translation. |
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation (2025.coling-main)
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| Challenge: | Existing knowledge graph embedding models measure the plausibility of triplets either through semantic matching or distance scoring functions. |
| Approach: | They propose to combine semantic matching with entity’s geometric distance to better measure the plausibility of triplets. |
| Outcome: | The proposed model outperforms existing models on well-known knowledge graph completion benchmark datasets. |
How Credible Is an Answer From Retrieval-Augmented LLMs? Investigation and Evaluation With Multi-Hop QA (2025.coling-main)
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| Challenge: | Retrieval-augmented large language models (RaLLMs) are reshaping knowledge acquisition, offering long-form, knowledge-grounded answers through advanced reasoning and generation capabilities. |
| Approach: | They propose a benchmarking system to evaluate RaLLMs' correctness and Groundedness to determine their reliability in multi-hop question-answering tasks. |
| Outcome: | The proposed model-based evaluation pipeline for multi-hop question-answering tasks reveals that the model generates inaccuracies when dealing with flawed or partial knowledge. |
Is Parameter Collision Hindering Continual Learning in LLMs? (2025.coling-main)
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| Challenge: | Existing methods to learn multiple tasks in parallel often lead to catastrophic forgetting, resulting in overwriting knowledge. |
| Approach: | They propose a non-collision low-rank Adaptation approach that leverages low collision rates to enhance continual learning (CL) in large language models. |
| Outcome: | The proposed approach achieves better task orthogonality and higher task orthognality than existing SOTA methods. |
Jump To Hyperspace: Comparing Euclidean and Hyperbolic Loss Functions for Hierarchical Multi-Label Text Classification (2025.coling-main)
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| Challenge: | Hierarchical Multi-Label Text Classification (HMTC) is a challenging machine learning task . a recent study evaluated the effectiveness of Euclidean and hyperbolic loss functions on HMTC . |
| Approach: | They evaluate label-aware and contrastive losses in the Euclidean and hyperbolic space . they find contrastive loss functions are less effective when deployed in the hyperbolical space compared to non-hyperbolic ones . |
| Outcome: | The proposed model improves on four commonly used HMTC datasets. |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
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| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)
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| Challenge: | Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries . |
| Approach: | They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors. |
| Outcome: | The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities. |
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain (2025.coling-main)
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| Challenge: | Existing research focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. |
| Approach: | They evaluate the efficacy of hybrid search across a variety of retrieval models in the french language . they find that fusion of different domain-general models consistently enhances performance . |
| Outcome: | The proposed model improves in-domain performance compared to a single model in a zero-shot context . the proposed model also improves when the models are trained in- domain . |
MPID: A Modality-Preserving and Interaction-Driven Fusion Network for Multimodal Sentiment Analysis (2025.coling-main)
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| Challenge: | Current methods for multimodal sensing analysis overlook nuanced differences and similarities across modalities, leading to potential biases. |
| Approach: | They propose a Modal-Preserving and Interaction-Driven Fusion Network to address these challenges by integrating text with audio and a separate Adaptive Graded Fusion Module for text and visual data. |
| Outcome: | The proposed model achieves state-of-the-art on CMU-MOSI, CMU -MOSEI, and CH-SIMS datasets. |
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models (2025.coling-main)
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| Challenge: | Existing methods for creating a vision question-answering with natural language explanations rely on human annotations that are time-consuming and costly. |
| Approach: | They propose a method that generates high-quality natural language explanations using LVLMs by using visual prompts. |
| Outcome: | The proposed method generates high-quality synthetic VQA-NLE datasets 20x faster than human annotations with minimal decrease in qualitative metrics. |
DefVerify: Do Hate Speech Models Reflect Their Dataset’s Definition? (2025.coling-main)
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| Challenge: | DefVerify is a 3-step procedure that encodes a user-specified definition of hate speech, quantifies to what extent the model reflects the intended definition, and identifies the point of failure in the workflow. |
| Approach: | They propose a 3-step procedure that encodes a user-specified definition of hate speech and quantifies to what extent the model reflects intended definition. |
| Outcome: | The proposed procedure detects gaps between definition and model behavior when applied to six popular hate speech benchmark datasets. |
Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
| Outcome: | The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods. |
ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval (2025.coling-main)
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| Challenge: | Existing approaches to improve retrieval effectiveness in high-resource languages are limited due to the lack of high-quality labeled data in non-English languages. |
| Approach: | They propose a modular dense retrieval model that learns from the rich data of a single high-resource language and effectively zero-shot transfers to a wide array of languages. |
| Outcome: | The proposed model performs well against state-of-the-art multilingual retrieval models trained on more extensive datasets in various languages. |
TEXT-CAKE: Challenging Language Models on Local Text Coherence (2025.coling-main)
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| Challenge: | a new evaluation benchmark, TEXT-CAKE, is used to evaluate language models for text coherence detection. |
| Approach: | They propose a benchmark to evaluate language models' ability to detect text coherence . they analyze multilingual and monolingual LMs with varying architectures and parameters . |
| Outcome: | The proposed model outperforms other models on the TEXT-CAKE evaluation benchmark. |
KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration (2025.coling-main)
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| Challenge: | Existing knowledge tracing models do not incorporate forgetting features to improve the learning and answering processes. |
| Approach: | They propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration using four real-world datasets to test the model. |
| Outcome: | The proposed model outperforms the existing knowledge tracing models and eliminates the need for artificial engineering features. |
Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)
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| Challenge: | Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering. |
| Approach: | They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization. |
| Outcome: | The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics. |
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning (2025.coling-main)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities in multi-modal context comprehension, but they still suffer from hallucination problems due to inconsistent outputs with the image content. |
| Approach: | They propose a training-free framework MVP to reduce hallucinations in Large Vision-Language Models . they propose multi-view information-seeking strategy to perceive the comprehensive information in the image . |
| Outcome: | The proposed framework reduces hallucinations in large vision-language models by combining multi-view multi-path reasoning with multi-vision multi-path reasoning. |
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
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| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
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| Challenge: | Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. |
| Approach: | They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods. |
| Outcome: | The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages. |
Factual Dialogue Summarization via Learning from Large Language Models (2025.coling-main)
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| Challenge: | Existing models generate fluent and coherent summaries, but inconsistencies can be found in generated summary. |
| Approach: | They propose to use symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization. |
| Outcome: | The proposed model outperforms baseline models in BART, PEGASUS, and Flan-T5 in factual consistency and accuracy. |
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs (2025.coling-main)
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| Challenge: | QUENCH is a text-based English quizzing benchmarking system for large language models (LLMs). |
| Approach: | They propose a text-based English Quizzing Benchmark manually curated from YouTube quiz videos. |
| Outcome: | The proposed system assesses the world knowledge and deduction capabilities of large language models via a zero-shot, open-domain quizzing setup. |
GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering (2025.coling-main)
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| Challenge: | Existing automated RAG evaluation frameworks overlook important failure modes when using GPT-4 as a judge. |
| Approach: | They propose a novel pipeline to assess the calibration and discrimination capabilities of judge models by using a meta-evaluation benchmark of 144 unit tests to identify key failure modes. |
| Outcome: | The proposed pipeline improves on existing frameworks, while state-of-the-art open-source judges do not generalize to their proposed criteria. |
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models (2025.coling-main)
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| Challenge: | Despite advances in training Large Language Models, they remain vulnerable to jailbreak, an adversarial attack method. |
| Approach: | They propose an adversarial jailbreak algorithm that exploits the gradient information of the suffix tokens to accelerate the optimization process. |
| Outcome: | The proposed model achieves 1.5x speedup while maintaining high attack success rates. |
Conditional Semantic Textual Similarity via Conditional Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods to assess similarity between sentences encounter over-estimation problem . compared to fuzzy representations, similarity is comparatively lower in terms of "The person's age". |
| Approach: | They propose a conditional contrastive learning framework that constructs positive and negative samples from two perspectives. |
| Outcome: | The proposed method achieves state-of-the-art performance with five models based on bi-encoder and tri-encoding architectures. |
A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions (2025.coling-main)
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| Challenge: | Code-switching (CSW) is a common linguistic phenomenon in multilingual societies . current literature on CSW in the arab world is limited to the Arabic language . |
| Approach: | They present a review of the literature in the field of code-switched Arabic NLP . they propose recommendations for future research . |
| Outcome: | This review provides a broad perspective on the current literature in the field of code-switched Arabic NLP . it also provides recommendations for future research . |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
Modal Feature Optimization Network with Prompt for Multimodal Sentiment Analysis (2025.coling-main)
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| Challenge: | Multimodal sentiment analysis(MSA) is used to understand human emotional states through multimodal. |
| Approach: | They propose a Modal Feature Optimization Network with a modal prompt attention mechanism to optimize the under-optimized modal representation by determining which modalities are under- optimized . |
| Outcome: | The proposed method outperforms existing state-of-the-art models on public benchmark datasets. |
Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies (2025.coling-main)
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| Challenge: | Existing fact-checking systems that use text and image information are susceptible to fake news spread by social media platforms. |
| Approach: | They propose a neural probing classifier based on multimodality and embeddings from text and image encoders to represent multimodal content for fact-checking. |
| Outcome: | The proposed classifier outperforms KNN and SVM baselines in leveraging extracted embeddings, highlighting its effectiveness for multimodal fact-checking. |
Faithful Inference Chains Extraction for Fact Verification over Multi-view Heterogeneous Graph with Causal Intervention (2025.coling-main)
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| Challenge: | Existing methods for fact verification do not extract faithful inference chains due to the diversity of relation paths. |
| Approach: | They propose a multi-view heterogeneous Graph with causal intervention to extract evidence graphs from the knowledge graph. |
| Outcome: | The proposed model provides precise evidence graphs and achieves state-of-the-art performance on the public KG-based fact verification dataset FactKG. |
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. |
| Approach: | They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent. |
| Outcome: | The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training. |
ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-main)
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Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii
| Challenge: | Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese. |
| Approach: | They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model. |
| Outcome: | The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain. |
Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation (2025.coling-main)
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| Challenge: | Writing arguments requires integrating high-level beliefs from various perspectives . current language models generate outputs autoregressively, resulting in limited diversity and coherence . |
| Approach: | They propose a persona-based multi-agent framework for argument writing that integrates beliefs from different perspectives into a coherent narrative. |
| Outcome: | The proposed framework generates more diverse arguments by both automatic and human evaluations. |
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)
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| Challenge: | Existing methods for text classification based on large language models are difficult to apply directly to solve. |
| Approach: | They propose a data quality enhancement method to improve LLMs' performance in classification tasks by using a greedy algorithm to select data and then performing fine-tuning. |
| Outcome: | The proposed method improves the performance of large language models in text classification tasks and significantly improves training efficiency, saving nearly half of the training time. |
Slender-Mamba: Fully Quantized Mamba in 1.58 Bits From Head to Toe (2025.coling-main)
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| Challenge: | Large language models (LLMs) have achieved significant performance improvements in natural language processing domain, but require large computational resources for training and inference. |
| Approach: | They propose to use a language model architecture based on State-Space Models to quantify embedding and projection layers of a model with 150 B tokens from scratch. |
| Outcome: | The proposed language model architecture reduces costs by compressing context windows during inference while reducing the cost of training and inference. |
What’s the most important value? INVP: INvestigating the Value Priorities of LLMs through Decision-making in Social Scenarios (2025.coling-main)
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| Challenge: | Large scale language models (LLMs) have demonstrated impressive performance in various tasks and are increasingly integrated into the decision-making process. |
| Approach: | They propose a framework for INvestigating Value Priorities through decision-making in social scenarios and evaluate seven popular LLMs. |
| Outcome: | The proposed framework covers 1613 scenarios and 3226 decisions across 283 topics and focuses on Universalism and Benevolence, while Power and Hedonism are given lower priority. |
BasqBBQ: A QA Benchmark for Assessing Social Biases in LLMs for Basque, a Low-Resource Language (2025.coling-main)
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| Challenge: | Existing pre-trained language models can propagate social biases in under-resourced languages like Basque. |
| Approach: | They propose a benchmark to assess biases in Basque using a multiple-choice question-answering task. |
| Outcome: | The proposed dataset is the first to assess biases in Basque across eight domains . larger models achieve better accuracy, but ambiguous cases remain challenging . |
DynRank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification (2025.coling-main)
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Abdelrahman Elsayed Mahmoud Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M.Abdelgwad, Adam Jatowt
| Challenge: | Existing approaches to enhancing passage retrieval rely on static prompts and pre-defined templates. |
| Approach: | They propose a dynamic question classification framework for open-domain question-answering systems that generates contextually relevant prompts. |
| Outcome: | The proposed framework improves passage retrieval in open-domain questionanswering systems by generating contextually relevant prompts. |
Why should only High-Resource-Languages have all the fun? Pivot Based Evaluation in Low Resource Setting (2025.coling-main)
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| Challenge: | a limited number of evaluation metrics and resources are available for low-resource languages . a pivot-based evaluation framework is proposed to address these limitations . |
| Approach: | They propose a pivot-based evaluation framework that leverages advanced metrics for more meaningful evaluation. |
| Outcome: | The proposed framework extends the coverage of both lexical-based and embedding-based metrics even for languages not directly supported by advanced metrics. |
The Shift from Logic to Dialectic in Argumentation Theory: Implications for Computational Argument Quality Assessment (2025.coling-main)
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| Challenge: | In the field of computational argument quality assessment, logic and dialectic are essential dimensions used to measure the quality of argumentative texts. |
| Approach: | They propose to separate logic and dialectic as quality dimensions in computational argument quality assessment . they propose to use dialectical considerations to improve the quality of argumentative texts . |
| Outcome: | The proposed method can benefit argument theory and argument analysis by separating the two quality dimensions. |
Task-Oriented Dialog Systems for the Senegalese Wolof Language (2025.coling-main)
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| Challenge: | Low-resource languages such as African ones are underrepresented in large language models limiting their performance in these languages. |
| Approach: | They propose a chatbot generation engine based on the Rasa framework and a method for projecting annotations onto the Wolof language using an in-house machine translation system. |
| Outcome: | The proposed approach performs similarly to the one obtained for French, which is a resource-rich language. |
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment (2025.coling-main)
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| Challenge: | Human values are inherently diverse, making it insufficient to align LLMs solely with general preferences. |
| Approach: | They propose a flexible paradigm for individual preference alignment that disentangles preference representation from text generation in LLMs. |
| Outcome: | The proposed method produces aligned quality and better than PEFT-based methods while reducing training time for each new individual preference by 80% to 90%. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |
Interactive Evaluation for Medical LLMs via Task-oriented Dialogue System (2025.coling-main)
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| Challenge: | In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding of patients’ conditions. |
| Approach: | They propose to use multi-turn medical dialogue evaluation to evaluate proactive communication and diagnostic capabilities of medical Large Language Models (LLMs) . |
| Outcome: | The proposed model outperforms existing models on multi-turn question-answering datasets and is therefore cost-effective. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
ACL-rlg: A Dataset for Reading List Generation (2025.coling-main)
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| Challenge: | Existing tools for searching the literature return an overwhelming number of results, making familiarization process daunting and inefficient. |
| Approach: | They propose to use ACL-rlg as the largest open expert-annotated reading list dataset to help researchers navigate key literature. |
| Outcome: | The proposed dataset outperforms existing search engines and indexing methods and shows signs of data contamination. |
SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have remarkable emergent abilities across various tasks, yet their performance on complex reasoning and planning tasks remains suboptimal. |
| Approach: | They propose a tree-search-based reasoning framework that encourages the exploration of intermediate steps and a round-scheduled strategy to manage draft model dispatching. |
| Outcome: | The proposed framework improves runtime speed and GPU memory management concurrently and handles multiple iterations for thought generation and state evaluation. |
Extracting structure from an LLM - how to improve on surprisal-based models of Human Language Processing (2025.coling-main)
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| Challenge: | Existing computational models capture prediction and reanalysis using Large Language Models (LLMs) and a statistical measure known as ‘surprisal’. |
| Approach: | They propose to extract structural information from Large Language Models and a statistical measure known as ‘surprisal’ to integrate it with their learnt statistics. |
| Outcome: | The proposed model achieved higher correlation with human reading times and better predicted the garden path effect and could distinguish between sentence types with different levels of difficulty. |
Evaluating Generalization Capability of Language Models across Abductive, Deductive and Inductive Logical Reasoning (2025.coling-main)
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| Challenge: | Recent research in language models (LMs) have demonstrated remarkable performance on many natural language tasks, yet to what extent LMs possess the capability of generalizing to unseen logical rules remains unclear. |
| Approach: | They propose to use a dataset to assess the generalization capabilities of LMs on ADI reasoning to assess their generalization abilities. |
| Outcome: | The proposed dataset shows that LMs perform poorly on ADI reasoning tasks and lacks generalization capabilities. |
Measuring the Robustness of Reference-Free Dialogue Evaluation Systems (2025.coling-main)
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| Challenge: | Advancements in dialogue systems powered by large language models have outpaced the development of reliable evaluation metrics. |
| Approach: | They propose a benchmark to evaluate the robustness of reference-free dialogue metrics against four categories of adversarial attacks. |
| Outcome: | The proposed benchmarks show that the two axes of reliability are not always aligned . the findings motivate the development of nuanced evaluation frameworks to address real-world dialogue challenges. |
Towards Robust Comparisons of NLP Models: A Case Study (2025.coling-main)
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| Challenge: | Existing statistical tests to compare the test scores of different NLP models have been proposed to account for nuisance factors such as noise, randomness, or hyperparameter values. |
| Approach: | They propose a regression analysis which isolates the effect of nuisance factors from the effects of the models’ capabilities. |
| Outcome: | The proposed model is able to show that the difference between BioLinkBERT and MSR BiomedBERT is 7 times smaller than previously reported. |
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
Enhancing Criminal Investigation Analysis with Summarization and Memory-based Retrieval-Augmented Generation: A Comprehensive Evaluation of Real Case Data (2025.coling-main)
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| Challenge: | KriRAG is a new architecture designed to assist criminal investigators in analyzing information and overcoming the challenge of information overload. |
| Approach: | They propose a Retrieval-Augmented Generation architecture that structures and summarizes extensive document collections based on existing investigative queries. |
| Outcome: | The proposed architecture is based on two homicide case files comprising approximately 3,700 documents and 13,000 pages. |
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction (2025.coling-main)
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| Challenge: | Unsupervised keyphrase extraction methods require large amounts of labeled data and are often domainspecific, limiting their practical applicability. |
| Approach: | They propose an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. |
| Outcome: | The proposed method outperforms baseline models on four datasets and is highly efficient on three of four dataset. |
Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods (2025.coling-main)
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| Challenge: | Automated speech recognition (ASR) systems are able to transcribe spontaneous human conversations with high accuracy. |
| Approach: | They evaluate the accuracy of open source automatic speech recognition systems across conversational speech datasets and explore the potential of ASR ensembling and post-ASR correction methods to improve transcription accuracy. |
| Outcome: | The proposed methods highlight the need for robust error correction techniques and address demographic biases to enhance ASR performance and inclusivity. |
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning (2025.coling-main)
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| Challenge: | Recent studies have focused on improving the ability of Large Language Models to perform complex reasoning. |
| Approach: | They propose a Direct-Indirect Reasoning method that integrates DR and IR as parallel reasoning paths that are merged to derive the final answer. |
| Outcome: | The proposed method outperforms existing methods on four datasets related to logical reasoning and proof. |
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges (2025.coling-main)
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| Challenge: | Existing methods for detecting data contamination in large language models have limitations and limitations . data contamination occurs when test or evaluation data is exposed to the model during its training phases . |
| Approach: | They evaluate five different methods for detecting data contamination in large language models . they find that current methods have non-trivial limitations in their assumptions and practical applications . |
| Outcome: | The proposed methods have non-trivial limitations and difficulties in detecting contamination . the authors highlight the complexity of contamination detection in advanced LLMs . |
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)
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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits. |
| Approach: | They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task. |
| Outcome: | The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |
A Benchmark of French ASR Systems Based on Error Severity (2025.coling-main)
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| Challenge: | Automatic Speech Recognition (ASR) transcription errors are often assessed using metrics that compare them with a reference transcription. |
| Approach: | They propose to categorize transcription errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
| Outcome: | The proposed evaluation categorizes errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
What Makes Cryptic Crosswords Challenging for LLMs? (2025.coling-main)
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| Challenge: | Recent research suggests that solving cryptic crosswords is challenging even for modern NLP models, including Large Language Models (LLMs). |
| Approach: | They establish benchmark results for three popular LLMs: Gemma2, LLaMA3 and ChatGPT, and investigate why these models struggle to achieve superior performance. |
| Outcome: | The proposed models perform significantly below humans on the cryptic crossword puzzle task, while human solvers achieve 99% accuracy. |
Improving the Efficiency of Visually Augmented Language Models (2025.coling-main)
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| Challenge: | Autoregressive Language Models lack visual knowledge due to reporting bias in textual corpora. |
| Approach: | They propose to use visual representations obtained from CLIP multimodal system to augment autoregressive language models with visual knowledge. |
| Outcome: | The proposed model outperforms VALM for visual language understanding, natural language understanding and language modeling tasks despite being significantly more efficient and simpler. |
Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Existing approaches to synthetic APE data generation use source (src) sentences in a parallel corpus to obtain translations (mt) through an MT system and treat corresponding reference (ref) sentences as post-edits (pe). |
| Approach: | They propose a reference-focused synthetic APE data generation technique that uses ‘ref’ instead of src’ sentences to obtain corrupted translations. |
| Outcome: | The proposed technique improves on English-German, English-Russian, English -Marathi, English and Hindi language pairs. |
EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data. |
| Approach: | They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. |
| Outcome: | The proposed framework shows consistent performance improvements on four benchmarks. |
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation (2025.coling-main)
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| Challenge: | Existing datasets suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. |
| Approach: | They propose a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data. |
| Outcome: | The proposed model performs better in tackling challenging and complex image translation tasks in the real world. |
Synthetic Paths to Integral Truth: Mitigating Hallucinations Caused by Confirmation Bias with Synthetic Data (2025.coling-main)
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| Challenge: | Existing methods to mitigate confirmation bias-induced hallucination in large language models (LLMs) however, they still exhibit issues such as confirmation bias, which remains unexplored in current research. |
| Approach: | They propose a method to mitigate confirmation bias-induced hallucination in large language models by using a synthetic data construction pipeline and direct preference optimization (DPO) training. |
| Outcome: | The proposed method improves response accuracy and reduced hallucination on natural questions open and halubench. |
Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) use subword vocabularies to process and generate text. |
| Approach: | They find that Large Language Models (LLMs) perform poorly at handling some types of affixations because subwords are marked as initial- or intra-word . |
| Outcome: | The largest models trained on enough data can mitigate this tendency because initial- and intra-word embeddings are aligned; in-context learning also helps when all examples are selected in a consistent way; but only morphological segmentation can achieve a near-perfect accuracy. |
WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)
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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing efforts to generate Wikipedia articles for new events fall short of real-world application. |
| Approach: | They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios. |
| Outcome: | The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability. |
LLMs meet Bloom’s Taxonomy: A Cognitive View on Large Language Model Evaluations (2025.coling-main)
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| Challenge: | Existing evaluation approaches for Large Language Models lack a structured approach that reflects the underlying cognitive abilities required for solving the tasks. |
| Approach: | They propose a hierarchical approach to evaluation of Large Language Models that leverages Bloom’s Taxonomy to identify how well they cover the levels of Bloom’ s taxonomies. |
| Outcome: | The proposed evaluation frameworks cover the Bloom’s Taxonomy, a hierarchical framework for categorizing cognitive skills, on the most widely used benchmarks. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable Styles (2025.coling-main)
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| Challenge: | Existing models for text-to-speech (TTS) synthesize speech with acoustic features . autoregressive models have problems with word skipping and repeated reading . non-autoregressive acustic models lack probabilistic modeling and unimodal characteristics of Gaussian distribution don't conform to true distribution of aural features, which restricts the diversity of generated prosodic features. |
| Approach: | They propose a multi-speaker acoustic model that hierarchically models speech prosodic features and controls different prosodic styles to guide prosody prediction. |
| Outcome: | The proposed method outperforms baseline models in naturalness and achieves superior synthesis speed compared to baseline models. |
OpenForecast: A Large-Scale Open-Ended Event Forecasting Dataset (2025.coling-main)
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| Challenge: | Existing closed-ended event forecasting methods are constrained by a limited answer space. |
| Approach: | They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events. |
| Outcome: | The proposed method can be used to evaluate the ability of complex event forecasting of large language models. |
A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing (2025.coling-main)
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| Challenge: | Existing studies have failed to account for the differences in concept relevance when a question involves multiple concepts . |
| Approach: | They propose a Knowledge Graph Reasoning-Based Model for CAT that captures semantic and relational information between concepts and questions and incorporates multiple evaluation objectives. |
| Outcome: | The proposed model outperforms existing methods on three authentic educational datasets. |
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM (2025.coling-main)
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| Challenge: | Empathetic conversation is a crucial characteristic in daily conversations between individuals. |
| Approach: | They propose an Emotional Knowledge Tool Calling framework which encapsulates commonsense knowledge bases as empathetic tools, enabling LLMs to integrate external knowledge flexibly. |
| Outcome: | The proposed framework can generate empathetic responses effectively on the TOOL-ED dataset. |
Annotating the French Wiktionary with supersenses for large scale lexical analysis: a use case to assess form-meaning relationships within the nominal lexicon (2025.coling-main)
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| Challenge: | Conducting large-scale empirical studies in lexical semantics remains an elusive goal for many languages lacking comprehensive semantic resources. |
| Approach: | They propose to use the Princeton WordNet to enrich the French Wiktionary with general semantic classes, known as supersenses, using a limited amount of manually annotated data. |
| Outcome: | The proposed method can be extended to other languages provided an electronic lexicon and manually annotated senses are available. |
When Evolution Strategy Meets Language Models Tuning (2025.coling-main)
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| Challenge: | Autoregressive language models with pretraining often display limited capability in effectively following instructions. |
| Approach: | They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest. |
| Outcome: | The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task. |
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)
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| Challenge: | Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content. |
| Approach: | They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size. |
| Outcome: | The proposed task systematically evaluates popular unlearning algorithms and reveals that the knowledge coverage of the forget set and its size play pivotal roles. |
Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)
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| Challenge: | Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space. |
| Approach: | They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction. |
| Outcome: | The proposed framework outperforms baselines on link prediction task. |
Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation (2025.coling-main)
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| Challenge: | LLMs are used to emulate sequential decision-making processes of humans . however, their ability to perform probabilistic sampling is limited . |
| Approach: | They propose to use large language models (LLMs) as agents to emulate the sequential decision-making processes of humans represented as Markov decision-makers (MDPs). |
| Outcome: | The proposed models can understand probabilities, but struggle with sampling precision . integrating coding tools can improve sampling precision, but this level of sampling precision still makes it difficult to simulate human behavior as agents. |
Pseudo-label Data Construction Method and Syntax-enhanced Model for Chinese Semantic Error Recognition (2025.coling-main)
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| Challenge: | Existing research on Chinese text error recognition has focused on pre-trained models, but training them from scratch is time-consuming and laborious. |
| Approach: | They propose a method for Chinese Semantic Error Recognition that generates pseudo-labels for augmented samples based on perplexity and model respectively. |
| Outcome: | The proposed method surpasses existing models in Chinese text error recognition due to Chinese semantics' complexity. |
An Active Learning Framework for Inclusive Generation by Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) exhibit bias toward underrepresented groups, despite advances in active learning. |
| Approach: | They propose a clustering-based active learning framework enhanced with knowledge distillation that transforms the intermediate outputs of the learner model to yield more representative models without prior knowledge of underlying data distribution. |
| Outcome: | The proposed framework improves performance across data subgroups and lexical diversity, underscoring the model’s resilience to skewness in available data. |
Multimodal Extraction and Recognition of Arabic Implicit Discourse Relations (2025.coling-main)
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| Challenge: | Identifying implicit discourse relations in written text is challenging, but it is also crucial to understand them in spoken discourse. |
| Approach: | They propose a method for implicit discourse relation identification that uses audio and text data to extract semantically equivalent pairs of implicit and explicit discourse markers. |
| Outcome: | The proposed method outperforms audio-based models but can be augmented by combining text and audio features. |
Post-Hoc Watermarking for Robust Detection in Text Generated by Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for document simplification address complex factors such as technical terminology, metaphors, and overall coherence. |
| Approach: | They propose a multi-agent framework AgentSimp for document simplification based on large language models that simulates collaboration among agents through roles played by multiple agents. |
| Outcome: | The proposed framework produces simplified documents that are more thoroughly simplified and more coherent across various articles and styles. |
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents (2025.coling-main)
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| Challenge: | Inspirational quotes from famous individuals are powerful tools that convey wisdom and insight in a concise and often figurative manner. |
| Approach: | They propose a context-based quote extraction system that aims to predict the most relevant quote from a long text. |
| Outcome: | The proposed system improves on a dataset with 5.08% BoW F1-score. |
VeritasQA: A Truthfulness Benchmark Aimed at Multilingual Transferability (2025.coling-main)
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Javier Aula-Blasco, Júlia Falcão, Susana Sotelo, Silvia Paniagua, Aitor Gonzalez-Agirre, Marta Villegas
| Challenge: | Large Language Models (LLMs) struggle with falsehoods and model hallucination . many efforts struggle to surpass 50% accuracy, with only targeted techniques reaching around 65% . |
| Approach: | They propose a truthfulness benchmark that focuses on imitative falsehoods . they use a set of 353 questions and answers inspired by common misconceptions based on the language . |
| Outcome: | The benchmark is available in Spanish, Catalan, Galician and English . it measures the truthfulness of multilingual LLMs using 353 questions and answers . |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering (2025.coling-main)
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| Challenge: | Existing methods for complex table question answering are often implicit, feeding the entire table into prompts. |
| Approach: | They propose a GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers. |
| Outcome: | The proposed method is able to identify the correct answers on two benchmark datasets and two LLM backbones. |
Persona-Consistent Dialogue Generation via Pseudo Preference Tuning (2025.coling-main)
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| Challenge: | Existing methods for improving persona consistency in dialogues require external resources. |
| Approach: | They propose a method for enhancing persona consistency in dialogue response generation using direct preference optimization using persona data. |
| Outcome: | The proposed method produces more consistent and natural responses than previous methods. |
Montague semantics and modifier consistency measurement in neural language models (2025.coling-main)
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| Challenge: | Existing studies on distributional language models have been focused on linguistics and their relationship with semantic formalisms for decades. |
| Approach: | They propose a method for measuring compositional behavior in contemporary language embedding models by introducing three new tests inspired by Montague semantics. |
| Outcome: | The proposed method measures compositional behavior in language embedding models on adjectival modifier phenomena in adjective-noun phrases. |
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters. |
| Approach: | They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA. |
| Outcome: | The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. |
Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models (2025.coling-main)
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| Challenge: | Symbols are used in abstract reasoning, chemical property prediction, and tabular question-answering. |
| Approach: | They propose a method that converts symbols to language-based representations to improve their accuracy. |
| Outcome: | The proposed method improves the accuracy of symbols in language-based models. |
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning (2025.coling-main)
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| Challenge: | Online abusive content detection, particularly in low-resource settings, remains underexplored. |
| Approach: | They propose to use pre-trained audio representations to detect abusive language in Indian languages using Few Shot Learning (FSL) . |
| Outcome: | The proposed model can be used to classify abusive language in 10 languages using the ADIMA dataset with FSL. |
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment. |
| Approach: | They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors. |
| Outcome: | The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. |
MOPO: Multi-Objective Prompt Optimization for Affective Text Generation (2025.coling-main)
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| Challenge: | Using multi-objective prompt optimization, users can choose the most appropriate prompt for their context. |
| Approach: | They propose a multi-objective prompt optimization methodology that optimizes prompts according to multiple objectives. |
| Outcome: | The proposed method improves performance by 15 pp across all objectives with a minimal loss (1–2 pp) |
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent (2025.coling-main)
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Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, Yi Fung, Preslav Nakov, Julia Hirschberg, Heng Ji
| Challenge: | Existing research on propaganda detection does not capture the motives behind the content or its broader impact. |
| Approach: | They propose a framework that dissects propaganda into techniques, arousal appeals, and underlying intent. |
| Outcome: | The proposed framework improves performance in a wide range of scenarios and can be used to identify and categorize propaganda techniques. |
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. |
| Approach: | They propose to use Multi-hop Questioning Answering under Knowledge Editing for Arabic Language to update and/or edit prior knowledge and test it via Multi-Hop Question Answering (MQA). |
| Outcome: | The proposed model outperforms baseline models by a significant margin . it can be used to update and/or edit prior knowledge and then test it with MQA . |
A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification (2025.coling-main)
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| Challenge: | Existing methods for hierarchical text classification struggle with fine-grained labels, leading to difficulties in accurate classification. |
| Approach: | They propose a hierarchical sequence ranking method for generating diverse negative samples using hierarchically structured hierarchic labels. |
| Outcome: | The proposed method achieves state-of-art (SOTA) on two datasets showing that it can distinguish between fine-grained labels and discriminate. |
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)
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| Challenge: | Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information. |
| Approach: | They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks. |
| Outcome: | The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs. |
A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models (2025.coling-main)
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| Challenge: | Existing approaches to handle missing data in tabular datasets rely on numerical estimations instead of pre-trained language models (LMs). |
| Approach: | They propose a method that leverages pre-trained language models to create contextually relevant descriptors for missing values. |
| Outcome: | The proposed approach outperforms the best-performing baselines in MCAR, MAR, and MNAR scenarios and offers a cost-effective solution for resource-constrained environments. |
Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Models (2025.coling-main)
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Sherzod Hakimov, Yerkezhan Abdullayeva, Kushal Koshti, Antonia Schmidt, Yan Weiser, Anne Beyer, David Schlangen
| Challenge: | Existing evaluation paradigms for text-only models are largely limited to a limited number of tasks and require little or no data and training cost. |
| Approach: | They propose to use a game-based evaluation paradigm to evaluate multimodal models by a goal-oriented game (self) play. |
| Outcome: | The proposed evaluation paradigm is more efficient than current methods for text-only models and is more cost-effective than existing methods. |
PADO: Personality-induced multi-Agents for Detecting OCEAN in human-generated texts (2025.coling-main)
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| Challenge: | Existing methods for personality detection are limited due to the latent and relative nature of personality and lack of annotated datasets. |
| Approach: | They propose a method that exploits the inherent knowledge of Large Language Models to capture the relative nature of personality traits by comparing contrasting perspectives. |
| Outcome: | The proposed approach exploits the inherent knowledge of Large Language Models to capture the relative nature of personality traits. |
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have been used in Knowledge Distillation (KD) to compress large models. |
| Approach: | They propose a Kullback-Leiber divergence method which adaptively allocates weights to combine RKL and FKL to reduce the size of Large Language Models (LLMs). |
| Outcome: | The proposed method outperforms baselines and improves diversity and quality of generated responses. |
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems often use a fixed strategy to extract information from multiple sources. |
| Approach: | They propose a method that dynamically determines optimal granularity of a knowledge source based on input queries using a router. |
| Outcome: | The proposed method predicts optimal granularity levels and significantly improves performance in downstream tasks. |
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons (2025.coling-main)
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| Challenge: | Existing methods to update factual knowledge overlook connections of same knowledge between different languages, resulting in knowledge conflicts and limited edit performance. |
| Approach: | They propose a method to edit multilingual knowledge simultaneously that avoids knowledge conflicts and improves edit performance. |
| Outcome: | The proposed method avoids knowledge conflicts and improves edit performance on bi-ZsRE and MzsRE benchmarks. |
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL (2025.coling-main)
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| Challenge: | Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question. |
| Approach: | They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases. |
| Outcome: | The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets. |
Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election (2025.coling-main)
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Roberto Mondini, Neema Kotonya, Robert L Logan IV, Elizabeth M. Olson, Angela Oduor Lungati, Daniel Odongo, Tim Ombasa, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Systematically organizing and geotagging large amounts of crowdsourced information requires substantial manual effort, often led by volunteers. |
| Approach: | They present a dataset of 14k citizen reports related to the 2022 Kenyan General Election . they investigate whether language models can assist in scalably categorizing and geotagging reports . |
| Outcome: | The proposed dataset aims to show whether language models can assist in categorizing and geotagging reports, thus highlighting its potential application in the AI for Social Good space. |
On Evaluating LLMs’ Capabilities as Functional Approximators: A Bayesian Evaluation Framework (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized the way we can formulate tasks in text-in-text-out format. |
| Approach: | They propose a new evaluation framework to comprehensively assess LLMs’ function modeling abilities by adopting a Bayesian perspective of function modeling. |
| Outcome: | The proposed evaluation framework enables LLMs to excel in utilizing prior knowledge to develop a strong understanding of the underlying function. |
Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference (2025.coling-main)
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| Challenge: | Creating NLP datasets with Large Language Models (LLMs) is an attractive alternative to relying on crowd-source workers. |
| Approach: | They recreate a portion of the Stanford Natural Language Inference corpus using GPT-4, Llama-2 70b for Chat, and Mistral 7b Instruct. |
| Outcome: | The proposed model can be used to generate NLP datasets with stereotypical biases and annotation artifacts. |
LLMs May Perform MCQA by Selecting the Least Incorrect Option (2025.coling-main)
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| Challenge: | Multiple Choice Question Answering (MCQA) is a fundamental format for various tasks in NLP, such as commonsense reasoning. |
| Approach: | They propose a method to increase the number of correct options in a dataset. |
| Outcome: | The proposed method improves the performance of multiple choice question answering (MCQA) and improves its accuracy. |
Benchmark Creation for Aspect-Based Sentiment Analysis in Low-Resource Odia Language and Evaluation through Fine-Tuning of Multilingual Models (2025.coling-main)
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| Challenge: | Aspect-based sentiment analysis is underexplored in low-resource languages such as Odia . a dataset is annotated for two tasks: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC) |
| Approach: | They propose to use a dataset for aspect-based sentiment analysis in Odia . they use ensemble data augmentation and a fine-tuned paraphrase generation model . |
| Outcome: | The proposed dataset is annotated for two tasks: ATE and APC . the proposed dataset will spur more work for the ABSA task in Odia . |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation (2025.coling-main)
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| Challenge: | Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration. |
| Approach: | They propose a novel approach that leverages the data characteristics of synthetic benchmarks to improve performance in real-world datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets and achieves a 29.94% improvement in Hits@1 on DOREMUS and 5.64% improvement on AGROLD. |
CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues (2025.coling-main)
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| Challenge: | Large-scale Wizard-Of-Oz dialogue datasets lack certain types of utterances, which would make them more realistic. |
| Approach: | They propose to use a large language model to create and repair communication errors in an automatic pipeline. |
| Outcome: | The proposed method is based on linguistic theory and uses a state-of-the-art Large Language Model (LLM) to create the error and repair it. |
JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language Models (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) focus on general domains, with fewer advancements in Japanese biomedical LLMs. |
| Approach: | They propose a benchmark for Japanese large language models with eight LLMs across four categories and 20 Japanese biomedical datasets for comparison. |
| Outcome: | The proposed benchmark includes eight LLMs across four categories and 20 Japanese biomedical datasets across five tasks. |
Automated Detection of Tropes In Short Texts (2025.coling-main)
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| Challenge: | Tropes are often used in movies to convey familiar patterns, but they also play a significant role in online communication . |
| Approach: | They propose to automatically detect tropes in social media posts by using a dataset . they define the task, distinguish it from previous work, and develop a machine learning technique . |
| Outcome: | The proposed method can detect tropes in social media posts with high accuracy. |
WER We Stand: Benchmarking Urdu ASR Models (2025.coling-main)
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| Challenge: | This paper analyzes the performance of three ASR models for low-resource languages like Urdu . low-rural languages like urdu have significant gaps in accuracy and reliability . |
| Approach: | They evaluate the performance of three ASR models: Whisper, MMS, and Seamless-M4T . they present the first conversational speech dataset for benchmarking Urdu ASR systems . |
| Outcome: | The proposed model families outperform Whisper, MMS, and Seamless-M4T on two types of speech datasets. |
CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)
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| Challenge: | Detecting fraudulent online text is essential as they exploit human greed and deceive individuals. |
| Approach: | They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months. |
| Outcome: | The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches. |
CateEA: Enhancing Entity Alignment via Implicit Category Supervision (2025.coling-main)
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| Challenge: | Existing Entity Alignment methods neglect the inherent semantic information of entities, limiting alignment precision and robustness. |
| Approach: | They propose to combine implicit category information into multi-modal representations by generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework. |
| Outcome: | Experiments on benchmark datasets show that CateEA outperforms state-of-the-art methods in various settings. |
Egalitarian Language Representation in Language Models: It All Begins with Tokenizers (2025.coling-main)
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| Challenge: | Tokenizers influence how language is represented in large language models . pre-tokenization choices can be problematic for some languages . |
| Approach: | They propose a tokenization algorithm that incorporates graphemes to improve tokenization . they validate this algorithm with Tamil, Sinhala, and Hindi scripts . |
| Outcome: | The proposed method outperforms tokenizers on Tamil, Sinhala, and Hindi scripts. |
PIRsuader: A Persuasive Chatbot for Mitigating Psychological Insulin Resistance in Type-2 Diabetic Patients (2025.coling-main)
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| Challenge: | Psychological Insulin Resistance (PIR) is a psychological barrier in diabetic patients . many patients have deep-rooted fears and misgivings related to insulin which hinder them from initiation of insulin-based treatments. |
| Approach: | They propose a persuasive chatbot that can mitiga psychological insulin resistance in diabetic patients . they harness conversation generation capabilities of state-of-the-art Large Language Models . |
| Outcome: | The proposed chatbot improves the willingness of patients to try insulin and addresses concerns in an empathetic manner. |
Continual Learning Using Only Large Language Model Prompting (2025.coling-main)
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| Challenge: | Existing continuous learning paradigms fine-tune language model parameters or use adapters or variants to adapt the LM. |
| Approach: | They propose a new continual learning paradigm wherein a large language model is regarded as a black box. |
| Outcome: | The proposed method outperforms baselines by a large margin in learning tasks incrementally. |
Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)
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| Challenge: | Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs. |
| Approach: | They propose a framework with three modules designed to assess diversity, redundancy, and difficulty. |
| Outcome: | The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs. |
Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas (2025.coling-main)
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| Challenge: | Existing studies on LMs have focused on linguistic generalizations and representations from developmentally plausible data. |
| Approach: | They propose to use phoneme- and grapheme-based language models to learn linguistic units at and below the word level. |
| Outcome: | The proposed models can achieve strong performance on syntactic and novel benchmarks and match grapheme-based models in standard tasks and novel evaluations. |
Evaluating Readability Metrics for German Medical Text Simplification (2025.coling-main)
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| Challenge: | Clinical reports and scientific information sources are written for medical experts, preventing patients from understanding the main messages of these texts. |
| Approach: | They evaluated the suitability of 18 statistical, part-of-speech-based, syntactic, semantic and fluency metrics to measure readability of German medical texts. |
| Outcome: | The proposed measures are compared with standard methods on English medical texts and simplified summaries. |
Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario (2025.coling-main)
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| Challenge: | Automated Grammatical Error Correction (GEC) is a scarcely explored low-resource language . a recent study focused on English, but it focused on Hindi, which presents unique challenges due to its complex syntax and intricate morphology. |
| Approach: | They propose to use a human-edited dataset to generate Hindi GEC data . they also investigate round trip translation using diverse languages for the technique . |
| Outcome: | The proposed method outperforms other methods in Hindi, showing that it is highly efficient. |
MuPe Life Stories Dataset: Spontaneous Speech in Brazilian Portuguese with a Case Study Evaluation on ASR Bias against Speakers Groups and Topic Modeling (2025.coling-main)
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Sidney Evaldo Leal, Arnaldo Candido Junior, Ricardo Marcacini, Edresson Casanova, Odilon Gonçalves, Anderson Silva Soares, Rodrigo Freitas Lima, Lucas Rafael Stefanel Gris, Sandra Aluísio
| Challenge: | Recent datasets for automatic speech recognition in Brazilian Portuguese lack diversity in terms of age groups, regional accents, and education levels. |
| Approach: | They propose to use a dataset to analyze the impact of ASR in Brazilian Portuguese (BP) they demonstrate that current models are biased regarding age, education, and regional accents. |
| Outcome: | The proposed dataset helps mitigate biases in current ASR models regarding education levels and age groups. |
Multi-Layered Evaluation Using a Fusion of Metrics and LLMs as Judges in Open-Domain Question Answering (2025.coling-main)
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| Challenge: | Existing methods for comparing machine-generated answers with reference are not perfect in terms of accuracy or cost. |
| Approach: | They propose to summarize long answers and use shortened versions to improve evaluation . they propose a multi-layered evaluation methodology that integrates different metrics tailored to various scenarios . |
| Outcome: | The proposed method outperforms existing evaluation methods but is more cost-effective than existing methods. |
BERT-based Classical Arabic Poetry Authorship Attribution (2025.coling-main)
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| Challenge: | AA in Arabic poetry has been a significant issue since the 9th century due to the loss of pre-Islamic poetry and the misattribution of post-Islamical works to earlier poets. |
| Approach: | They propose a computational approach to authorship attribution in Arabic poetry using the entire Classical Arabic Poetry corpus for the first time. |
| Outcome: | The proposed model achieves F1 scores ranging from 0.97 to 1.0 and was applied to four pre-Islamic misattribution cases. |
It’s What You Say and How You Say It: Investigating the Effect of Linguistic vs. Behavioral Adaptation in Task-Oriented Chatbots (2025.coling-main)
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| Challenge: | linguistic adaptation is not known to have a positive impact on dialog success and user perception. |
| Approach: | They evaluate subjective and objective aspects of dialog success and user perceptions through a user study . they also examine linguistic adaptations of dialog agents to determine which aspects influence user perception . |
| Outcome: | The proposed agents can differ in their level of formality and their linguistic style. |
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation (2025.coling-main)
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Hyeonseok Lim, Dongjae Shin, Seohyun Song, Inho Won, Minjun Kim, Junghun Yuk, Haneol Jang, KyungTae Lim
| Challenge: | Existing evaluation datasets for external knowledge-based VQA lack a capability to determine which passage is useful for answering queries. |
| Approach: | They propose a visual question answering benchmark for vision language models based on retrieval augmented generation (RAG) the proposed benchmark includes five input passages, a capability lacking in previous research. |
| Outcome: | The proposed benchmark includes five input passages and is validated using the state-of-the-art Llama3-based VLM, the Llava-Llamama-3 model. |
LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)
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| Challenge: | Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets. |
| Approach: | They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model. |
| Outcome: | The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data. |
Bilingual Evaluation of Language Models on General Knowledge in University Entrance Exams with Minimal Contamination (2025.coling-main)
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Eva Sánchez Salido, Roser Morante, Julio Gonzalo, Guillermo Marco, Jorge Carrillo-de-Albornoz, Laura Plaza, Enrique Amigo, Andrés Fernandez García, Alejandro Benito-Santos, Adrián Ghajari Espinosa, Victor Fresno
| Challenge: | Existing benchmarks for Large Language Models have been proposed as single-task evaluations, but they are not fully comprehensive. |
| Approach: | They present a bilingual dataset that contains 1003 multiple-choice questions in Spanish and English. |
| Outcome: | The proposed model ranking is almost identical to the one obtained with MMLU . |
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)
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| Challenge: | Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered . |
| Approach: | They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts. |
| Outcome: | The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script. |
DROWN: Towards Tighter LiRPA-based Robustness Certification (2025.coling-main)
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| Challenge: | Existing methods for certifying the robustness of deep neural networks suffer from precision or scalability issues. |
| Approach: | They propose a method to certify the robustness of deep neural networks . they propose to use two pairs of linear bounds to refine pre-activation bounds . |
| Outcome: | The proposed method achieves higher certified robustness than the baseline on CNNs and 4.68 times larger certified radii than the Transformers. |
Large Language Models with Reinforcement Learning from Human Feedback Approach for Enhancing Explainable Sexism Detection (2025.coling-main)
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| Challenge: | Recent advances in natural language processing have significantly improved text comprehension. |
| Approach: | They propose a Reinforcement Learning from Human Feedback (RLHF) based fine-tuning framework for sexism detection that leverages contextual learning to understand and apply instructions to new scenarios without additional training. |
| Outcome: | The proposed framework outperforms existing models on three EDOS tasks and scores 0.8681 on binary sexism detection, 0.6829 on category classification of sexists and 0.4722 on task C. |
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification (2025.coling-main)
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Shijing Chen, Mohamed Reda Bouadjenek, Usman Naseem, Basem Suleiman, Shoaib Jameel, Flora Salim, Hakim Hacid, Imran Razzak
| Challenge: | Multi-level Hierarchical Classification (MLHC) is a critical tool in modern data analysis. |
| Approach: | They propose a taxonomy-embedded transitional LLM-agnostic framework for multimodality classification that leverages large language models to enforce consistency across hierarchical levels. |
| Outcome: | The proposed framework improves on the MEP-3M dataset with various hierarchical levels compared to conventional models. |
Representation Purification for End-to-End Speech Translation (2025.coling-main)
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| Challenge: | Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. |
| Approach: | They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST. |
| Outcome: | The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a *transcript-free* setting. |
Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)
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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak
| Challenge: | Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context. |
| Approach: | They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations . |
| Outcome: | The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say . |
HYDEN: Hyperbolic Density Representations for Medical Images and Reports (2025.coling-main)
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| Challenge: | Existing methods for visual semantic representation learning struggle to address semantic uncertainty, especially in the medical domain. |
| Approach: | They propose a hyperbolic density embedding based image-text representation learning approach tailored for specific medical domain data. |
| Outcome: | The proposed method performs better than baseline methods on zero-shot tasks and fine-tuning tasks on different datasets. |
Towards Human Understanding of Paraphrase Types in Large Language Models (2025.coling-main)
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| Challenge: | Current paraphrase evaluations of language models use binary approaches, offering limited interpretability of specific text changes. |
| Approach: | They introduce a dataset of 800 sentence-level and word-level annotations by 15 annotators and a human preference ranking of paraphrases with different types. |
| Outcome: | The proposed model can generate simple APTs, but struggle with complex structures (e.g., subordination changes). |
Just Read the Codebook! Make Use of Quality Codebooks in Zero-Shot Classification of Multilabel Frame Datasets (2025.coling-main)
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| Challenge: | Recent development of Large Language Models has led to more scrutiny of their performance on complex datasets. |
| Approach: | They propose to use large language models to provide concise instructions on how to code text with a multitude of complex labels on two datasets with varying topics. |
| Outcome: | The proposed approach outperforms few-shot In-Context-Learning setups on two complex datasets and is token-efficient and requires less hands-on engineering. |
NLP for preserving Torlak, a vulnerable low-resource Slavic language (2025.coling-main)
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| Challenge: | Torlak is an endangered, low-resource Slavic language with a high degree of areal and inter-speaker variation. |
| Approach: | They aim to improve the prediction of morphosyntactic annotations for this low-resource Slavic language using the fine-tuning of large language models. |
| Outcome: | The proposed models improve the prediction of morphosyntactic annotations for Torlak using fine-tuning of large language models. |
Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models (2025.coling-main)
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| Challenge: | In Context-aware Machine Translation, the context sentences are available to the system and can be used to maintain coherence of translation and resolve ambiguities. |
| Approach: | They investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English- to-French directions. |
| Outcome: | The attention heads influence the models' ability to disambiguate pronouns in the English-to-German and English- to-French directions. |
ModaFact: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection (2025.coling-main)
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| Challenge: | NLP studies have mostly dealt with factuality and modality separately . linguistic modality conveys the relationship a situation is supposed to have with respect to wishes, norms, goals, authority, etc. |
| Approach: | They propose a resource with joint factuality and modality information for event-denoting expressions in Italian. |
| Outcome: | The proposed resource is consistent with existing ones and compares classification systems trained on italy's ModaFact dataset and best-performing model. |
Why Does ChatGPT “Delve” So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models (2025.coling-main)
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| Challenge: | Scientific English is currently undergoing rapid change, with words like “delve,” “intricate,” and “underscore” appearing far more frequently than just a few years ago. |
| Approach: | They propose a formal method to characterize scientific English linguistic changes . they propose lexical overrepresentation by reinforcement learning from human feedback . |
| Outcome: | The proposed method yields 21 focal words whose increased occurrence in scientific abstracts is likely the result of LLM usage. |
Evaluating Pixel Language Models on Non-Standardized Languages (2025.coling-main)
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| Challenge: | pixel-based models can be used to transfer learning from standard languages to dialects . pretrained language models achieve strong results for languages seen during training, but their performance declines with out-of-domain dialects. |
| Approach: | They compare pixel-based models to token-based ones to evaluate dialects . standard german is tokenized in a more meaningful way, whereas the Bern dialect is tokenize in pixel form . |
| Outcome: | The proposed models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. |
LOLA – An Open-Source Massively Multilingual Large Language Model (2025.coling-main)
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Nikit Srivastava, Denis Kuchelev, Tatiana Moteu Ngoli, Kshitij Shetty, Michael Roeder, Hamada Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo
| Challenge: | Using a sparse Mixture-of-Experts Transformer architecture, our model is highly efficient and efficient across languages. |
| Approach: | They propose a multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. |
| Outcome: | The proposed model performs well on natural language generation and understanding tasks while avoiding the common pitfalls of multilinguality. |
Cross-Lingual Sentence Compression for Length-Constrained Subtitles in Low-Resource Settings (2025.coling-main)
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| Challenge: | a new system for sentence compression is developed for broadcast and live media . the goal is to reduce the on-screen visual constraints of the text . |
| Approach: | They develop a machine translation and sentence compression system that trains on openly available parallel corpora organized by compression ratios. |
| Outcome: | The proposed system preserves high semantic meaning and metric evaluations for compressed contexts. |
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)
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| Challenge: | Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation. |
| Approach: | They propose a method for generating and validating QA datasets for low-resource languages . they use English data as context to generate synthetic multiple-choice (MC) question-answer pairs . |
| Outcome: | The proposed method maintains quality, reduces likelihood of factual errors, and circumvents costly annotation. |
Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models (2025.coling-main)
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| Challenge: | a study examines the behavior of routers in Mixture of Experts (MoE) models . experts with similar linguistic traits are often routed to the same expert regardless of context . |
| Approach: | They investigate how tokens are routed based on their linguistic features . they aim to explore whether experts specialize in processing tokens with similar linguistic traits . |
| Outcome: | The proposed model-integrated routers are based on Mixture of Experts (MoE) models . the results show that expert specialization is high for POS categories . |
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks (2025.coling-main)
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| Challenge: | Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a benchmark for textual adversarial defence that evaluates state-of-the-art defence mechanisms across diverse datasets, models, and tasks. |
| Outcome: | The proposed benchmark incorporates a wide range of datasets and evaluates state-of-the-art defence mechanisms. |
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings (2025.coling-main)
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| Challenge: | Prior work has demonstrated the importance of the conversational context in stance detection. |
| Approach: | They propose a multimodal architecture for stance detection that fuses transformer-based content embedding with unsupervised structural embeddment. |
| Outcome: | The proposed model outperforms strong baselines on common benchmarks and outperformed existing models on common frameworks. |
IRUEX: A Study on Large Language Models Problem-Solving Skills in Iran’s University Entrance Exam (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have profound implications for education. |
| Approach: | They present a novel multiple-choice educational resource specifically designed to evaluate the performance of Large Language Models (LLMs) they use a dataset that contains 868 questions and 36,485 additional questions . |
| Outcome: | The IRUEX dataset contains 868 questions and 36,485 additional questions. |
data2lang2vec: Data Driven Typological Features Completion (2025.coling-main)
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| Challenge: | Language typology databases are useful for multilingual Natural Language Processing (NLP) but their coverage is limited, with only 28.9% of all possible combinations specified in the database. |
| Approach: | They propose to use textual data to improve feature prediction by using a multi-lingual Part-of-Speech tagger and a more realistic evaluation setup to focus on likely to be missing typology features. |
| Outcome: | The proposed model outperforms previous studies on missing features in 1,749 languages and with external statistical features and machine learning algorithms. |
Explanation Regularisation through the Lens of Attributions (2025.coling-main)
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| Challenge: | Explanation regularisation (ER) is a method to guide text classifiers to form their predictions relying on tokens that humans consider plausible. |
| Approach: | They introduce an auxiliary explanation loss to measure how well an input attribution technique's output agrees with human-annotated rationales. |
| Outcome: | The proposed model improves classification performance in out-of-domain (OOD) settings by relying on tokens humans consider plausible. |
Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs (2025.coling-main)
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| Challenge: | a fine-tuned small language model (SLM) can generate human-like text, but it requires immense computational resources and large datasets. |
| Approach: | They evaluate the creative writing abilities of a fine-tuned small language model, BART-large . they compare it to human writers and two large language models: GPT-3.5 and GPT-4o . |
| Outcome: | The proposed model outperforms human writers and two large language models in two experiments . the results highlight how model size and fine-tuning influence creativity, fluency, and coherence . |
Generics are puzzling. Can language models find the missing piece? (2025.coling-main)
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| Challenge: | Generic sentences express generalisations about the world without explicit quantification . human biases in stereotypes can be observed in language models, authors say . |
| Approach: | They analyze generic sentences to determine their quantification and quantify their implicit quantifications using language models. |
| Outcome: | The proposed model shows that generics are more context-sensitive than determiner quantifiers and express weak generalisations. |
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination – generating content that does not align with realworld facts. |
| Approach: | They propose to extrapolate critical token probabilities beyond the last layer to improve decoding by manipulating the predicted distributions at inference time. |
| Outcome: | The proposed methods surpass state-of-the-art on multiple datasets by large margins. |
Iterative Structured Knowledge Distillation: Optimizing Language Models Through Layer-by-Layer Distillation (2025.coling-main)
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| Challenge: | Structured pruning and knowledge distillation are often not efficient and require a fixed architecture, limiting flexibility. |
| Approach: | They propose a method which integrates knowledge distillation and structured pruning by replacing transformer blocks with smaller, efficient versions during training. |
| Outcome: | The proposed method outperforms L1 pruning and maintains four-fifths of performance on language modeling and commonsense reasoning tasks. |
Why do language models perform worse for morphologically complex languages? (2025.coling-main)
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| Challenge: | Language models perform differently across languages, a new study suggests . morphological typology may explain some of the performance differences, authors say . |
| Approach: | They propose to test morphological alignment of tokenizers, tokenization quality and disparities in dataset sizes and measurement to test this hypothesis. |
| Outcome: | The proposed model shows that fusional languages perform better than fusionative languages . the authors suggest that morphological typology may explain some of the performance differences . |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
| Outcome: | The proposed pipeline achieves state-of-the-art across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks. |
Beyond Surprisal: A Dual Metric Framework for Lexical Skill Acquisition in LLMs (2025.coling-main)
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| Challenge: | Existing learning curves capture when and how a model learns to use words correctly, but they neglect the equally important skill of avoiding incorrect usage. |
| Approach: | They propose a new metric which measures a model's capacity to refrain from using words in unexpected or unexpected contexts. |
| Outcome: | The proposed metric measures the model's ability to refrain from using words in unexpected or unexpected contexts. |
RUAccent: Advanced System for Stress Placement in Russian with Homograph Resolution (2025.coling-main)
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| Challenge: | a novel approach to stress placement in Russian text is proposed . homographs are spelled identically but have different meanings and stress patterns . previous approaches to stress placing in Russian have struggled with homograph . |
| Approach: | They propose a system that uses morphological analysis, context-aware neural models, and a specialized "-fikator" they find that the system places stress accurately on standard Russian words and resolves homographs based on their context . |
| Outcome: | The proposed system outperforms existing solutions on homographs and non-homograph words. |
On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning (2025.coling-main)
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| Challenge: | Text-based reinforcement learning is a form of interactive fiction where players manipulate the environment using text and admissible actions in natural language. |
| Approach: | They show that rich semantic understanding leads to efficient training of text-based RL agents . they also show that semantic degeneration occurs when LMs are inappropriately fine-tuned . |
| Outcome: | The results suggest that semantic understanding is not important for the task . they also show that fine-tuning language models can degenerate the agent's performance . |
HateBRXplain: A Benchmark Dataset with Human-Annotated Rationales for Explainable Hate Speech Detection in Brazilian Portuguese (2025.coling-main)
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| Challenge: | Hate speech detection systems have been developed to inhibit offensive and hateful language from being published or spread on the Web and social media. |
| Approach: | They propose to use a Portuguese dataset to provide rationales for hate speech detection with text span annotations. |
| Outcome: | The proposed models outperform the baselines in Portuguese and showed that they provide plausible explanations when compared to human annotations. |
LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)
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| Challenge: | Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA). |
| Approach: | They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics. |
| Outcome: | The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood. |
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
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Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
| Outcome: | The proposed models can extract metaphors from literary texts without using domain experts. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics (2025.coling-main)
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| Challenge: | Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation. |
| Approach: | They propose a framework that uses large language models to analyze speaker characteristics . they use two-stage learning to make the models reason speaker characteristics and track emotion of the speaker . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Analysing Zero-Shot Readability-Controlled Sentence Simplification (2025.coling-main)
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| Challenge: | Text simplification (RCTS) models often depend on parallel corpora with readability annotations on both source and target sides. |
| Approach: | They propose to use instruction-tuned large language models for zero-shot RCTS to reduce reliance on parallel corpora with readability annotations on both source and target sides. |
| Outcome: | The proposed model can generate sentences with the desired readability, but the model's limitations and characteristics of the source sentences impede it. |
The Invalsi Benchmarks: measuring the Linguistic and Mathematical understanding of Large Language Models in Italian (2025.coling-main)
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| Challenge: | Invalsi MATE is a high-resource language, but there are few benchmarks to evaluate generative Large Language Models in this language. |
| Approach: | They propose three benchmarks to evaluate language models on mathematical understanding in italian . they use the Invalsi tests, which are administered to students aged 6 to 18 in the italian school system . |
| Outcome: | The proposed benchmarks are based on the Invalsi tests and the Italian highschool math Olympics. |
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| Challenge: | Existing methods to mitigate hallucinations generate erroneous or fabricated information. |
| Approach: | They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text. |
| Outcome: | The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks. |
Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection (2025.coling-main)
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| Challenge: | Existing methods for crowdsourcing data collection require a human workforce, which is hard to sustain. |
| Approach: | They propose to use Speech Foundation Models to automate validation processes . they find that SFMs can reduce reliance on human validation . |
| Outcome: | The proposed model reduces the reliance on human validation without degrading the quality of the final data. |
Improving Accessibility of SCOTUS Opinions: A Benchmark Study and a New Dataset for Generic Heading Prediction and Specific Heading Generation (2025.coling-main)
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| Challenge: | SCOTUS opinions are notoriously long and use specialised language, making them laborious to read and understand. |
| Approach: | They propose generic and specific headings for each section to be trained automatically . they compare the performance of different systems trained for each subtask . |
| Outcome: | The proposed system can achieve a score of 0.90% in predicting generic headings . the proposed system also achieves similar scores in generating specific headings. |
SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts (2025.coling-main)
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| Challenge: | Existing frameworks for evaluating robustness of large language models rely on standardized benchmarks that can escalate costs and limit evaluations across domains. |
| Approach: | They propose a framework to evaluate the robustness of large language models using adversarial prompts and domain-constrained knowledge guidelines. |
| Outcome: | The proposed framework reduces dependency on conventional benchmarks and provides efficient evaluations in constrained domains. |
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models (2025.coling-main)
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| Challenge: | a new analysis leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). |
| Approach: | They propose to use linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). |
| Outcome: | The proposed analysis reveals that linguistic similarity is significantly influenced by training data exposure, leading to higher cross-LLM agreement in higher-resource languages. |
MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)
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| Challenge: | Existing research indicates that LLMs can be overconfident and stubborn. |
| Approach: | They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate. |
| Outcome: | The proposed approach outperforms established baselines on various ERE tasks and LLMs. |
Cross Domain Classification of Education Talk Turns (2025.coling-main)
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| Challenge: | Prior research has focused on the annotation of conversational talk-turns within the classroom, offering a statistical analysis of the various types of discourse prevalent in these environments. |
| Approach: | They examine the generalizability and transferability of text classifiers trained to predict classroom discourse across educational domains by accompanying each talk turn with dialog-level context. |
| Outcome: | The proposed models exhibit high generalizability when training and test datasets originate from the same or similar domains. |
Automated Molecular Concept Generation and Labeling with Large Language Models (2025.coling-main)
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| Challenge: | Concept-based models lack explainability and need predefined concepts and manual labeling in molecular science. |
| Approach: | They propose a framework that leverages Large Language Models to generate and label predictive molecular concepts without human input. |
| Outcome: | The proposed framework outperforms existing models on several benchmarks while maintaining explainability and allowing easy intervention. |
URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base (2025.coling-main)
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Aditya Armaan Khan, Mason Stephen Shipton, David Anugraha, Kaiyao Duan, Phuong H. Hoang, Eric Khiu, A. Seza Doğruöz, Annie Lee
| Challenge: | URIEL is limited in terms of linguistic inclusion and overall usability . URIel+ provides robust, customizable distance calculations to better suit the needs of users. |
| Approach: | They propose a new version of URIEL and a query tool that provides a standardized approach to representing languages as geographical, phylogenetic, and typological vectors. |
| Outcome: | URIEL+ expands the user experience with robust, customizable distance calculations to better suit the needs of users. |
A Framework for Effective Invocation Methods of Various LLM Services (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are becoming a fundamental tool for various natural language processing tasks due to commercial reasons, the potential risk of misuse and expensive tuning cost. |
| Approach: | They propose a framework for constructing an effective LLM services invocation strategy that best meets task demands. |
| Outcome: | The proposed framework classifies existing methods into four categories: input abstraction, semantic cache, solution design, and output enhancement, which can be used separately or jointly during the invocation life cycle. |
DP-FROST: Differentially Private Fine-tuning of Pre-trained Models with Freezing Model Parameters (2025.coling-main)
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| Challenge: | Training models with differential privacy has received a lot of attention since it provides theoretical guarantee of privacy preservation. |
| Approach: | They propose methods that fine-tune large-scale pre-trained models with freezing unimportant parameters for downstream tasks while satisfying differential privacy. |
| Outcome: | The proposed methods fine-tune large pre-trained models with freezing unimportant parameters while satisfying differential privacy while preserving their utility. |
Evaluating LLMs’ Capability to Identify Lexical Semantic Equivalence: Probing with the Word-in-Context Task (2025.coling-main)
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| Challenge: | Existing methods to evaluate the capability of large language models to identify lexical semantic equivalence are not currently being used. |
| Approach: | They propose to use the Word-in-Context (WiC) task to determine whether the meanings of a target word remain identical across different contexts to evaluate their capability. |
| Outcome: | The proposed method outperforms other LLMs in the Word-in-Context (WiC) task. |
Close or Cloze? Assessing the Robustness of Large Language Models to Adversarial Perturbations via Word Recovery (2025.coling-main)
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| Challenge: | Existing models implicitly recover the original text, but it is unclear when they rely on context and when they implicitly do so. |
| Approach: | They propose to use a dictionary to recover adversarial words by using a phonetic, typo, and visual attack to study word recovery performance. |
| Outcome: | The proposed model outperforms open-source models on hateful, offensive, and toxic classification tasks. |
NüshuRescue: Reviving the Endangered Nüshu Language with AI (2025.coling-main)
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| Challenge: | Nüshu is a rare syllabic script used by Yao women in china for self-expression . a lack of data makes the reconstruction labor-intensive and costly . |
| Approach: | They propose an AI-driven framework to train large corpora on endangered languages . Nüshu is a rare syllabic script used by Yao women in china for self-expression . |
| Outcome: | NüshuRescue automates evaluation and expands target corpora to accelerate linguistic revitalization. |
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering (2025.coling-main)
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| Challenge: | e-health records underscore the growing significance of information extraction (IE) from these datasets. |
| Approach: | They propose a target-oriented pre-training paradigm for extractive question-answering in the medical domain . TOP-Training moves one step further than popular domain-oriented fine-tuning . |
| Outcome: | The proposed method improves on the Medical-EQA benchmarks. |
Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations (2025.coling-main)
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| Challenge: | Existing LLMs rely on static, predefined personas to capture dynamic and evolving nature of human personalities. |
| Approach: | They propose a dataset with 400,000 conversations and a framework for generating personalized conversations using long-form journal entries from Reddit. |
| Outcome: | The proposed framework generates high-quality, personality-rich dialogues grounded in reddit journal entries. |
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index (2025.coling-main)
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| Challenge: | Prompt engineering is a growing subdiscipline of natural language processing . a lack of appropriate consideration for the financial constraints of computationally burdensome methods can limit their adoption and impact. |
| Approach: | They propose a new metric that combines accuracy scores with token consumption to reflect different resource constraints. |
| Outcome: | The economic prompting index (EPI) measures the performance of 6 prompting techniques across 10 widely-used language models and 4 diverse datasets. |
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)
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| Challenge: | Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind. |
| Approach: | They propose a realistic setting in which they aim to translate between a high-resource and a low-resourced language with limited parallel data, a bilingual dictionary, and c) a monolingual target-domain corpus in the high-rsource language. |
| Outcome: | The proposed methods are compared with a human evaluation of DALI and show that the most effective is the simplest. |
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (2025.coling-main)
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| Challenge: | Existing studies show that dependency information is used only for encoder-only-based relation extraction tasks. |
| Approach: | They propose a syntax-aware seq2seq pre-trained model for relation extraction that incorporates dependency information into a seq2-trained language model by continual pre-training with a dependency parsing task. |
| Outcome: | The proposed model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a generative sequence-to-sequence (sequ2sq)-based dependency parsing task. |
Exploring Language Model Generalization in Low-Resource Extractive QA (2025.coling-main)
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| Challenge: | Existing LLMs struggle with dataset demands of closed domains such as medicine and law . current LLM performance in closed domain is lacking, even on traditional tasks such as Natural Language Inference . |
| Approach: | They investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift . they find that LLMs struggle with dataset demands of closed domains . |
| Outcome: | The proposed model performs poorly in extractive question answering tasks under domain drift . the proposed model can generalize to domains that require specific knowledge without training . |
Explain-Analyze-Generate: A Sequential Multi-Agent Collaboration Method for Complex Reasoning (2025.coling-main)
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| Challenge: | Multiagent debate (MAD) is a popular approach for large language models . however, the performance of LLMs is suboptimal in complex reasoning scenarios . |
| Approach: | They propose a sequential collaboration framework to enable agents to provide constructive assistance to peers by decomposing complex tasks into essential subtasks. |
| Outcome: | The proposed framework achieves the highest performance on 19 out of 23 tasks and lower costs compared to MAD. |
Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods for rumor detection are limited in labeled data, but social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. |
| Approach: | They propose a framework for rumor detection with Graph Supervised Contrastive Learning that heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumors detection. |
| Outcome: | The proposed framework heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection. |
Addressing the Training-Inference Discrepancy in Discrete Diffusion for Text Generation (2025.coling-main)
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| Challenge: | Existing discrete diffusion models for text generation have a discrepancy between training and inference. |
| Approach: | They propose a training schema that considers two-step diffusion processes and a scheduling technique that gradually increases the probability of using self-generated text as training progresses. |
| Outcome: | The proposed training schema and scheduling technique improve diffusion models on four widely used datasets. |
Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model (2025.coling-main)
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| Challenge: | Pretrained Language Models excel in various Natural Language Processing tasks, but performance on social media applications like rumor detection remains suboptimal. |
| Approach: | They propose a pretraining strategy to infuse information from propagation structures into pretrained language models to capture interactions of stance and sentiment crucial for rumor detection. |
| Outcome: | The proposed model outperforms existing methods on social media applications and significantly improves rumor detection performance. |
EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs (2025.coling-main)
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Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, Weiguo Chen
| Challenge: | Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLM or suffer from prohibitive computational costs due to tight coupling. |
| Approach: | They propose a framework that can strike a balance between performance and efficiency via an iterative paradigm. |
| Outcome: | The proposed framework can strike a balance between performance and efficiency via an iterative paradigm. |
Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025.coling-main)
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| Challenge: | Popularity of Large Language Models (LLMs) has seen a skyrocketing increase in recent years. |
| Approach: | They present a systematic review of the language adaptation process for Large Language Models including vocabulary expansion, continued pre-training, and instruction fine-tuning. |
| Outcome: | The proposed model is based on empirical studies conducted on LLaMA2 and discussions on various settings affecting the model's capabilities. |
Dialectal and Low Resource Machine Translation for Aromanian (2025.coling-main)
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| Challenge: | Existing training methods for low-resource languages are focused on English or are massively multilingual, but do not consider the particularities of lowresource language. |
| Approach: | They propose a neural machine translation system that can translate between Romanian, English, and Aromanian. |
| Outcome: | The proposed system can translate between Romanian, English, and Aromanian . BLEU scores range from 17 to 32 depending on direction and genre of text . |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
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| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)
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| Challenge: | Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts. |
| Approach: | They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels. |
| Outcome: | The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels. |
Cross-lingual Social Misinformation Detector based on Hierarchical Mixture-of-Experts Adapter (2025.coling-main)
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| Challenge: | a global trend of misinformation is affecting non-native speaker users who are more susceptible to misinformation on foreign social media platforms. |
| Approach: | They propose a method to integrate sentiment analysis as an auxiliary task and a hierarchical routing strategy and expert-mask mechanism to enhance cross-lingual social misinformation detection. |
| Outcome: | The proposed method improves cross-lingual social misinformation detection in non-native speakers with only monolingual social media histories. |
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) are not effective in solving real-world healthcare tasks, but they are able to provide demographic information and provide biased health predictions. |
| Approach: | They evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLM to real-world healthcare tasks. |
| Outcome: | The proposed models perform poorly in real-world healthcare tasks and are inconsistent with existing learning frameworks. |
A Text Embedding Model with Contrastive Example Mining for Point-of-Interest Geocoding (2025.coling-main)
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| Challenge: | Existing studies have focused on coarse-grained locations, but we focus on fine-grain POIs, which have many candidates with similar names. |
| Approach: | They develop a text embedding-based geocoding model and investigate (1) entry encoding representations and (2) hard negative mining approaches suitable for enhancing the model’s disambiguation ability. |
| Outcome: | The proposed model significantly improves its disambiguation ability and entry encoding representations. |
In-context Continual Learning Assisted by an External Continual Learner (2025.coling-main)
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| Challenge: | Existing continual learning methods suffer from catastrophic forgetting (CF) . Existing methods rely on fine-tuning or adapting large language models (LLMs) |
| Approach: | They propose an approach that integrates an external continual learner (ECL) with ICL to enable scalable CL without catastrophic forgetting (CF). |
| Outcome: | The proposed approach outperforms existing baselines while maintaining high performance. |
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods for Document-level Relation Extraction assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. |
| Approach: | They propose a method that leverages the Variational Autoencoder architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. |
| Outcome: | The proposed method outperforms state-of-the-art models on two benchmark datasets and is available on github. |
Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection (2025.coling-main)
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| Challenge: | Existing methods for detecting hateful memes rely on extensive training. |
| Approach: | They propose a method that integrates evolution attribute and in-context information of memes into large multimodal models via Chain-of-Evolution (CoE) prompting. |
| Outcome: | The proposed method improves existing methods on public datasets and can be used as interpretive tool to promote understanding of evolution of memes. |
An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning (2025.coling-main)
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| Challenge: | Existing models for dialogue policy training consider one-step dialogues, leading to inaccurate simulations. |
| Approach: | They propose a framework for dialogue policy learning that trains an agent to select dialogue actions via deep reinforcement learning. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three dialogue datasets . it uses model-based reinforcement learning with automatically constructed causal chains . |
Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction (2025.coling-main)
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| Challenge: | Existing methods to extract triplets from context often decompose into named entity recognition and relation classification, which may introduce error propagation. |
| Approach: | They propose a Relation-centric joint ZSRTE method which leverages unseen relation labels to extract triplets in one go. |
| Outcome: | The proposed method achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. |
CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation (2025.coling-main)
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| Challenge: | Existing methods for generating human-like dialogues lack implicit correlations among factors . different factors may alternately dominate token-level response generation during decoding . |
| Approach: | They propose a framework that can model complex multiple interaction factors to generate human-like conversations. |
| Outcome: | The proposed framework generates human-like conversations with multiple factors compared to state-of-the-art methods . et al. show that the proposed framework produces superior results over existing methods compared with the state- of-the art methods based on multiple datasets . |
Courtroom-LLM: A Legal-Inspired Multi-LLM Framework for Resolving Ambiguous Text Classifications (2025.coling-main)
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| Challenge: | Using a multi-LLM structure inspired by legal courtroom processes, we demonstrate that it can improve decision-making accuracy in ambiguous text classification scenarios. |
| Approach: | They propose a legal-inspired multi-LLM structure that simulates a courtroom setting within LLMs and assigns roles similar to those of prosecutors, defense attorneys, and judges. |
| Outcome: | The proposed model outperforms both single-LLM classifiers and simpler multi-LLMS setups in ambiguous text classification tasks. |
RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems (2025.coling-main)
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| Challenge: | Existing approaches to combat character hallucination are vulnerable to attack . large language models (LLMs) are capable of generating responses inconsistent with intended personas . |
| Approach: | They propose a novel defence strategy that generates supplemental context through narration to mitigate role-query conflicts and improve query generalization. |
| Outcome: | The proposed defence strategy outperforms refusal-based strategies in character hallucinations and query generalization. |
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
Cognate Detection for Historical Language Reconstruction of Proto-Sabean Languages: the Case of Ge’ez, Tigrinya, and Amharic (2025.coling-main)
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| Challenge: | As languages evolve, we risk losing ancestral languages. |
| Approach: | They propose to use cognates to reconstruct proto-languages from cognates in child languages that have likely evolved from the same word in the proto-linguistics. |
| Outcome: | The proposed method is based on automatic cognate detection and in-context learning with GPT-4o to generate the proto-language from the cognates and use Sequence-to-Sequence models. |
Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings (2025.coling-main)
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| Challenge: | Existing studies on cosine similarity focus on the angle or correlation coefficient, but this study proposes a novel interpretation of the term word similarity. |
| Approach: | They propose a method for selecting statistically significant axes by deriving the probability distributions that govern each component and the product of components. |
| Outcome: | The proposed interpretation of cosine similarity is demonstrated through intuitive numerical examples and thorough numerical experiments. |
Piecing It All Together: Verifying Multi-Hop Multimodal Claims (2025.coling-main)
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| Challenge: | Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. |
| Approach: | They propose a task that requires models to reason over multiple pieces of evidence . they construct a large-scale dataset comprising 15k multi-hop claims paired with multimodal evidence - generated and refined using large language models with additional input from human feedback. |
| Outcome: | The proposed method is based on human performance benchmarks and human reasoning hops. |
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing language models lack data and computation power, but they are extremely parameter-heavy and difficult to train. |
| Approach: | They propose a retrieval augmented generation framework backed by a large language model to correct the output of a smaller model for morphological glossing. |
| Outcome: | The proposed model is highly effective in data-scarce settings and offers a state-of-the-art for morphological glossing. |
Large Language Model-Based Event Relation Extraction with Rationales (2025.coling-main)
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| Challenge: | Existing methods for ERE rely on large language models, but they face limitations. |
| Approach: | They propose an LLM-based approach with rationales for the ERE task . LLMERE transforms ERE into a question-and-answer task that may have multiple answers . |
| Outcome: | Experimental results show that LLMERE improves over existing methods. |
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations (2025.coling-main)
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| Challenge: | Existing evaluation methods rely on human judgment to assess data accuracy and visual communication, which is costly and unscalable. |
| Approach: | They propose a framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. |
| Outcome: | The proposed framework assesses data representation quality and communicative clarity of charts using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Making Large Language Models into World Models with Precondition and Effect Knowledge (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are not inherently designed to model real-world dynamics, but can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state and predicting the resulting world state upon action execution. |
| Approach: | They propose to use Large Language Models to model world states and preconditions . they validate that precondition and effect knowledge generated by LLMs aligns with human understanding of world dynamics . |
| Outcome: | The proposed model can predict valid actions and state transitions, thereby replicating existing models. |
DORA: Dynamic Optimization Prompt for Continuous Reflection of LLM-based Agent (2025.coling-main)
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| Challenge: | Existing studies have shown that reflection can enhance performance, but our investigation reveals an undesirable pattern in reflection framework: effective self-reflection occurs primarily at the beginning of iterations, with subsequent attempts failing to produce further improvements. |
| Approach: | They propose a framework that generates task-adaptive reflection advice using an external open-source small language model. |
| Outcome: | The proposed framework generates task-adaptive and diverse reflection advice in MiniWoB++ and Alfworld environments. |
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning (2025.coling-main)
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| Challenge: | Large language models generate convincing, fluent explanations, but they often generate inconsistent explanations on different inputs. |
| Approach: | They propose a method that adapts large language models to generate more consistent explanations on related examples. |
| Outcome: | The proposed method yields a 10.0% relative explanation consistency improvement across a variety of question-answering datasets and generalizes to 7 out-of-distribution datasets not seen during finetuning (+4.5%) |
Propulsion: Steering LLM with Tiny Fine-Tuning (2025.coling-main)
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| Challenge: | Propulsion is a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the model’s parameters. |
| Approach: | They propose a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the parameters. |
| Outcome: | The proposed method reduces parameter count from 355.3 million to 0.086 million while maintaining competitive performance across benchmarks. |
DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying (2025.coling-main)
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| Challenge: | Recent advances in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference. |
| Approach: | They propose a method that uses two prefixes to learn from different events and templates. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets . it can leverage possible connections between different events and capture relevant information from the prefix . |
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs (2025.coling-main)
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| Challenge: | Recent advances in Multimodal Large Language Models have led to a significant surge in the resource consumption of these models. |
| Approach: | They propose a method to reduce image tokens using visual query data by using CLIP metrics to reduce computational overhead and maintain consistent performance. |
| Outcome: | The proposed method has been extensively tested across 12 datasets and shows a significant reduction in computational overhead while maintaining a consistent level of performance. |
Leveraging Large Pre-trained Multilingual Models for High-Quality Speech-to-Text Translation on Industry Scenarios (2025.coling-main)
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| Challenge: | Speech-to-Text Translation systems rely on a sequential pipeline that combines ASR and MT models. |
| Approach: | They propose a parameter-efficient framework that integrates one LPSM with a multilingual MT engine. |
| Outcome: | The proposed framework integrates one LPSM with a multilingual MT engine. |
SA-DETR:Span Aware Detection Transformer for Moment Retrieval (2025.coling-main)
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| Challenge: | Moment Retrieval aims to locate video segments related to text. |
| Approach: | They propose a method that leverages the importance of instance related span anchors . they initialize span anchor using instance related fuse token and supervise them with GT labels . |
| Outcome: | The proposed method achieves competitive results on QVHighlights, Charades-STA and TACoS. |
Aligning LLMs with Individual Preferences via Interaction (2025.coling-main)
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| Challenge: | Existing studies on LLMs alignment focus on generalizing their behavior to generalized values such as helpfulness, harmlessness, and honesty. |
| Approach: | They train large language models to "interact to align" to implicitly infer user preferences . they use a multi-turn preference dataset to generate a personalized alignment . |
| Outcome: | The proposed method enables dynamic, personalized alignment via interaction with a multi-turn preference dataset. |
Automatic Evaluation of Language Generation Technology Based on Structure Alignment (2025.coling-main)
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| Challenge: | Existing methods for automatic evaluation ignore syntax of sentences despite its importance in determining meaning. |
| Approach: | They propose an automatic evaluation metric that considers both the words in sentences and their syntactic structures. |
| Outcome: | The proposed method is comparable to baselines from two NLP tasks. |
Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse (2025.coling-main)
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Jie Cao, Abhijit Suresh, Jennifer Jacobs, Charis Clevenger, Amanda Howard, Chelsea Brown, Brent Milne, Tom Fischaber, Tamara Sumner, James H. Martin
| Challenge: | a recent study focuses on analyzing tutoring discourse using talk moves . scaling the collection, annotation, and analysis of extensive tutoring dialogues is a challenge . |
| Approach: | They propose to analyze tutoring discourse using talk moves to develop machine learning models . they use a compact dataset to analyze dialogue context, speaker information and ablation data . |
| Outcome: | The proposed model improves performance in classrooms and in small groups . the proposed model is based on existing datasets and models designed for classroom teaching . |
How to Leverage Digit Embeddings to Represent Numbers? (2025.coling-main)
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| Challenge: | Existing numerical reasoning models struggle to understand numbers, despite simple generalisations. |
| Approach: | They propose to use mathematical priors to compute digit embeddings and explicitly incorporate them into transformer models by adding a special token to the input embedded digits or introducing an additional loss function to enhance correct predictions. |
| Outcome: | The proposed method is compatible with any pretrained model and easy to implement. |
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment (2025.coling-main)
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| Challenge: | Existing methods to address conversational search challenges are limited by one specific retrieval system. |
| Approach: | They propose a framework to enhance generalizability of information-seeking queries by aligning reformulation models with term-based and semantic retrieval systems. |
| Outcome: | The proposed framework outperforms existing methods in a more efficient framework. |
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)
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| Challenge: | Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits. |
| Approach: | They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text. |
| Outcome: | The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets. |
Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times (2025.coling-main)
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| Challenge: | Recent research has shown a strong fit between surprisal values from Transformers and reading times. |
| Approach: | They evaluate a Transformer model that uses a recency bias added to attention scores to improve the fit to human reading times. |
| Outcome: | The proposed model improves on a Transformer that includes a recency bias added to attention scores. |
ProsodyFlow: High-fidelity Text-to-Speech through Conditional Flow Matching and Prosody Modeling with Large Speech Language Models (2025.coling-main)
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| Challenge: | Text-to-speech (TTS) models have been developed to generate high-quality speech. |
| Approach: | They propose an end-to-end TTS model that integrates large self-supervised speech models and conditional flow matching to model prosodic features effectively. |
| Outcome: | The proposed model improves synthesis quality and efficiency compared to existing models, showing that it generates more prosodic and expressive speech synthesizing. |
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy (2025.coling-main)
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| Challenge: | Existing models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), which hinders the achievement of satisfactory performance. |
| Approach: | They propose a framework which fully leverages sentence-level information to improve OOE-NER performance by exploiting pre-trained language models' ability to understand target entity’s sentence context with a template set and refines sentence representation based on positive and negative templates. |
| Outcome: | The proposed framework outperforms state-of-the-art models on five datasets on named entity recognition (NER) tasks. |
Cross-lingual Evaluation of Multilingual Text Generation (2025.coling-main)
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| Challenge: | Existing methods for multilingual text generation are limited by language and data leakage. |
| Approach: | They propose an annotation-free cross-lingual evaluation protocol for multilingual text generation . they first generate English references from the translated non-English inputs into English . |
| Outcome: | The proposed protocol shows a high correlation to the reference-based ROUGE metric in four languages on news text summarization. |
Norm of Mean Contextualized Embeddings Determines their Variance (2025.coling-main)
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| Challenge: | Contextualized embeddings vary by context, even for the same token . a recent study shows a trade-off between the norm and the variance of the embedded word . |
| Approach: | They show that contextualized embeddings vary by context, even for the same token . they focus on the norm of the mean embeddment and the variance of the embeddables . |
| Outcome: | The proposed method is efficient and efficient for embeddings in sentences. |
Exploring the Impacts of Feature Fusion Strategy in Multi-modal Entity Alignment (2025.coling-main)
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| Challenge: | Existing approaches to merge multi-modal knowledge only use one fusion strategy . however, the impact of the fusion on individual entities could be ignored . |
| Approach: | They propose an adaptive multi-modal feature fusion strategy for entity alignment that selects the optimal entity-level feature blending strategy. |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance compared to models using the same modality on a dataset with multiple inconsistent images and styles. |
Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)
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| Challenge: | ice cream flavors and climate change are among the topics people hold on various topics. |
| Approach: | They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance. |
| Outcome: | The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation. |
How Likely Do LLMs with CoT Mimic Human Reasoning? (2025.coling-main)
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| Challenge: | Using chain-of-thought to elicit reasoning capabilities is not always effective and accurate. |
| Approach: | They compare the reasoning process of LLMs with humans to understand the causal chain . they find that LLM deviates from the ideal causal chain, resulting in spurious correlations . |
| Outcome: | The proposed method does not improve performance or accurately represent reasoning processes in LLMs. |
SGMEA: Structure-Guided Multimodal Entity Alignment (2025.coling-main)
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| Challenge: | Existing methods focus on interactions between neighboring entities in the structural modality while neglecting interactions between entities in visual and attribute modalities. |
| Approach: | They propose a structure-guided multimodal entity alignment method which prioritizes structural information from knowledge graphs to enhance the visual and attribute modalities. |
| Outcome: | The proposed method achieves state-of-the-art performance across multiple datasets, validating its effectiveness and superiority in practical applications. |
Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding. |
| Approach: | They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets. |
Incorporating Review-missing Interactions for Generative Explainable Recommendation (2025.coling-main)
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| Challenge: | Existing models of explainable recommendation use user reviews as ground truths, but in practice, a large amount of users may not leave reviews after purchasing items. |
| Approach: | They propose to incorporate user preferences into explainable recommender models by leveraging generative models to predict the missing reviews and then training the model based on all the predicted and original reviews. |
| Outcome: | The proposed model improves the explanation quality on three publicly available datasets. |
Transformer-based Speech Model Learns Well as Infants and Encodes Abstractions through Exemplars in the Poverty of the Stimulus Environment (2025.coling-main)
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| Challenge: | Existing theories of language learning for infants are inadequate, according to Chomsky . infants learn language in impoverished environments, according a new study . |
| Approach: | They designed a series of tasks, scenarios, and metrics to simulate the POS . they found that the emerging speech model wav2vec2.0 can learn well in noisy Mandarin environments. |
| Outcome: | The proposed model can learn in noisy and sparse Mandarin environments. |
Hire Me or Not? Examining Language Model’s Behavior with Occupation Attributes (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been widely integrated into production pipelines due to their impressive performance across multiple tasks. |
| Approach: | They construct a dataset using a standard occupation classification knowledge base and tested it on three families of LLMs. |
| Outcome: | The proposed framework analyzes LLMs’ behavior with respect to gender stereotypes in the context of occupation decision making. |
Enhancing Factual Consistency in Text Summarization via Counterfactual Debiasing (2025.coling-main)
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| Challenge: | Abstractive text summarization has produced fluent and informative outputs, but factual inconsistency is a challenge. |
| Approach: | They propose a framework that mitigates the causal effects of language bias and irrelevancy bias by counterfactual estimation. |
| Outcome: | The proposed framework outperforms baseline methods on two widely used summarization datasets. |
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks focus primarily on pure graph understanding, lacking a comprehensive evaluation across all graph types and detailed capability definitions. |
| Approach: | They propose a benchmark to evaluate LLMs' graph comprehension and reasoning abilities using a three-tier hierarchical taxonomy and a granular taxonomies. |
| Outcome: | The proposed model includes 11 datasets with 5,140 graphs of varying complexity. |
Exploring Content Predictability in Turn-Taking Through Different Computer-Mediated Communications (2025.coling-main)
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Wanqing He, Calen C. MacDonald, Yejoon Yoo, Marcos Eizayaga, Ryun Shim, Lev D. Katreczko, Susan R. Fussell
| Challenge: | Existing studies on face-to-face (f2f) communication have suggested that speakers rely heavily on a variety of multi-modal cues to make real-time predictions about upcoming words. |
| Approach: | They assessed how loss of multi-modal cues would affect word predictability in turn-taking by watching videos, listening to audio, or reading a transcript of f2f conversations. |
| Outcome: | The results confirm the key role of prediction in language processing and conversation smoothness, especially in computer-mediated conversation (CMC). |
VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks . |
| Approach: | They propose a low-resource multilingual large language model that expands the vocabulary for multilingual support. |
| Outcome: | The proposed model outperforms existing models in multilingual instruction-following tasks, but lags behind English-centric models in some tasks. |
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation (2025.coling-main)
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| Challenge: | Using large language models to generate code has shown significant promise, but selecting effective examples to improve generation quality remains a challenging task. |
| Approach: | They propose a framework that utilizes algorithmic plans to identify and retrieve effective examples. |
| Outcome: | The proposed framework outperforms the state-of-the-art RAG methods in code generation even when the source and target languages match or differ. |
Multilingual and Explainable Text Detoxification with Parallel Corpora (2025.coling-main)
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Daryna Dementieva, Nikolay Babakov, Amit Ronen, Abinew Ali Ayele, Naquee Rizwan, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Daniil Alekhseevich Moskovskiy, Elisei Stakovskii, Eran Kaufman, Ashraf Elnagar, Animesh Mukherjee, Alexander Panchenko
| Challenge: | Existing approaches to manage toxic speech on social platforms are limited . however, there is a need for more proactive moderation of abusive speech. |
| Approach: | They extend parallel text detoxification corpus to new languages to test the approach . they propose a method that combines toxic and non-toxic sentences into a more neutral form . |
| Outcome: | The proposed method integrates the descriptive features of toxic and non-toxic sentences into a more neutral or non- toxic form. |
Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in various NLP tasks, including semantic parsing, which translates natural language into formal code representations. |
| Approach: | They propose a semantic captioning task to repurpose semantic parsing datasets for semantic captions. |
| Outcome: | The proposed model outperforms random selection and other methods by 39% on BLEU score. |
Factual Knowledge Assessment of Language Models Using Distractors (2025.coling-main)
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Hichem Ammar Khodja, Abderrahmane Ait gueni ssaid, Frederic Bechet, Quentin Brabant, Alexis Nasr, Gwénolé Lecorvé
| Challenge: | Language models encode extensive factual knowledge within their parameters. |
| Approach: | They propose a new interpretable knowledge assessment method that leverages distractors to provide incorrect alternatives to the correct answer. |
| Outcome: | The proposed method shows that it is aligned with human judgment and stronger robustness to verbalization artifacts. |
Paraphrase Generation Evaluation Powered by an LLM: A Semantic Metric, Not a Lexical One (2025.coling-main)
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| Challenge: | Existing measures for automatic paraphrase generation are based on lexical distances or semantic embedding alignments. |
| Approach: | They propose a measure based on a log likelihood ratio from an LLM to assess the quality of a potential paraphrase. |
| Outcome: | The proposed measure is better for sorting pairs of sentences by semantic proximity and provides an interpretable classification threshold between paraphrases and non-paraphrases. |
Summarization of Opinionated Political Documents with Varied Perspectives (2025.coling-main)
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| Challenge: | Political ideologies can lead people to develop misperceptions of groups with opposing opinions, such as the 2024 US presidential election, French legislative election, or the Brexit referendum. |
| Approach: | They propose a dataset and task for independently summarizing political perspectives in a set of opinionated news articles. |
| Outcome: | The proposed dataset and task evaluates models of varying sizes and architectures on a set of opinionated news articles. |
Measuring Contextual Informativeness in Child-Directed Text (2025.coling-main)
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Maria R. Valentini, Téa Y. Wright, Ali Marashian, Jennifer M. Ellis, Eliana Colunga, Katharina von der Wense
| Challenge: | Recent advances in natural language processing (NLP) have made it possible to generate children's stories with a single word. |
| Approach: | They propose a task of measuring contextual informativeness in children's stories and a large language model to automate the task. |
| Outcome: | The proposed method outperforms baselines and can generalize to measuring contextual informativeness in adult-directed text. |
Can Large Language Models Differentiate Harmful from Argumentative Essays? Steps Toward Ethical Essay Scoring (2025.coling-main)
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| Challenge: | Existing automated essay scoring systems overlook ethical and moral aspects of content, erroneously assigning high scores to essays that propagate harmful opinions. |
| Approach: | They introduce a Harmful Essay Detection benchmark to test the effectiveness of various Large Language Models (LLMs) they find that current AES systems overlook ethically and morally problematic elements in essays . |
| Outcome: | The proposed benchmark compared LLMs and AES models to identify and score harmful essays. |
Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions (2025.coling-main)
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Rumana Ferdous Munne, Noriki Nishida, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Automated biomedical annotation presents significant challenges when entities are not explicitly mentioned in the text. |
| Approach: | They propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. |
| Outcome: | The proposed method performs well when processes are not explicitly mentioned . it is time-consuming and expensive to annotate biomedical texts with a specific ontology . |
Mitigating Shortcut Learning via Smart Data Augmentation based on Large Language Model (2025.coling-main)
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| Challenge: | Existing methods to improve shortcut learning performance are limited by manual definition of shortcuts and inherent confirmation bias during model training. |
| Approach: | They propose a method of Smart Data Augmentation based on Large Language Models to identify shortcuts and generate their anti-shortcut counterparts. |
| Outcome: | The proposed method shows an improvement of 5.61% across various natural language processing tasks. |
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning (2025.coling-main)
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Raymond Li, Yuxi Feng, Zhenan Fan, Giuseppe Carenini, Weiwei Zhang, Mohammadreza Pourreza, Yong Zhang
| Challenge: | In-context Learning (ICL) has proven to be effective in a variety of complex tasks, but the selection of the most beneficial demonstration examples remains an open research problem. |
| Approach: | They propose a demonstration retrieval framework that learns a weighted combination of LLM hidden states where rich semantic information is encoded. |
| Outcome: | Experiments on two popular NL2SQL benchmarks show that the proposed method outperforms state-of-the-art models. |
Improving NMT Models by Retrofitting Quality Estimators into Trainable Energy Loss (2025.coling-main)
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| Challenge: | Reinforcement learning has shown great promise in aligning language models with human preferences in a variety of text generation tasks, including machine translation. |
| Approach: | They propose a method that employs quality estimators as trainable loss networks to backpropagate to the NMT model. |
| Outcome: | The proposed method outperforms strong baselines and proximal policy optimizations on English-to-Mongolian translation. |
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning (2025.coling-main)
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Yifan Du, Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Jinpeng Wang, Chuyuan Wang, Mingchen Cai, Ruihua Song, Ji-Rong Wen
| Challenge: | Experimental results show that visual instruction tuning improves performance of Multi-modal Large Language Models (MLLMs) to extend the application scope of Large Language Modells, a surge of work augments LLMs with vision encoders to endow the ability of multi-modal cognition and reasoning. |
| Approach: | They propose a systematic approach to create high-quality visual reasoning instructions using a synthesize-complicate-reformulate paradigm. |
| Outcome: | The proposed method improves performance of MLLMs by 27.86% and 27.60% on MME-Perception and MME Cognition. |
TriFine: A Large-Scale Dataset of Vision-Audio-Subtitle for Tri-Modal Machine Translation and Benchmark with Fine-Grained Annotated Tags (2025.coling-main)
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| Challenge: | Existing video-guided machine translation approaches use coarse-grained visual information, resulting in information redundancy and high computational overhead. |
| Approach: | They propose a fine-grained approach to video-guided machine translation using visual information . they use a large-scale dataset with annotated multimodal fine-grain tags . |
| Outcome: | The proposed approach achieves superior performance with lower computational overhead compared to coarse-grained methods and text-only models. |
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025.coling-main)
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| Challenge: | Existing evaluation approaches to evaluate Large Language Models are affected by potential biases within LLMs. |
| Approach: | They propose two many-shot In-Context Learning (ICL) prompt templates to help LLM evaluators mitigate potential biases. |
| Outcome: | The proposed templates reduce biases by using in-context examples with model-generated rationales as references. |
GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary (2025.coling-main)
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| Challenge: | Effective RD methods have applications in accessibility, translation or writing support systems. |
| Approach: | They propose a simple approach to RD that leverages LLMs and embedding models to obtain the most relevant word or set of words given a textual description or definition. |
| Outcome: | The proposed approach outperforms baselines in well studied RD datasets while showing less overfitting. |
Momentum Posterior Regularization for Multi-hop Dense Retrieval (2025.coling-main)
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| Challenge: | Current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA . posterior information is often defined as the response, which may not connect to the query without intermediate retrieval . |
| Approach: | They propose to distill knowledge from a posterior retrieval into a prior retrieval for multi-hop QA . they propose to use momentum moving average method to update posterior information along with prior retrievals . |
| Outcome: | Experiments on HotpotQA and StrategyQA show that MoPo outperforms baselines in retrieval and downstream QA tasks. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (2025.coling-main)
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| Challenge: | Existing research still faces spurious query-anchor matching due to unobserved factors. |
| Approach: | They propose a model that uses the front-door criteria to decompose the expansion process into a parser module and a connector to isolate confounding effects. |
| Outcome: | Extensive experiments on three benchmarks validate the effectiveness of the proposed model. |
Inside-Outside Algorithm for Probabilistic Product-Free Lambek Categorial Grammar (2025.coling-main)
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| Challenge: | Many studies have discovered hidden syntactic structures within language models without the guidance of explicit rules. |
| Approach: | They propose an inside-outside algorithm for Probabilistic Lambek Categorical Grammar. |
| Outcome: | The proposed algorithm is used in the estimation of probabilistic context-free grammars. |
Perceive the Passage of Time: A Systematic Evaluation of Large Language Model in Temporal Relativity (2025.coling-main)
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| Challenge: | Temporal perception is crucial for Large Language Models to understand the world. |
| Approach: | They propose a temporal-relative ability benchmark to evaluate LLMs' temporal perception . they conduct extensive experiments on popular LLM GPT-4 scenarios . |
| Outcome: | The proposed benchmarks show a significant performance gap between LLMs and humans in temporal-relative capability. |
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)
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| Challenge: | a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process. |
| Approach: | They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions. |
| Outcome: | The proposed method improves performance across language generation tasks significantly. |
PToco: Prefix-based Token-level Collaboration Enhances Reasoning for Multi-LLMs (2025.coling-main)
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| Challenge: | Existing approaches to collaboration between multiple Large Language Models (LLMs) rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer. |
| Approach: | They propose a mechanism that enables collaboration among less capable LLMs independent of tokenizer differences. |
| Outcome: | The proposed mechanism improves performance over individual models and generalizes well across different quantities and sizes of participating models. |
MAGRET: Machine-generated Text Detection with Rewritten Texts (2025.coling-main)
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| Challenge: | Existing studies focus on detecting machine-generated text in open-source models, but their performance on closed-source large models is limited. |
| Approach: | They propose a method to detect rewritten text from large language models using a BERT encoder and propose to refine it to achieve semantic alignment. |
| Outcome: | The proposed method outperforms baseline methods on three text-generated datasets. |
Structured List-Grounded Question Answering (2025.coling-main)
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| Challenge: | Document-grounded dialogue systems aim to answer user queries by leveraging external information. |
| Approach: | They propose a dataset to evaluate QA systems' ability to interpret and use structured lists . they use language models and model-based filtering processes to enhance data quality . |
| Outcome: | The proposed model outperforms baselines on the LIST2QA dataset . it shows that the proposed model is more accurate and complete than baselines . |
Low-Resource Language Expansion and Translation Capacity Enhancement for LLM: A Study on the Uyghur (2025.coling-main)
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Kaiwen Lu, Yating Yang, Fengyi Yang, Rui Dong, Bo Ma, Aihetamujiang Aihemaiti, Abibilla Atawulla, Lei Wang, Xi Zhou
| Challenge: | Extensive experiments have shown that our strategy effectively expands the low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
| Approach: | They propose a direct preference optimization based on translation self-evolution to expand low-resource languages into large language models by using Uyghur as an example. |
| Outcome: | The proposed strategy expands low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
Unraveling the Mystery: Defending Against Jailbreak Attacks Via Unearthing Real Intention (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly vulnerable to elusive and implicit intentions, causing security risks and compromising user experience. |
| Approach: | They propose a method to detect and mitigate implicit jailbreak attacks using LLMs by unearthing real intentions and a greedy gradient-based algorithm to remove the least important parts of a sentence. |
| Outcome: | The proposed method reduces attacks success rate and Harmful Score while maintaining overall model performance. |
A Flash in the Pan: Better Prompting Strategies to Deploy Out-of-the-Box LLMs as Conversational Recommendation Systems (2025.coling-main)
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Gustavo Adolpho Lucas de Carvalho, Simon Ben Igeri, Jennifer Healey, Victor Bursztyn, David Demeter, Lawrence A. Birnbaum
| Challenge: | Recent studies have shown that using conversation history can improve question generation and product recommendation in naturalistic, multi-round conversational recommendation settings. |
| Approach: | They propose a method to generate better questions to elicit human preferences and to make recommendations using the information gained through these questions. |
| Outcome: | The proposed method beats state-of-the-art benchmarks on two datasets and shows that it is more accurate when users answer more questions than prior methods. |
Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process. |
| Approach: | They propose a framework that employs learned rules to guide the generation of logical forms. |
| Outcome: | The proposed method achieves competitive results on standard KBQA datasets. |
Mitigating Language Confusion through Inference-time Intervention (2025.coling-main)
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| Challenge: | Existing methods to address the problem of language confusion are incontext learning and supervised fine-tuning (SFT) however, they consume context window space and require extensive data collection. |
| Approach: | They propose a language-sensitive intervention that detects and assesses language confusion without additional complex mechanisms. |
| Outcome: | The proposed method detects language confusion and assesses content quality without additional complex mechanisms. |
Detecting deepfakes and false ads through analysis of text and social engineering techniques (2025.coling-main)
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| Challenge: | Existing deepfake detection algorithms focus on technical analysis of video and audio . authors examine stylistic inconsistencies and manipulative language patterns . |
| Approach: | They propose a method that emphasizes the analysis of text-based transcripts . they examine stylistic inconsistencies and manipulative language patterns . |
| Outcome: | The proposed method improves the accuracy of distinguishing between fake and real materials. |
Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech Resources (2025.coling-main)
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| Challenge: | Currently, no unified information on speakers and computational tools are available for these languages. |
| Approach: | They present a systematization of the indigenous languages spoken in Argentina, along with national demographic data on the country’s Indigenous population. |
| Outcome: | The proposed systematization of the indigenous languages spoken in Argentina, along with national demographic data on the country’s Indigenous population, is based on the Argentine population. |
The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction (2025.coling-main)
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| Challenge: | low-level tasks are used to extract character networks from literary texts, but no study has been conducted on their impact on performance. |
| Approach: | They focus on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks. |
| Outcome: | The proposed methods outperform traditional pipelines in terms of recall and recall. |
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede’s Cultural Dimensions (2025.coling-main)
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| Challenge: | Large language models (LLMs) are deployed in many countries, but they fail to account for cultural variances among their potential users. |
| Approach: | They propose to use Hofstede’s cultural dimension framework to quantify cultural alignment using latent variable analysis to evaluate large language models against cultural dimensions of regions like the United States, China, and Arab countries. |
| Outcome: | The proposed model is compared against LLMs in the United States, China, and Arab countries and demonstrates that all models struggle to grasp cultural values, while GPT-4 shows a unique capability to adapt to cultural nuances, particularly in Chinese settings. |
META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models (2025.coling-main)
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| Challenge: | Supervised fine-tuning (SFT) is widely adopted for tailoring large language models (LLMs) to specific downstream tasks. |
| Approach: | They propose a memory-efficient method for automatic sample reweighting that learns to re-weight fine-tuning samples by minimizing the loss on a small, high-quality validation set. |
| Outcome: | Meta-LoRA learns to reweight fine-tuning samples by minimizing the loss on a small, high-quality validation set through an end-to-end bi-level optimization framework based on meta-learning. |
Can Large Language Models perform Relation-based Argument Mining? (2025.coling-main)
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| Challenge: | Existing methods for RbAM fail to perform satisfactorily across different datasets. |
| Approach: | They propose to use relation-based argument mining to determine agreement (support) and disagreement (attack) relations amongst textual arguments in binary and ternary settings. |
| Outcome: | The proposed method outperforms the best performing (RoBERTa-based) baseline on two open-source LLMs and with GPT-3.5-turbo on several datasets for (binary and ternary) RbAM. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |
CmEAA: Cross-modal Enhancement and Alignment Adapter for Radiology Report Generation (2025.coling-main)
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| Challenge: | Existing methods for automatic radiology report generation suffer from data bias. |
| Approach: | They propose a method that connects a vision encoder with a frozen large language model by using a cross-modal enhancement and alignment adapter. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on IU X-Ray and MIMIC-CXR datasets. |
Semantic Reshuffling with LLM and Heterogeneous Graph Auto-Encoder for Enhanced Rumor Detection (2025.coling-main)
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| Challenge: | Current methods struggle against complex propagation influenced by bots, coordinated accounts, and echo chambers, which fragment information and increase risks of misjudgments. |
| Approach: | They propose a framework that integrates metapath-based rumor reconstruction and narrative reordering to detect rumors. |
| Outcome: | The proposed model outperforms existing methods and is highly accurate and robust. |
Extracting, Detecting, and Generating Research Questions for Scientific Articles (2025.coling-main)
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Sina Taslimi, Artemis Capari, Hosein Azarbonyad, Zi Long Zhu, Zubair Afzal, Evangelos Kanoulas, George Tsatsaronis
| Challenge: | Existing tools to generate and extract RQs from scientific articles lack a definition of RQ in articles. |
| Approach: | They propose to use a set of regular expressions to identify articles with well-defined RQs and a detection component to identify more complex RQ's in articles. |
| Outcome: | The proposed pipeline can detect and generate RQs from scientific articles and generate high-quality ones. |
Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning (2025.coling-main)
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| Challenge: | Current methods for insider threat detection suffer from low precision and information loss . a novel approach to detect insider threats is needed to improve accuracy . |
| Approach: | They propose a precise anomaly detection solution based on Large Language Model (LLM) fine-tuning . they represent user behavior in natural language and implement a threat tracing mechanism . |
| Outcome: | The proposed solution achieves an F1 score of 0.8941 on the CERT v6.2 dataset . |
Flashback: Memory Mechanism for Enhancing Memory Efficiency and Speed in Deep Sequential Models (2025.coling-main)
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| Challenge: | Existing deep sequential processing models have problems with memory degradation and inaccurate gradient backpropagation. |
| Approach: | They propose a Flashback property that preserves memory as an identity mapping until it is overwritten by a hidden state at a different time step. |
| Outcome: | The proposed model can be implemented in Transformers and Mamba, and it performs well. |
Engagement-driven Persona Prompting for Rewriting News Tweets (2025.coling-main)
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| Challenge: | Text style transfer is a challenging research task which modifies the linguistic style of a text to meet pre-set objectives such as making the text simpler or more accessible. |
| Approach: | They propose to use large language models to rewrite Dutch news tweets to match specific linguistic styles to achieve a more accessible and accessible text. |
| Outcome: | The proposed prompting strategies perform best for rewriting Dutch news tweets in specific linguistic styles (formal, casual and factual). |
A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)
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| Challenge: | Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs. |
| Approach: | They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation . |
| Outcome: | The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets. |
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection (2025.coling-main)
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| Challenge: | Existing datasets and models fail to address the complexities of multilingual data, authors say . detection of radical content on online platforms has become an increasingly pressing concern . |
| Approach: | They propose a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
| Outcome: | The proposed dataset is annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic (2025.coling-main)
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| Challenge: | Existing benchmarks for large language models (LLMs) in Arabic are lacking . despite progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks . |
| Approach: | They propose to use Arabic as a language to assess trustworthiness of large language models. |
| Outcome: | The proposed benchmark measures the trustworthiness of large language models in Arabic. |
Comparative Study of Multilingual Idioms and Similes in Large Language Models (2025.coling-main)
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Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh
| Challenge: | figurative language is one of the most challenging aspects of human language for LLMs to comprehend . |
| Approach: | They evaluate LLMs using two multilingual datasets on simile and idiom interpretation and two new evaluation sets for Persian . they find prompt engineering methods are generally effective, but their success varies by figurative type, language, and model. |
| Outcome: | The proposed models perform better in simile and idiom interpretations across languages and figurative types. |
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)
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Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao
| Challenge: | Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks. |
| Approach: | They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. |
| Outcome: | The proposed framework eliminates the need for user alignment between platforms. |
Multi-Modal Entities Matter: Benchmarking Multi-Modal Entity Alignment (2025.coling-main)
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| Challenge: | Existing MMEA datasets consider multi-modal data as attributes of textual entities, neglecting correlations between the multi-modal data. |
| Approach: | They propose a multi-modal entity alignment dataset that models multi-dimensional data as textual entities in the MMKG. |
| Outcome: | The proposed dataset can learn the structural information of entities by considering both intra-modal and cross-modal relations and infer the similarity of different types of entity pairs. |
Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)
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| Challenge: | Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance. |
| Approach: | They propose a memory network with logical inference for extractive QA in multiparty dialogues. |
| Outcome: | The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
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| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
| Approach: | They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context. |
| Outcome: | The proposed model improves parser performance in a social media context without pre-identified discourse units. |
PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models (2025.coling-main)
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| Challenge: | prevailing notion of paraphrase is simplistic, offering only limited view of vast spectrum of paraphrasing phenomena. |
| Approach: | They propose a benchmarking tool for paraphrase detection that provides a fine-grained evaluation lens. |
| Outcome: | The proposed benchmark enables rapid calibration of models to specific strictness levels. |
Dynamic-prototype Contrastive Fine-tuning for Continual Few-shot Relation Extraction with Unseen Relation Detection (2025.coling-main)
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| Challenge: | Existing approaches to learn relations from labeled data overlook task interference in continual learning and memory requirements for different relations. |
| Approach: | They propose a framework to learn new relations from limited labeled data while preserving knowledge about previously learned relations. |
| Outcome: | The proposed framework is more practical and comprehensive for real-world scenarios. |
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration (2025.coling-main)
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| Challenge: | Rhetorical figures are used to convey subtle, implicit meanings or to emphasize statements. |
| Approach: | They propose a web application that facilitates the identification and annotation of German rhetorical figures. |
| Outcome: | The proposed application improves the user experience with Retrieval Augmented Generation (RAG). |
Quantifying the Influence of Evaluation Aspects on Long-Form Response Assessment (2025.coling-main)
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| Challenge: | Evaluating the outputs of large language models (LLMs) on long-form generative tasks remains challenging. |
| Approach: | They propose to compute an overall quality score as a weighted average of factuality, informative-ness, and formality as compared to previous metrics. |
| Outcome: | The proposed method achieves stronger correlations with human judgments compared to previous metrics. |
CharMoral: A Character Morality Dataset for Morally Dynamic Character Analysis in Long-Form Narratives (2025.coling-main)
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| Challenge: | Existing studies on character analysis focus on character identification, social network analysis, and the exploration of characters' personas or personalities. |
| Approach: | They propose a four-stage framework to automatically classify actions as moral or immoral based on context. |
| Outcome: | The proposed framework is effective in moral reasoning tasks in multiple genres. |
Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)
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| Challenge: | Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input. |
| Approach: | They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer . |
| Outcome: | The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input . |
Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation. |
| Approach: | They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data. |
| Outcome: | The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data. |
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning (2025.coling-main)
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| Challenge: | Low-rank adaptation and its variants have been popular due to their ability to avoid excessive inference costs. |
| Approach: | They propose a low-rank adaptation method that enables high-rank updates with low costs while leveraging semantic and linguistic information inherent in pre-trained weight. |
| Outcome: | The proposed method outperforms LoRA and other fine-tuning methods across tasks with less trainable parameters. |
Learn from Failure: Causality-guided Contrastive Learning for Generalizable Implicit Hate Speech Detection (2025.coling-main)
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| Challenge: | Existing methods for detecting implicit hate speech rely on correlations between class labels and spurious attributes, which leads to poor performance on data lacking correlations. |
| Approach: | They propose a causality-guided contrastive learning approach to enhance the generalizability of implicit hate speech detection by aligning the representations of samples with the same class but opposite spurious attributes. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods in cross-domain generalization on multiple implicit hate speech datasets. |
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)
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| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
| Approach: | They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets. |
| Outcome: | The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks. |
Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)
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| Challenge: | Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations. |
| Approach: | They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks. |
| Outcome: | The proposed task outperforms representative models across all datasets. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
Automatic Mathematic In-Context Example Generation for LLM Using Multi-Modal Consistency (2025.coling-main)
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| Challenge: | Existing methods for in-context learning require annotated datasets, resulting in higher computational costs and lower quality examples. |
| Approach: | They propose a framework that automatically generates high-quality in-context examples to enhance LLMs’ mathematical reasoning. |
| Outcome: | Evaluated on four math problem datasets, the proposed framework outperforms baseline methods with LLM accuracy ranging from 87.0% to 99.3%. |
From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation (2025.coling-main)
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| Challenge: | Existing approaches focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation. |
| Approach: | They propose a multimodal dialogue system that generates empathetic responses from a perspective that considers the personality traits of users. |
| Outcome: | The proposed system generates empathetic responses from a multimodal perspective and analyzes multimodal data to understand the user’s emotional state and situation. |
Integrating Visual Modalities with Large Language Models for Mental Health Support (2025.coling-main)
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| Challenge: | Existing work of mental health support primarily utilizes unimodal textual data and fails to understand and respond to users’ emotional states comprehensively. |
| Approach: | They propose a framework that integrates multimodal inputs and counseling strategies to enhance the performance of Large Language Models (LLMs) This approach allows LLMs to generate more nuanced and supportive responses. |
| Outcome: | The proposed framework outperforms existing models and delivers more empathetic, coherent, and contextually relevant mental health support responses. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
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| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
Selected Languages are All You Need for Cross-lingual Truthfulness Transfer (2025.coling-main)
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| Challenge: | Existing methods for truthfulness enhancement in English are limited to multilingual scenarios. |
| Approach: | They propose a method for cross-lingual truthfulness transfer that uses language bias and transfer contributions to select an optimal subset of all tested languages and employ translation instruction tuning for cross language truthfulness transfers. |
| Outcome: | The proposed method reduces multilingual representation disparity and boosts cross-lingual truthfulness transfer of LLMs. |
OVEL: Online Video Entity Linking (2025.coling-main)
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| Challenge: | Existing studies on Multi-modal Entity Linking focus on linking textual and visual mentions or offline videos’ mentions to entities in multi-modal knowledge bases. |
| Approach: | They propose a task called Online Video Entity Linking to establish connections between online videos and a knowledge base with high accuracy and timeliness. |
| Outcome: | The proposed method can establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness. |
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models (2025.coling-main)
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Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat, James Garforth, Bjorn Ross, Arno Onken, Atoosa Kasirzadeh, Alexandra Birch
| Challenge: | Existing literature on the ethical aspects of large language models (LLMs) is lacking a single practical guide on the subject. |
| Approach: | They propose to translate ethics literature into concrete recommendations for computer scientists by presenting an open and living resource for NLP practitioners and those tasked with evaluating the ethical implications of others’ work. |
| Outcome: | The proposed guide is an open and living resource for NLP practitioners and those tasked with evaluating the ethical implications of others’ work. |
Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025.coling-main)
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| Challenge: | Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, which is crucial for knowledge completion, fusion, and inference. |
| Approach: | They propose to embed entities and relations into a low-dimensional space to enable knowledge Graphs to be effectively used by downstream AI tasks. |
| Outcome: | The proposed framework is universal and flexible, suitable for various KGE models. |
Chinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion (2025.coling-main)
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| Challenge: | Existing methods for classification of reading difficulty of texts are insufficiently trained and lack of linguistic features. |
| Approach: | They propose a method that combines adaptive pre-training with feature fusion to capture different text difficulties and an interactive attention mechanism to integrate linguistic and deep features. |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on Chinese textbook dataset and can be applied to other languages. |
Multitask-Bench: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis. |
| Approach: | They propose to fine-tune LLMs on benign (non-harmful) data to ensure safe outputs. |
| Outcome: | The proposed model reduces attack success rates across a range of tasks without compromising its usefulness. |
Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweets (2025.coling-main)
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| Challenge: | generative AI has been used to generate fluent and convincing text on social media platforms . a new study examines the generative capabilities of four popular large language models . |
| Approach: | They propose a methodology to examine the generative capabilities of four prominent LLMs on Twitter using a dataset from Llama 3, Mistral, Qwen2 and GPT4o. |
| Outcome: | The proposed method examines the generative capabilities of four prominent LLMs on Twitter. |
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations. |
| Approach: | They propose a commonsense reasoning framework for sarcasm detection based on commonsensense augmentation to supplement commonsence knowledge and infer the incongruity. |
| Outcome: | The proposed framework is able to detect sarcasm in five datasets and is robust to complex scenarios. |
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning (2025.coling-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. |
| Approach: | They propose a new reasoning strategy Solution Guidance (SG) and a plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) which focuses on problem understanding and decomposition at the semantic and logical levels, rather than specific computations. |
| Outcome: | The proposed reasoning strategy Solution Guidance (SG) and plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) improves the reasoning capabilities of small language models on various reasoning tasks. |
LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025.coling-main)
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| Challenge: | Existing approaches to multi-hop question answering emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information. |
| Approach: | They propose a Local-tO-Global optimized retrieval method to discover more beneficial information and improve tuplet objective loss. |
| Outcome: | The proposed method outperforms state-of-the-art models and significantly improves multi-hop reasoning. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Impromptu Cybercrime Euphemism Detection (2025.coling-main)
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| Challenge: | Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals . |
| Approach: | They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token. |
| Outcome: | The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector. |
ALIS: Aligned LLM Instruction Security Strategy for Unsafe Input Prompt (2025.coling-main)
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| Challenge: | Existing instruction tuning methods may fail to balance performance with robustness against attacks from user input like prompt injection and jailbreaking. |
| Approach: | They propose an instruction tuning paradigm to decompose user inputs into irreducible atomic instructions and organize them into instruction streams to guide response generation of model. |
| Outcome: | The proposed model can maintain security constraints by ignoring or rejecting user mode instructions when user mode instruction conflicts with kernel mode instructions. |
ProTOD: Proactive Task-oriented Dialogue System Based on Large Language Model (2025.coling-main)
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| Challenge: | Existing task-oriented dialogue systems engage with users in a reactive manner, relying on a basic single-query mechanism and employing passive policy planning. |
| Approach: | They propose a novel LLM-based proactive TOD framework to improve system proactivity and goal completion. |
| Outcome: | The proposed framework improves system proactivity and goal completion rates by 10% while enhancing proactive engagement. |
Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025.coling-main)
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| Challenge: | Visual question answering (VQA) is a multi-modal translation challenge that requires the analysis of both images and questions simultaneously to generate appropriate responses. |
| Approach: | They propose a textless multilingual visual question answering dataset that incorporates speech-based questions in English, german, spanish and french. |
| Outcome: | The proposed framework is superior to existing frameworks for speech-based VQA . the proposed framework can generate better results for image, text and audio representations . |
Detecting Conversational Mental Manipulation with Intent-Aware Prompting (2025.coling-main)
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| Challenge: | Existing approaches to detect mental manipulations are limited due to complexity of detecting subtle, covert tactics in conversations. |
| Approach: | They propose an approach to detect mental manipulations using large language models using intent-aware prompting by capturing the intents of participants. |
| Outcome: | The proposed approach significantly reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. |
MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have advanced in many fields, but focus on English-centric models requires extensive data. |
| Approach: | They propose a method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddables to target languages. |
| Outcome: | The proposed method outperforms baseline and existing cross-lingual transfer methods in target languages. |
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
| Approach: | They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules. |
| Outcome: | The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech. |
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs (2025.coling-main)
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Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Sunayana Sitaram, Kalika Bali, Tanuja Ganu, Akshay Nambi
| Challenge: | Large language models (LLMs) excel in diverse applications but still struggle with non-Latin scripts and low-resource languages. |
| Approach: | They propose a dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. |
| Outcome: | The proposed approach achieves 10-15% improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models. |
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models (2025.coling-main)
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Sofia Jamil, Bollampalli Areen Reddy, Raghvendra Kumar, Sriparna Saha, Joseph K. J, Koustava Goswami
| Challenge: | Poems are a distinct form of literature, with meanings that transcend beyond the literal words. |
| Approach: | They propose a framework to generate images that visually represent the meanings of poems using prompt tuning and a PoeKey algorithm to extract emotions, visual elements, and themes from poems. |
| Outcome: | The proposed framework generates images that visually represent the meanings of poems and their images. |
Argumentation and Domain Discourse in Scholarly Articles on the Theory of International Relations (2025.coling-main)
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Magdalena Wolska, Sassan Gholiagha, Mitja Sienknecht, Dora Kiesel, Irene Lopez Garcia, Patrick Riehmann, Matti Wiegmann, Bernd Froehlich, Katrin Girgensohn, Jürgen Neyer, Benno Stein
| Challenge: | SKILL project aims to provide students with AI tools to facilitate analysis of argumentation in scholarly articles on international relations. |
| Approach: | They propose to use AI to analyze argumentation in scholarly articles on international relations . they use a dataset, discourse analysis, and baseline experiments to examine argumentation and domain content types . |
| Outcome: | The proposed method enables educationally-relevant insight into scholarly IR discourse . it requires domain-specific training and fine-tuning on relation and content type prediction tasks. |
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
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| Challenge: | Existing methods to model event associations struggle with semantic ambiguity and embedding bias. |
| Approach: | They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model . |
| Outcome: | The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism. |
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation (2025.coling-main)
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| Challenge: | KEEM is a dynamically generated dataset designed to enhance memory updates in long-term conversational systems. |
| Approach: | They propose a dataset that keeps emotional and essential memories and generates integrative memories that incorporate emotional context and causal relationships. |
| Outcome: | The Keep Emotional and Essential Memory (KEEM) dataset enhances memory updates in long-term conversational systems. |
medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs (2025.coling-main)
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| Challenge: | Electronic Medical Records (EMRs) are the digitized record of a patient's medical and health information and are integral to modern healthcare. |
| Approach: | They propose a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. |
| Outcome: | The proposed framework assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. |
AIDER: a Robust and Topic-Independent Framework for Detecting AI-Generated Text (2025.coling-main)
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| Challenge: | Current fine-tuned detectors lack robustness against adversarial attacks and struggle with out-of-distribution topics, limiting their practical applicability. |
| Approach: | They propose a topic-independent framework for detecting AI-generated text . it leverages the ALBERT model for topic content disentanglement, enhancing transferability to unseen topics. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in detecting human-written and AI-generated content under adversarial and topic-varied conditions. |
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)
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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin
| Challenge: | Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. |
| Approach: | They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning. |
| Outcome: | The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets. |
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models (2025.coling-main)
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| Challenge: | Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability. |
| Approach: | They propose a black-box evaluation approach and a new dataset, Abstain-QA, to rigorously assess AA across varied question types, domains, and task types. |
| Outcome: | The proposed evaluation process and new dataset, Abstain-QA, are crafted to rigorously assess AA across varied question types, domains, and task types. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |
InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)
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| Challenge: | InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Approach: | They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Outcome: | The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws. |
Let’s Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are composed of neurons that exhibit diverse behaviors and roles. |
| Approach: | They propose a novel approach that refines the granularity of parameter training down to the individual neuron, enabling a more parameter-efficient fine-tuning model. |
| Outcome: | The proposed approach exceeds the performance of full-parameter fine-tuning and PEFT and provides insights into the analysis of neurons. |
Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)
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| Challenge: | Existing approaches to detect fake news in unseen domains are limited by domain-specific training. |
| Approach: | They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations . |
| Outcome: | The proposed method can detect fake news in unseen domains with the help of pre-trained language models. |
Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings (2025.coling-main)
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| Challenge: | Recent results suggest that positional encodings are not necessary when training decoder-only Transformer language models. |
| Approach: | They propose a causal attention mechanism that allows Transformers to store positional information without positional encodings. |
| Outcome: | The proposed model can reconstruct the positions of tokens without positional encodings. |
RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation (2025.coling-main)
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| Challenge: | Existing methods for prompting Large Language Models (LLMs) are lacking in advanced reasoning skills. |
| Approach: | They propose a method that generates and utilizes contextually reconstructed sentences to generate few-shot exemplars. |
| Outcome: | The proposed method significantly improves the performance of large language models in vertical and lateral thinking tasks, surpassing traditional exemplar selection strategies across a variety of few-shot settings. |
Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments (2025.coling-main)
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Iker De la Iglesia, Iakes Goenaga, Johanna Ramirez-Romero, Jose Maria Villa-Gonzalez, Josu Goikoetxea, Ander Barrena
| Challenge: | Evaluating LLM-generated text has become a key challenge in domain-specific contexts like the medical field. |
| Approach: | They propose a method to evaluate LLM-generated medical explanatory arguments using Proxy Tasks and rankings to align results with human evaluation criteria. |
| Outcome: | The proposed evaluation method is robust against adversarial attacks, including the assessment of non-argumentative text. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
Improved Sparse Upcycling for Instruction Tuning (2025.coling-main)
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| Challenge: | Existing methods for sparse upcycling lead to performance degradation in instruction tuning scenarios. |
| Approach: | They propose a representation-based approach to convert dense language models into sparsely activated ones by initializing router weights from language models. |
| Outcome: | The proposed architecture improves model capabilities and routing consistency across multiple benchmarks. |
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)
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Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
| Challenge: | Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios. |
| Approach: | They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage. |
| Outcome: | The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method. |
ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction? (2025.coling-main)
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| Challenge: | Existing methods for moral foundation prediction are limited due to lack of annotated data. |
| Approach: | They propose a framework for fine-tuning a pre-trained language model to the task of moral foundation prediction. |
| Outcome: | The proposed framework outperforms state-of-the-art methods for moral foundation prediction with an average increase of 35% in the out-of domain scenario. |
SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (2025.coling-main)
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| Challenge: | Existing work on cyberbullying detection in Chinese is underdeveloped due to the lack of comprehensive and reliable datasets. |
| Approach: | They propose to use Chinese social media sessions to analyze Chinese cyberbullying content to improve the quality of annotations. |
| Outcome: | The proposed dataset shows that it performs better than existing methods on Weibo and a major social media platform. |
Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training (2025.coling-main)
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| Challenge: | State-of-the-art approaches to this task resort to supervised training and labelling, masking or training. |
| Approach: | They propose an approach that does without any task-specific supervision and offers thus a better potential for improvement. |
| Outcome: | The proposed approach achieves very competitive performance and scales up in a way that requires no task-specific supervision. |
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary? (2025.coling-main)
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Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro Ozaki, Maria Angelica Riera Machin, Hongyu Sun, Justin Vasselli, Taro Watanabe
| Challenge: | Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words . a recent study shows that frequency from YouTube subtitles is comparable to and often better than the best available resources. |
| Approach: | They use YouTube subtitles to construct frequency norms for five languages . they find they are comparable to and often better than the best currently available resources . |
| Outcome: | The proposed method improves on the best currently available resources for Chinese, English, Indonesian, Japanese, and Spanish. |
RealSafe: Quantifying Safety Risks of Language Agents in Real-World (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have attracted much attention in Artificial Intelligence. |
| Approach: | They propose a framework to rigorously assess the safety and reliability of large language model (LLM) agents in real application scenarios. |
| Outcome: | The framework evaluates the safety and reliability of large language model (LLM) agents in 14 different application scenarios utilizing three contexts - standard operations, ambiguous interactions, and malicious behaviors. |
Voice synthesis in Polish and English - analyzing prediction differences in speaker verification systems (2025.coling-main)
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| Challenge: | Using audio deepfakes, we can create high quality false voice recordings convincing enough to deceive human ears and pose security concerns. |
| Approach: | They examine the effects of deepfakes on speaker recognition systems across English and Polish corpora, evaluating both Text-to-Speech and Voice Conversion methods. |
| Outcome: | The proposed methods can maintain personal traits, posing risks of unauthorized access, and can be used to deceive human ears. |
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment (2025.coling-main)
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Umair Nawaz, Awais Muhammad, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan, Rao Anwer
| Challenge: | Recent studies have addressed this problem by building domain-specialized image-text data. |
| Approach: | They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features . |
| Outcome: | The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks. |
RUIE: Retrieval-based Unified Information Extraction using Large Language Model (2025.coling-main)
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| Challenge: | Unified information extraction (UIE) aims to extract diverse structured information from unstructured text using a single model or framework. |
| Approach: | They propose a framework that leverages in-context learning for efficient task generalization by combining LLM preferences with a keyword-enhanced reward model. |
| Outcome: | The proposed framework performs better on eight held-out datasets than existing methods and instruction-tuning methods. |
It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language (2025.coling-main)
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| Challenge: | Figure-based language is used to convey opinions, ideas, or emotions in texts and dialogues. |
| Approach: | They evaluate the capabilities of Large Language Models to address TER and generate textual explanations of TER predictions. |
| Outcome: | The proposed model outperforms the open-source models in Zero- and Few-Shot Learning settings and shows significant performance improvements. |
MuKA: Multimodal Knowledge Augmented Visual Information-Seeking (2025.coling-main)
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| Challenge: | Existing methods for visual information-seeking tasks rely on textual knowledge . existing methods can impair information retrieval and confuse MLLMs . |
| Approach: | They propose a framework which leverages a multimodal knowledge base to address these limitations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the InfoSeek and E-VQA benchmarks. |
MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)
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| Challenge: | Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures. |
| Approach: | They propose to use graph kernel search to discover subgraphs from the entire graph to bridge the graph and LLMs, helping with graph retrieval and LRM generation. |
| Outcome: | The proposed method achieves state-of-the-art on two graph-based tasks and the results are published in the journal Nature. |
MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection (2025.coling-main)
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| Challenge: | Existing knowledge triples are ineffective in medical question-answering because of superfluous data and inability to capture complex relationships between symptoms and treatments. |
| Approach: | They propose a first-order logical reasoning model that uses First-Order Logic to model intricate relationships between diseases and treatments. |
| Outcome: | The proposed model captures the interplay of symptoms, diseases, and treatments, enhancing context comprehension. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning (2025.coling-main)
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| Challenge: | Existing methods focus on semantic similarity between queries and candidate exemplars, while logical connections between reasoning steps can be beneficial to depict problem-solving process. |
| Approach: | They propose a method to retrieve exemplars with semantic and structural similarity using a graph kernel. |
| Outcome: | The proposed method is superior to state-of-the-art retrieval-based approaches on mathematics and logical reasoning tasks. |
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use, Planning (Including RAG), and Feedback Learning (2025.coling-main)
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| Challenge: | Large Language Models have been used for planning, tool use, and feedback learning . inconsistent taxonomy and complexity of workflows create challenges . |
| Approach: | They propose a unified taxonomy to review and discuss the three paradigms . they define environments/tasks, common LLM-profiled roles and universally applicable workflows based on prior work . |
| Outcome: | The proposed taxonomy compares LMPR implementations and workflow usage across paradigms . large language models have human-like reasoning capabilities, the authors say . |
Analyzing Offensive Language Dataset Insights from Training Dynamics and Human Agreement Level (2025.coling-main)
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| Challenge: | Existing models struggle to generalize themselves when applied to implicit cases, especially in out-of-domain scenarios. |
| Approach: | They propose a data refinement strategy that integrates human annotation agreement with model training dynamics to enhance model performance and generalization. |
| Outcome: | The proposed approach improves performance across multiple datasets and models while retaining ambiguous cases crucial for out-of-domain generalization. |
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL (2025.coling-main)
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| Challenge: | Existing text-to-SQL approaches have overlooked the critical aspect of system robustness. |
| Approach: | They propose a robust text-to-SQL solution that integrates with LLMs . their method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% . |
| Outcome: | The proposed solution achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks. |
Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)
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| Challenge: | Existing video LLMs excel at capturing the overall description of a video but lack the ability to demonstrate an understanding of temporal dynamics and localized content within the video. |
| Approach: | They propose a Time-Perception Enhanced Video Grounding via Boundary Perception and Temporal Reasoning to improve LLMs' understanding of video temporality. |
| Outcome: | The proposed method improves on three datasets: ActivityNet, Charades, and DiDeMo (up to 11.2% improvement on R@0.3). |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks on nested tool learning are lacking relevant data instances. |
| Approach: | They propose a method to construct large-scale nested tool calls with different nesting structures using a large-quality dataset. |
| Outcome: | The proposed method can be used to evaluate the nested tool learning abilities of large language models (LLMs) in real-world applications. |
A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection (2025.coling-main)
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| Challenge: | Recent approaches to detecting music entities have been limited due to ambiguity in user-generated content. |
| Approach: | They propose to use user-generated metadata to benchmark and test models for entity detection . they find that large language models outperform SLMs in a variety of downstream tasks . |
| Outcome: | The proposed model outperforms existing models in a variety of downstream tasks . the proposed model is more robust in the ICL setting than the existing models . |
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (2025.coling-main)
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| Challenge: | a new benchmark evaluates video-based optical character recognition (Video OCR) performance of multi-modal models in videos . the benchmark aims to improve video LLMs' ability to extract text from video content . previous benchmarks have focused on video QA, but not video-related QA. |
| Approach: | They propose to evaluate the video OCR performance of multi-modal models in videos . they use a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement . |
| Outcome: | The proposed benchmark includes 1,028 videos and 2,961 question-answer pairs . it integrates the OCR ability of image LLMs with manual refinement . |
Disentangle to Decay: Linear Attention with Trainable Decay Factor (2025.coling-main)
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| Challenge: | Existing linear attention models use a decay factor based positional encoding (PE), but the decay factor is manually designed and non-trainable, limiting further optimization. |
| Approach: | They propose a PE-based positional encoding that disentangles decay factor into two parts to achieve further optimization and stable training. |
| Outcome: | The proposed model achieves stable training of decay factor and improves inference efficiency in normal context and extrapolation scenarios. |
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification (2025.coling-main)
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| Challenge: | Existing models with black-box nature obscure decision-making process and lack interpretability. |
| Approach: | They propose a multi-head graph attention-based prototypical network that uses a vector and prototypes to learn an interpretable prototypical representation. |
| Outcome: | The proposed model achieves superior results without sacrificing the accuracy of the original black-box LMs. |
Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)
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| Challenge: | Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions. |
| Approach: | They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. |
| Outcome: | The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection. |
An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025.coling-main)
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| Challenge: | Existing methods for tabular prediction rely on extensive pre-training or fine-tuning of LLMs . a retrieval-based approach eliminates the need for training any modules or performing data augmentation . |
| Approach: | They propose a retrieval-based approach that utilizes the powerful capabilities of large language models in representation, comprehension, and inference. |
| Outcome: | The proposed method exhibits strong predictive performance on tabular prediction task, affirming its practicality and effectiveness. |
AIGT: AI Generative Table Based on Prompt (2025.coling-main)
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| Challenge: | Tabular data is an essential resource for many fields, but current methods do not fully utilize the rich information available in tables. |
| Approach: | They propose a method that utilizes metadata information to generate tabular data . they propose long-token partitioning algorithms that enable AIGT to model tables of any scale . |
| Outcome: | The proposed approach achieves state-of-the-art on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system. |
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models (2025.coling-main)
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Kazuki Hayashi, Kazuma Onishi, Toma Suzuki, Yusuke Ide, Seiji Gobara, Shigeki Saito, Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
| Challenge: | Large-scale vision language models excel at generating factual content, but their ability to rank images from multiple perspectives has not been explored. |
| Approach: | They propose a framework to evaluate large-scale vision-language models by measuring their ability to rank image texts from multiple perspectives. |
| Outcome: | The proposed evaluation framework measures how closely LVLMs' judgments align with human interpretations. |
Development of Numerical Error Detection Tasks to Analyze the Numerical Capabilities of Language Models (2025.coling-main)
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| Challenge: | Existing language models are difficult to detect numerical errors because of their finite set of tokens. |
| Approach: | They use a benchmark dataset to classify numerical errors using automatically generated numerical errors and investigate their ability to detect errors. |
| Outcome: | The proposed model performs well in the numerical error detection task, but not as accurate as humans. |
Searching for Structure: Investigating Emergent Communication with Large Language Models (2025.coling-main)
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| Challenge: | Human languages have evolved to be structured through repeated language learning and use. |
| Approach: | They propose to use large language models to optimise for implicit biases that shape languages to improve communicative efficiency. |
| Outcome: | The proposed models can be used to study language evolution and open possibilities for human-machine interactions. |
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation (2025.coling-main)
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| Challenge: | Generative large language models generate a high-dimensional probability distribution over all tokens in their vocabulary. |
| Approach: | They conduct extensive sensitivity analyses to determine how hyperparameter choices shape the outputs of generative large language models. |
| Outcome: | The proposed methods influence the distribution of diversity and coherence metrics in human-written text, but the optimal configurations vary across models and tasks. |
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages (2025.coling-main)
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| Challenge: | Existing multilingual models such as XLM-R support only approximately 100-200 languages, leaving nearly 7,000 low-resource languages untapped. |
| Approach: | They construct and open-source a dataset of four-language corpora obtained through machine translation into Chinese, Uyghur and Tibetan. |
| Outcome: | The proposed dataset includes two resource-rich languages and two low-resource languages. |
How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty (2025.coling-main)
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| Challenge: | Language models have made significant progress on complex reasoning tasks, but it is impossible to obtain perfect rationales from models or even from humans. |
| Approach: | They propose a way to guide models to choose between two different reasoning paths depending on the ambiguity of rationales. |
| Outcome: | The proposed approach leads to robust performance in adversarial scenarios where rationale quality is inconsistent. |
Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)
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| Challenge: | Recent advances in neural language models have sparked a new surge of intelligent agent research. |
| Approach: | They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. |
| Outcome: | The proposed method achieves an average success rate of 42.68% on two constraint-intensive benchmarks, TravelPlanner and API-Bank. |
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework (2025.coling-main)
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| Challenge: | Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations. |
| Approach: | They introduce a Chinese nursing dataset and implement incremental pre-training and supervised fine-tuning techniques to enhance LLM performance in specialized tasks. |
| Outcome: | The proposed model performs better in real-time patient monitoring and interaction tasks than previous models. |
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation (2025.coling-main)
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| Challenge: | Large multimodal models struggle with text-rich images because of inadequate training data. |
| Approach: | They propose to use annotations from human annotators to generate instruction data by a hybrid approach to generate text prompts for large language models. |
| Outcome: | The proposed model improves multimodal alignment for text-rich images by using human annotations and tailored text prompts for large language models. |
Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (2025.coling-main)
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| Challenge: | Knowledge bases (KBs) in low-resource languages are often incomplete, restricting the ability to do zero-shot question answering using multilingual language models. |
| Approach: | They propose a novel cross-lingual mapping technique which improves word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment. |
| Outcome: | The proposed approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access. |
FarExStance: Explainable Stance Detection for Farsi (2025.coling-main)
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Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi, Mohammad Taher Pilehvar, Jennifer Foster
| Challenge: | FarExStance is a new dataset for explainable stance detection in Farsi . it contains extractive explanations as evidence for stance labels and claims . |
| Approach: | They propose a dataset for explainable stance detection in Farsi with extractive explanations as evidence. |
| Outcome: | The proposed model is the most accurate on stance detection, while the best explanation is from few-shot Claude-3.5-Sonnet. |
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability (2025.coling-main)
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| Challenge: | a new study explores how large language models capture aspects of human linguis-tic ability . large language model performance is limited by the mechanisms behind their performance . |
| Approach: | They employ psycholinguistic paradigms to explore neuron-level representations in language models . they found that large language models exhibit human-like abilities in three tasks . |
| Outcome: | The proposed model shows human-like abilities in sound-shape association, gender association and implicit causality. |
Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages (2025.coling-main)
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| Challenge: | lexical gaps between dialects in cross-lingual information retrieval (CLIR) are caused by orthographic variations and different regional expressions. |
| Approach: | They propose a dataset that consists of seven German dialects extracted from Wikipedia. |
| Outcome: | The proposed dataset consists of seven German dialects extracted from Wikipedia. |
MoKA:Parameter Efficiency Fine-Tuning via Mixture of Kronecker Product Adaption (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is one of the most popular PEFT methods . low-rank update mechanism of LoRA somewhat limits its ability to approximate full-parameter fine-tuning during training process. |
| Approach: | They propose a parameter-efficient fine-tuning framework that combines Kronecker product with the Mixture-of-Experts method to achieve parameter efficiency and better model performance. |
| Outcome: | The proposed framework outperforms existing methods on the GLUE benchmark and instruction tuning tasks for large language models. |
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts. |
| Approach: | They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios. |
| Outcome: | The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner. |
Can LLMs Help Create Grammar?: Automating Grammar Creation for Endangered Languages with In-Context Learning (2025.coling-main)
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| Challenge: | a new study examines the potential of large language models for documenting endangered languages . the model can be used to generate grammatical information for low-resource languages despite limitations . |
| Approach: | They examine the efficacy of LLMs in generating grammatical information for low-resource languages . they use bilingual dictionaries and parallel sentences of the unknown language as a case study . |
| Outcome: | The proposed model produces coherent grammatical rules and lexical entries using bilingual dictionaries and parallel sentences of the unknown language without building the model from scratch. |
Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition (2025.coling-main)
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| Challenge: | Theory of Mind (ToM) is the ability to attribute and infer the mental states of others. |
| Approach: | They propose an LLM-based inference algorithm that improves model performance on complex ToM tasks by simulating user perspectives. |
| Outcome: | The proposed algorithm improves model performance on complex ToM tasks while requiring minimal prompt tuning across tasks and no additional model training. |
Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts (2025.coling-main)
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| Challenge: | a recent rise in polarization has led to a rise in the use of loud and extreme voices in public spaces. |
| Approach: | They propose ways to parse and convey information from small-group recorded conversations . they show that LLMs can provide socially relevant context to improve comprehension . |
| Outcome: | The proposed models improve comprehension, readability, and empathy in small-group conversations . the proposed models struggle with capturing key social aspects, the authors show . |
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)
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| Challenge: | Existing methods for detecting LLM-generated text require no training data. |
| Approach: | They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts. |
| Outcome: | The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets. |
VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)
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Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao
| Challenge: | Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data. |
| Approach: | They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts . |
| Outcome: | The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR. |
Self-Evolution Knowledge Distillation for LLM-based Machine Translation (2025.coling-main)
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| Challenge: | Existing knowledge distillation strategies for large language models minimize output distributions between student and teacher models indiscriminately for each token. |
| Approach: | They propose a distillation strategy that integrates teacher and one-hot distribution of ground truth into the student distribution as prior knowledge, which promotes the distillation process. |
| Outcome: | The proposed method brings an average improvement of approximately 1.4 SacreBLEU points across four translation directions in the WMT22 test sets. |
On Weaponization-Resistant Large Language Models with Prospect Theoretic Alignment (2025.coling-main)
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| Challenge: | Existing safeguards for large language models are inadequate for open-weight models as minimal fine-tuning can bypass them. |
| Approach: | They propose a framework that prioritizes maximizing generative utility rather than a singular optimization metric and integrates prospect theory into LLM training to strengthen LLMs against misuse and weaponization. |
| Outcome: | The proposed framework strengthens LLMs against misuse and weaponization while maintaining high performance even after extensive fine-tuning. |
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks (2025.coling-main)
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| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
| Approach: | They examine the alignment between LLM evaluators and human annotators by comparing conventional and alignment tasks with different evaluation criteria. |
| Outcome: | The proposed models excel in general criteria, such as fluency, but face challenges with complex criteria, including numerical reasoning. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Evaluating Transformers for OCR Post-Correction in Early Modern Dutch Theatre (2025.coling-main)
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| Challenge: | a new study examines the effectiveness of two types of transformer models for OCR post-correction in early modern Dutch plays. |
| Approach: | They propose to use large generative models and sequence-to-sequence models for OCR post-correction in early modern Dutch plays. |
| Outcome: | The proposed model outperforms generative models on the OCR post-correction task . the model outpersforms the model with the lowest error rate on the historical English dataset . |
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types. |
| Approach: | They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. |
| Outcome: | The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures. |
In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)
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| Challenge: | Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries. |
| Approach: | They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference. |
| Outcome: | The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation. |
ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)
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| Challenge: | Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability. |
| Approach: | They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark. |
| Outcome: | The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy. |
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models (2025.coling-main)
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| Challenge: | Visual Language Models (VLMs) have been gaining popularity with large language models, but few attempts have been made to incorporate efficient linear Recurrent Neural Networks (RNNs) into VLMs. |
| Approach: | They propose a linear RNN model with a data-dependent recurrence and sandwich prompts to enhance modeling capabilities and a 2D image scanning mechanism to enrich the processing of visual sequences. |
| Outcome: | The proposed model achieves competitive performance compared to Transformer-based models on various benchmarks. |
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)
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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang
| Challenge: | Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well. |
| Approach: | They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet. |
| Outcome: | The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future. |
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests (2025.coling-main)
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| Challenge: | Existing task-oriented dialogue benchmarks lack sufficient examples of complex discourse phenomena such as indirectness. |
| Approach: | They propose a set of linguistic criteria and an LLM-based pipeline for generating realistic IURs to test natural language understanding and dialogue state tracking models before deployment in a new domain. |
| Outcome: | The proposed model can handle indirect user requests (IURs) but lacks examples of complex discourse phenomena such as indirectness. |
Consistency Rating of Semantic Transparency: an Evaluation Method for Metaphor Competence in Idiom Understanding Tasks (2025.coling-main)
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| Challenge: | Idioms condense complex semantics into fixed phrases, making idiom comprehension a test of metaphor competence. |
| Approach: | They propose a method to evaluate the metaphor competence of LLMs for the idiom understanding task: the Consistency Rating of Semantic Transparency (CR-ST). |
| Outcome: | The proposed method assesses the difficulty of understanding idioms through two dimensions: overall semantic transparency and constituent semantic transparency, aiming to gauge LLMs’ mastery of metaphor competence. |
KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions (2025.coling-main)
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| Challenge: | Existing benchmarks that assess this vulnerability rely on manual construction, resulting in limited size and lack of expandability. |
| Approach: | They propose a method to generate false premise questions based on knowledge graphs . they modify true triplets extracted from KGs to create false premises . |
| Outcome: | The proposed method generates semantically rich FPQs using state-of-the-art GPTs. |
IberoBench: A Benchmark for LLM Evaluation in Iberian Languages (2025.coling-main)
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Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, Silvia Paniagua Suárez, Naiara Perez, Anna Salles, Susana Sotelo Docio, Júlia Falcão, Jose Javier Saiz, Robiert Sepulveda Torres, Jeremy Barnes, Pablo Gamallo, Aitor Gonzalez-Agirre, German Rigau, Marta Villegas
| Challenge: | Existing multi-task benchmarks for Large Language Models are limited to English . a new benchmark is needed to evaluate models on a range of tasks . |
| Approach: | They propose a multilingual, multi-task benchmark for Iberian languages built on the LM Evaluation Harness framework. |
| Outcome: | The proposed benchmark covers 62 tasks divided into 179 subtasks and is available in Iberian, Basque, Catalan, Galician, European Spanish and European Portuguese. |
Efficient Architectures for High Resolution Vision-Language Models (2025.coling-main)
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| Challenge: | Recent advances in vision-Language Models (VLMs) have limited accuracy of fine details within high resolution images, which limits performance in multiple tasks. |
| Approach: | They propose a new architecture that efficiently processes high-resolution images while training fewer parameters than similarly sized VLMs. |
| Outcome: | The proposed architecture achieves high efficiency while maintaining strong performance in tasks that require fine-grained image understanding and/or handling of scene-text. |
NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)
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| Challenge: | Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability. |
| Approach: | They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema. |
| Outcome: | The proposed task is more efficient than previous methods. |
Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)
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| Challenge: | Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. |
| Approach: | They propose a dialog context fusion mechanism for end-to-end task-oriented dialog augmented with three additional tasks: dialog summarization, domain prediction, and slot detection. |
| Outcome: | The proposed method achieves state-of-the-art on the MultiWOZ and CrossWOZ datasets. |
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs (2025.coling-main)
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| Challenge: | Existing solutions to alleviate hallucination have considered utilizing LLMs’ inherent reasoning abilities to alleviating hallucinism, such as self-correction and diverse sampling methods. |
| Approach: | They propose a counterfactual multi-agent debate framework that predetermines LLMs' stances to override their inherent biases for answer inspection. |
| Outcome: | Extensive experiments on four datasets of three tasks demonstrate the superiority of the proposed framework over existing methods. |
Extracting the Essence and Discarding the Dross: Enhancing Code Generation with Contrastive Execution Feedback (2025.coling-main)
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| Challenge: | erroneous code generation methods amalgamate feedback and correct code as target sentences . a new approach to code generation with feedback is needed to improve model performance . |
| Approach: | They propose a learning-based code generation model with execution feedback that integrates feedback and correct code as target sentences. |
| Outcome: | a new model with execution feedback shows improvements in generating accurate code and understanding error correction. |
From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls (2025.coling-main)
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| Challenge: | Existing studies have focused on the generation and evaluation of analytical reports derived from Earnings Calls (ECs). |
| Approach: | They propose to use Large Language Models to generate and evaluate analytical reports derived from Earnings Calls (ECs) they propose to introduce specialized agents that introduce diverse viewpoints and desirable topics into the report generation process. |
| Outcome: | The proposed model improves the quality of reports in different settings, while human-written reports remain preferred in the majority of cases. |
Leveraging LLM-Generated Schema Descriptions for Unanswerable Question Detection in Clinical Data (2025.coling-main)
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| Challenge: | Existing methods rely on model uncertainty but lack interpretability and data imbalance. |
| Approach: | They propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. |
| Outcome: | The proposed model improves interpretability and improves accuracy in binary classification tasks. |
Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Recent studies suggest that large language models can transfer skills learned in one language to others, but internal mechanisms behind this ability remain unclear. |
| Approach: | They find that LLMs map semantically identical inputs from different languages into a common semantic latent space that allows for consistent processing across languages. |
| Outcome: | The findings highlight the structural evolution of multilingual models during training and scaling up. |
Understanding Token Probability Encoding in Output Embeddings (2025.coling-main)
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| Challenge: | a common log-linear encoding of output token probabilities is used in language models, but it is sparse and inaccurate. |
| Approach: | They propose an approximate log-linear encoding of output token probabilities within the output embedding vectors and show that it is accurate and sparse. |
| Outcome: | The proposed output embeddings capture the corpus token frequency information in early steps, even before an obvious convergence of parameters starts. |
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |
Evaluating the Consistency of LLM Evaluators (2025.coling-main)
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| Challenge: | Large language models (LLMs) have shown potential as general evaluators with the benefits of speed and cost. |
| Approach: | They conduct extensive studies on the two aspects of consistency in LLM evaluations, Self-Consistency (SC) and Inter-scale Consistency on different scoring scales and criterion granularity with open-source and proprietary models. |
| Outcome: | The results show that strong proprietary models are not necessarily consistent evaluators, highlighting the importance of considering consistency in assessing the capability of LLM evalueators. |
MDPO: Customized Direct Preference Optimization with a Metric-based Sampler for Question and Answer Generation (2025.coling-main)
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| Challenge: | Existing methods for QA data generation are limited by the dependence of existing evaluation metrics on ground truth labels. |
| Approach: | They propose a set of unsupervised evaluation metrics for QA data that enable multidimensional assessment based on the relationships among context,question and answer. |
| Outcome: | The proposed method outperforms state-of-the-art methods on public datasets and shows that it produces high-quality and domain-specific QA pairs. |
A Collaborative Reasoning Framework Powered by Reinforcement Learning and Large Language Models for Complex Questions Answering over Knowledge Graph (2025.coling-main)
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| Challenge: | Knowledge Graph Question Answering (KGQA) aims to answer natural language questions by reasoning across multiple triples in knowledge graphs. |
| Approach: | They propose a collaborative reasoning framework powered by RL and LLMs to answer complex questions based on the knowledge graph. |
| Outcome: | The proposed model surpasses state-of-the-art models on four datasets. |
Scalability of Bayesian Network Structure Elicitation with Large Language Models: a Novel Methodology and Comparative Analysis (2025.coling-main)
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| Challenge: | Existing methods for BN structure learning are limited by the size of the BN. |
| Approach: | They propose a method for Bayesian Networks (BNs) structure elicitation that initializes several LLMs with different experiences and queries them to create a structure. |
| Outcome: | The proposed method performs better than the existing method with one of the three studied LLMs, but the performance decreases with the increase in BN size. |
An LLM-based Framework for Biomedical Terminology Normalization in Social Media via Multi-Agent Collaboration (2025.coling-main)
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| Challenge: | Experimental results indicate that our approach exhibits competitive performance. |
| Approach: | They propose a tuning-free approach to normalize non-standard terms using large language models . they use a search engine and a domain knowledge base to expand the short texts into accurate descriptions . |
| Outcome: | The proposed approach is based on the "Recall and Re-rank" framework . it can be used to identify the standard term in a specified termbase for non-standardized mentions . |
Driving Chinese Spelling Correction from a Fine-Grained Perspective (2025.coling-main)
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| Challenge: | Existing evaluations for Chinese spelling correction lack nuanced typology for spelling errors, creating an "invisible" bottleneck . |
| Approach: | They propose a fine-grained evaluation principle for Chinese spelling correction (CSC) they categorize spelling errors into six different types and use it to evaluate models . |
| Outcome: | The proposed evaluation principle can be leveraged to enhance CSC training models. |
LAiW: A Chinese Legal Large Language Models Benchmark (2025.coling-main)
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Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia, Qianqian Xie, Yifang Zhang, Weiguang Han, Wei Tian, Hao Wang
| Challenge: | Xie et al., 2023) show that large language models (LLMs) can generate legal text, but lack the legal syllogism . legal experts are cautious about their practical application due to the opaque nature of the LLMs. |
| Approach: | They propose a Chinese legal LLM benchmark structured around the legal syllogism . they evaluate LLMs across three levels of capability, each reflecting a more complex stage of legal . |
| Outcome: | The proposed benchmark identifies that LLMs lack the legal syllogism, which hinders trust and understanding from legal experts. |
Retrieval-Augmented Generation for Large Language Model based Few-shot Chinese Spell Checking (2025.coling-main)
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| Challenge: | Existing LLM-based Chinese spelling check methods rely on fixed prompt samples . existing methods are limited by technical bottlenecks, complex recognition environments, and individual differences . |
| Approach: | They propose a framework called RagID to provide well-chosen prompt samples . they propose to use semantic-based similarity search and iterative discriminator mechanism . |
| Outcome: | The proposed framework can provide well-chosen prompt samples and reduce overcorrection issues in Chinese spelling check tasks. |
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (2025.coling-main)
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| Challenge: | Existing models generate text on demand, but in real-life situations, individuals do not continuously generate text or voice opinions. |
| Approach: | They propose a novel task to identify news-triggered opinion expressing timing by using a dataset generated by professional stock analysts. |
| Outcome: | The proposed model can generate opinion on stock analysts' actions and improves performance in various opinion understanding tasks. |
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT). |
| Approach: | They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions. |
| Outcome: | The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks. |
Interpreting Topic Models in Byte-Pair Encoding Space (2025.coling-main)
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| Challenge: | Byte-pair encoding (BPE) is a popular method of tokenizing valid words onto a token space V b with a predetermined fixed size, and handling out-of-vocabulary words, breaking words into smaller tokens. |
| Approach: | They propose to interpret the recovery of valid words from these tokens as a ranking problem and apply existing evaluation measures to topic sets. |
| Outcome: | The proposed model interprets the recovery of valid words from these tokens as a ranking problem and applies existing evaluation measures. |
SUMIE: A Synthetic Benchmark for Incremental Entity Summarization (2025.coling-main)
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| Challenge: | Existing datasets that test incrementally update entity summaries are lacking. |
| Approach: | They propose a fully synthetic dataset that exposes real-world IES challenges by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy. |
| Outcome: | The proposed dataset shows that state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. |
Text-Attributed Graph Learning with Coupled Augmentations (2025.coling-main)
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| Challenge: | Existing models focus on either the text attribute or the graph structure, neglecting the other aspect. |
| Approach: | They propose a model that combines the strengths of both text-learning and graph-learning models in parallel. |
| Outcome: | The proposed model outperforms existing models on diverse datasets. |
From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)
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| Challenge: | Document Image Translation (DIT) aims to translate documents in images from one language to another. |
| Approach: | They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization. |
| Outcome: | The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality. |
MESAQA: A Dataset for Multi-Span Contextual and Evidence-Grounded Question Answering (2025.coling-main)
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| Challenge: | Existing question answering systems focus on extracting answers from single spans, but real-world scenarios require synthesizing information from multiple spans. |
| Approach: | They propose a dataset that leverages the MASH-QA dataset and large language models (LLMs) to ensure that each Q/A pair requires considering all selected spans. |
| Outcome: | The proposed method enables the model to answer multiple Q/A pairs in a single span, while ensuring that all selected spans are considered. |
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)
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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities. |
| Approach: | They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy. |
| Outcome: | The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages. |
Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization (2025.coling-main)
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| Challenge: | Existing methods for erasure of memorized text fail to unlearn large numbers of memorizable samples without jeopardizing model utility. |
| Approach: | They propose a method that allows LLMs to memorize and recite some training sequences verbatim . they propose an entropy-based loss method that is shown to be more stable . |
| Outcome: | The proposed method improves model utility and accuracy while preserving model ability in language generation and understanding. |
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach (2025.coling-main)
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| Challenge: | Existing DS-NER approaches rely on large validation sets and test set for tuning inappropriately. |
| Approach: | They propose a method where training data is annotated using domain dictionaries and test data is analyzed by domain experts. |
| Outcome: | The proposed method reduces the need for labor-intensive manual annotations but rely on large human labeled validation set. |
BinarySelect to Improve Accessibility of Black-Box Attack Research (2025.coling-main)
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| Challenge: | Adversarial text attacks require a high level of knowledge of the models they target. |
| Approach: | They propose a more efficient selection method which combines binary search and attack selection methods to greatly reduce the number of queries needed to find a token. |
| Outcome: | The proposed method reduces the number of queries needed to find the first token compared to n queries on the Yelp dataset with a drop in attack effectiveness of only 5 points. |
Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations (2025.coling-main)
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| Challenge: | Existing data on ESL speakers' communication and interaction skills are lacking in the evaluation of the sophisticated features of dialogue. |
| Approach: | They propose an evaluation framework for interactive dialogue assessment in ESL speakers. |
| Outcome: | The proposed framework provides a means to assess ESL communication, useful for language assessment. |
Imposter: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion Models (2025.coling-main)
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| Challenge: | ImPoster is a novel algorithm for generating a target image of a ‘source’ subject performing a 'driving' action. |
| Approach: | They propose an unsupervised approach that generates a target image of a ‘source’ subject performing a driving action from a single pair of inputs along with the text descriptions of the two images. |
| Outcome: | The proposed algorithm is completely unsupervised and does not require access to additional annotations like keypoints or pose. |
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)
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| Challenge: | naive prompts can enhance the task performance of large language models, but they are resource-intensive. |
| Approach: | They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models. |
| Outcome: | The proposed method is based on a large-scale dataset and performed fairly across multiple models. |
Context Filtering with Reward Modeling in Question Answering (2025.coling-main)
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| Challenge: | Question Answering (QA) tasks require a mix of relevant and irrelevant information in these contexts to perform well. |
| Approach: | They propose a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. |
| Outcome: | The proposed approach outperforms baseline models in 6.8-folds. |
Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)
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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. |
| Approach: | They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training. |
| Outcome: | The proposed task improves distribution case-to-code induction and various coding generation tasks. |
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable language generation capabilities, propelling advancements in various understanding/generation tasks, including opendomain question answering (QA). |
| Approach: | They propose a chain-of- Discussion framework to leverage synergy among multiple open-source Large Language Models (LLMs) aiming to provide more correct and more comprehensive answers for open-ended QA, although they are not strong enough individually. |
| Outcome: | The proposed framework leverages the synergy among multiple open-source Large Language Models (LLMs) to provide more correct and comprehensive answers for open-ended QA, although they are not strong enough individually. |
RAIDEN Benchmark: Evaluating Role-playing Conversational Agents with Measurement-Driven Custom Dialogues (2025.coling-main)
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| Challenge: | Existing benchmarks for RPCA evaluation are lacking for dialogues . authors introduce specialized judging LLM for automatic RPca evaluation . compelling role-playing agent is expected to lead to more in-depth conversations . |
| Approach: | They propose a benchmark to assess the effectiveness of RPCA interactions using dialogues . they introduce a specialized judging LLM tailored for automatic RPca evaluation . |
| Outcome: | The proposed benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators. |
CryptOpiQA: A new Opinion and Question Answering dataset on Cryptocurrency (2025.coling-main)
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| Challenge: | Using a dataset of tweets and Reddit, we investigate the public opinion on cryptocurrency and bitcoin on Twitter and RedDit. |
| Approach: | They create a dataset to investigate the public opinion on cryptocurrency and bitcoin on Twitter and Reddit. |
| Outcome: | The proposed dataset contains gold standard and silver standard labels and a question-answering sub-corpus. |
No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement (2025.coling-main)
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| Challenge: | Modular deep learning is the most effective way to lift the curse of multilinguality. |
| Approach: | They propose a method which enables training-free post-processing to address this limitation by adding learning to the language adapters and transitioning the framework from a multi-task to a multiple language setup. |
| Outcome: | The proposed method consistently improves baselines with significant gains, especially in the most challenging case of zero-shot application. |
NYAYAANUMANA and INLEGALLLAMA: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis (2025.coling-main)
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Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Shivam Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | In India, a significant backlog of cases burdens the legal system. |
| Approach: | They present a corpus of 7,02,945 preprocessed Indian legal cases compiled for LJP . they use a domain-specific generative large language model tailored to the intricacies of the legal system . |
| Outcome: | The proposed dataset surpasses existing datasets like PredEx and ILDC, and improves prediction accuracy and comprehensible explanations. |
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media (2025.coling-main)
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| Challenge: | Existing studies have focused on the identification of social media posts that contain misrepresentations of information within associated news articles. |
| Approach: | They propose a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. |
| Outcome: | The proposed model outperforms large language models on the ManiTweet dataset and reveals intriguing connections between manipulation and the domain and factuality of news articles. |
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)
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| Challenge: | Empirical evidence suggests that LLMs perform worse than conventional KGC approaches. |
| Approach: | They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations. |
| Outcome: | The proposed method achieves substantial performance gain compared to existing state-of-the-art methods. |
FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation (2025.coling-main)
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| Challenge: | Recent advances in text-to-image generation still exhibit limitations in terms of knowledge access. |
| Approach: | They propose a fine-grained retrieval-augmented image generation model that breaks down the retrieval task into four critical stages: query decomposition, candidate selection, retrieval augmented diffusion, and self-reflection. |
| Outcome: | The proposed method significantly reduces noise associated with retrieval-augmented image generation and performs better in complex, open-world scenarios. |
User Willingness-aware Sales Talk Dataset (2025.coling-main)
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| Challenge: | Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. |
| Approach: | They developed a user willingness–aware sales talk collection by leveraging the ecological validity concept to elicit natural user willingness. |
| Outcome: | The proposed system elicited user willingness at the utterance level from multiple perspectives and was able to improve the user's intent to purchase. |
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation (2025.coling-main)
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| Challenge: | Recent studies show that using large language models (LLMs) is the most reliable method to evaluate QA models, but suffers from limited interpretability, high cost, and environmental harm. |
| Approach: | They propose to use soft exact match (EM) with entity-driven answer set expansion to expand gold answer set to include diverse surface forms. |
| Outcome: | The proposed method outperforms traditional evaluation methods while offering the benefits of high interpretability and reduced environmental harm. |
Data Augmentation for Cross-domain Parsing via Lightweight LLM Generation and Tree Hybridization (2025.coling-main)
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| Challenge: | Existing approaches for constituency parsing are expensive and lack high-quality labeled data. |
| Approach: | They propose a data augmentation method via lightweight large language model (LLM) generation and tree hybridization to generate a large number of structurally diverse instances. |
| Outcome: | The proposed method achieves significant improvements on five target domains with a lightweight LLM generation cost. |
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)
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Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong
| Challenge: | CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
| Approach: | They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems. |
| Outcome: | The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
Optimizing Lifelong Fine-Tuning for Multiple Tasks via Dataless Distribution Replay (2025.coling-main)
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| Challenge: | Existing methods to fine-tune large language models with minimal instruction data are prone to catastrophic forgetting during life-long fine- tuning. |
| Approach: | They propose a dataless distribution replay approach for life-long fine-tuning that replays the output of the linear layers at previous task stages. |
| Outcome: | The proposed method achieves significant improve- ments compared to several strong lifelong fine- tuning methods. |
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)
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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang
| Challenge: | Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application. |
| Approach: | They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%. |
Efficient Data Labeling by Hierarchical Crowdsourcing with Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been gaining attention for their impressive performance in in-context dialogues. |
| Approach: | They propose a hierarchical framework that leverages multiple LLMs for efficient data labeling under budget constraints. |
| Outcome: | The proposed framework outperforms human labelers and GPT-4 in terms of accuracy and efficiency. |
Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty? (2025.coling-main)
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| Challenge: | Supervised approaches to difficulty estimation have yielded mixed results . generative large models are seen as a weakness when answering questions . |
| Approach: | They exploit generative large models to explore correlations between two different metrics of uncertainty, and the actual student response distribution. |
| Outcome: | The proposed model uncertainty is different in the case of correct vs wrong answers and the student response distribution is different. |
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents. |
| Approach: | They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents. |
| Outcome: | The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets. |
LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
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| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
PERSONA: A Reproducible Testbed for Pluralistic Alignment (2025.coling-main)
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| Challenge: | Currently, preference optimization approaches fail to capture the plurality of user opinions . Currently used methods do not account for the pluralities of users and difference of opinion . |
| Approach: | They propose a reproducible test bed to evaluate pluralistic alignment of language models . they generate user profiles from census data and use a large-scale evaluation dataset . |
| Outcome: | The proposed model improves pluralistic alignment of language models with diverse user values . it generates a large-scale evaluation dataset with 317,200 feedback pairs . |
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings (2025.coling-main)
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| Challenge: | Sentence embedding models are limited for many low-resource languages, including Luxembourgish. |
| Approach: | They propose to use Luxembourgish as an enhanced sentence embedding model with strong cross-lingual capabilities to address this issue. |
| Outcome: | The proposed model can embed Luxembourgish sentences better than high-resource languages. |
Human Interest Framing across Cultures: A Case Study on Climate Change (2025.coling-main)
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| Challenge: | Human Interest (HI) framing is a narrative strategy that injects news stories with a relatable, emotional angle and a human face to engage the audience. |
| Approach: | They perform a systematic analysis of HI stories to understand its role in climate change reporting in English-speaking countries from four continents. |
| Outcome: | The proposed approach has shown to capture and retain readership and enhance political engagement of the population. |
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs (2025.coling-main)
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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models (LLMs) generate naturallysounding answers over a broad range of human inquiries, but they still produce content that deviates from real-world facts. |
| Approach: | They propose a framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document. |
| Outcome: | The proposed framework assesses the factuality of free-form responses in open domains and evaluates factually of LLMs. |
A Dataset for Expert Reviewer Recommendation with Large Language Models as Zero-shot Rankers (2025.coling-main)
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Vanja M. Karan, Stephen McQuistin, Ryo Yanagida, Colin Perkins, Gareth Tyson, Ignacio Castro, Patrick G.T. Healey, Matthew Purver
| Challenge: | state of the art reviewer recommendation systems still have relatively high error rates . |
| Approach: | They propose to use a large language model to improve on SotA, but not a cure-all . they first create a new dataset and introduce LLMs with prompting to evaluate their performance. |
| Outcome: | The proposed approach improves on SotA but not cure-all, the authors argue . they show that the proposed approach can be extended to many related tasks . |
Evaluating Model Alignment with Human Perception: A Study on Shitsukan in LLMs and LVLMs (2025.coling-main)
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| Challenge: | This work examines the alignment of large language models and large vision-language models with human perception. |
| Approach: | They use a dataset of *shitsukan* terms elicited from individuals in response to object images to evaluate their understanding of the Japanese concept of shitukan. |
| Outcome: | The proposed models demonstrated mixed accuracy across benchmark tasks, with limited overlap between model- and human-generated terms. |