Papers by Bing Liu
MIBench: Evaluating Multimodal Large Language Models over Multiple Images (2024.emnlp-main)
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Haowei Liu, Xi Zhang, Haiyang Xu, Yaya Shi, Chaoya Jiang, Ming Yan, Ji Zhang, Fei Huang, Chunfeng Yuan, Bing Li, Weiming Hu
| Challenge: | Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored. |
| Approach: | They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios. |
| Outcome: | The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC. |
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering (2025.acl-long)
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| Challenge: | Existing methods rely on entity vector matching, but the purpose of the question is abstract and difficult to match with specific entities. Existing approaches rely only on entity-vector matching, and there is a problem with multi-hop reasoning. |
| Approach: | They propose a framework that constructs reasoning paths from purposes back to conditions using the KG ontology. |
| Outcome: | Experiments on the WebQSP and CWQ datasets show that ORT significantly improves the capability of large language models in knowledge graph question answering tasks (KGQA). |
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models (2024.findings-emnlp)
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Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin
| Challenge: | Recent advances in large language models (LLMs) focus on replicating human cognition in specific contexts, overlooking the inherently dynamic nature of cognition. |
| Approach: | They propose a task to assess cognitive dynamics of large language models (LLMs) they introduce a benchmark and two evaluation metrics to validate the benchmark and evaluate it through participant surveys. |
| Outcome: | The proposed task overcomes the limitations of existing methods and is available for download. |
Learning Event Graph Knowledge for Abductive Reasoning (2021.acl-long)
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| Challenge: | Existing models for abductive reasoning based on formal logic lack commonsense knowledge and effective reasoning mechanism. |
| Approach: | They propose a narrative text-based abductive reasoning task NLI with a latent variable to capture commonsense knowledge from event graph for guiding the abductive reasoning task. |
| Outcome: | The proposed model outperforms baseline methods on the abductive reasoning task. |
Agentic Rubrics as Contextual Verifiers for SWE Agents (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have rapidly advanced on coding tasks, enabling increasingly capable software engineering agents for real-time code editing and bug fixing. |
| Approach: | They propose to use a rubric checklist to create a context-grounded rubric for SWE agents. |
| Outcome: | The proposed rubrics achieve a score of 54.2% on Qwen3-Coder-30B-A3B and 40.6% on Qween3-332B . |
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
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| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
CogBERT: Cognition-Guided Pre-trained Language Models (2022.coling-1)
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| Challenge: | Existing methods fine-tune pre-trained models on cognitive data, ignoring the semantic gap between texts and cognitive signals. |
| Approach: | They propose a framework that can induce fine-grained cognitive features from cognitive data and incorporate them into pre-trained language models by adaptively adjusting the weight of cognitive features for different NLP tasks. |
| Outcome: | The proposed framework can induce fine-grained cognitive features from cognitive data and incorporate them into BERT by adaptively adjusting weight of cognitive features for different NLP tasks. |
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)
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| Challenge: | Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift. |
| Approach: | They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks. |
| Outcome: | The proposed framework outperforms baselines on Chinese and English CCR datasets. |
Feature Projection for Improved Text Classification (2020.acl-main)
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| Challenge: | In sentiment classification, there are some good features that are indicative of class labels, but there are also many common features that do not discriminate for classification. |
| Approach: | They propose to project existing features into the orthogonal space of the common features and make them more discriminative for classification. |
| Outcome: | The proposed method improves CNN, RNN, Transformer, and Bert based text classification and obtains markedly better results. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification (2020.acl-main)
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| Challenge: | Existing approaches to comparative preference classification do not learn entity-aware representations well or use sequential modeling approaches that do not generalize well. |
| Approach: | They propose a deep-level deep-graph attention network that leverages word embeddings and syntactic information to solve a comparative preference classification problem. |
| Outcome: | The proposed model achieves state-of-the-art performance in comparative preference classification. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si
| Challenge: | Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning. |
| Approach: | They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task. |
| Outcome: | The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings. |
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data. |
| Approach: | They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information. |
| Outcome: | The proposed model outperforms existing systems on most few-shot settings. |
AnRe: Analogical Replay for Temporal Knowledge Graph Forecasting (2025.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are vital for event prediction, yet current methods face limitations. |
| Approach: | They propose a training-free Analogical Replay reasoning framework that uses LLMs to extract historical contexts and generate analogical reasoning examples as contextual inputs. |
| Outcome: | The proposed model outperforms existing training-free methods on four benchmarks. |
Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions (2025.emnlp-industry)
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| Challenge: | Existing talent search approaches fail to capture nuanced job-specific preferences and mitigate noise from subjective human judgments. |
| Approach: | They propose a framework that extracts fine-grained recruitment signals from job descriptions and historical hiring data and employs a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. |
| Outcome: | The proposed framework improves talent search effectiveness and delivers substantial business value. |
Distilled Dual-Encoder Model for Vision-Language Understanding (2022.emnlp-main)
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| Challenge: | Experimental results show that the proposed cross-modal attention distillation is crucial to the success of our framework. |
| Approach: | They propose a framework that distills knowledge of fusion-encoder teacher into dual-encoding student model. |
| Outcome: | The proposed model is competitive with the fusion-encoder teacher model in performance, but suffers from a lack of deep cross-modal interactions. |
AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)
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| Challenge: | Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data. |
| Approach: | They propose a new training paradigm which exploits the inherent information in CoT for iterative generation. |
| Outcome: | The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself. |
Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)
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| Challenge: | End-to-end neural machine translation (NMT) has attracted increasing attention in recent years. |
| Approach: | They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning. |
| Outcome: | The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism . |
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)
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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem. |
| Approach: | They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task. |
| Outcome: | The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis. |
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation (2024.lrec-main)
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| Challenge: | Health coaching is a patient-centered clinical practice that aims to help patients achieve personalized and lifestyle-related goals to enhance their health behaviors. |
| Approach: | They propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. |
| Outcome: | The proposed model outperforms existing state-of-the-art models while eliminating the need for predefined schema and corresponding annotations. |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme. |
| Approach: | They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage . |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity. |
An Evaluation Mechanism of LLM-based Agents on Manipulating APIs (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities across a variety of tasks, such as language, mathematics, coding, and etc. |
| Approach: | They propose to decompose tool use capability into seven aspects and form a thorough evaluation schema for generic agents. |
| Outcome: | The proposed agent acts like a super-APP and can manipulate API-based tools. |
ExpeTrans: LLMs Are Experiential Transfer Learners (2025.acl-long)
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| Challenge: | Recent studies provide large language models with textual task-solving experiences via prompts to improve their performance. |
| Approach: | They propose to use prompts to provide LLMs with textual task-solving experiences during their inference stage. |
| Outcome: | The proposed framework improves the performance of large language models on 13 datasets. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton
| Challenge: | commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools . |
| Approach: | They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression . |
| Outcome: | The proposed approach outperforms human experts in medical examinations on diverse datasets. |
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. |
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks, but struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. |
| Approach: | They propose a framework which introduces the paradigm of resolution refutation to solve first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction. |
| Outcome: | The proposed framework outperforms existing models while maintaining performance in simple scenarios. |
How Does Selective Mechanism Improve Self-Attention Networks? (2020.acl-main)
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| Challenge: | Experimental results show that selective SANs outperform the standard SAN by paying more attention to content words that contribute to the meaning of the sentence. |
| Approach: | They propose to implement selective SANs with a flexible Gumbel-Softmax to improve word order encoding and structure modeling. |
| Outcome: | The proposed system outperforms the standard SANs on several representative NLP tasks including natural language inference, semantic role labelling, and machine translation. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Noisy Multi-Label Text Classification via Instance-Label Pair Correction (2024.findings-naacl)
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| Challenge: | Noise is a significant challenge for machine learning models, especially deep learning models. |
| Approach: | They propose a holistic selection metric that identifies noisy pairs while considering global loss information and instance-specific ranking information. |
| Outcome: | The proposed approach significantly improves performance in noisy multi-label text classification tasks. |
Beware of Your Po! Measuring and Mitigating AI Safety Risks in Role-Play Fine-Tuning of LLMs (2025.acl-long)
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Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Existing role-play fine-tuning techniques improve role adaptability but may degrade safety performance, especially for villainous characters. |
| Approach: | They propose safety-aware Role-Play Fine-Tuning (SaRFT) to balance role-playing capabilities and safety. |
| Outcome: | The proposed method outperforms state-of-the-art baselines under both LoRA and full-parameter fine-tuning settings. |
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. |
Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021.naacl-main)
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Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
| Challenge: | Existing dialogue systems focus on functional goals, open-domain chatbots on socially engaging conversations. |
| Approach: | They propose to add chit-chat to ENhance Task-ORiented dialogues by a human-assisted data collection approach to augment task-oriented dialogues with minimal annotation effort. |
| Outcome: | The proposed models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike while maintaining competitive task performance. |
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)
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| Challenge: | Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM. |
| Approach: | They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions . |
| Outcome: | The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability. |
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language (2023.acl-long)
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| Challenge: | Existing studies focus on decoding word-level fMRI volumes from a restricted vocabulary. |
| Approach: | They propose an open-vocabulary task to bridge fMRI time series and human language . they use a pre-trained language model to construct a robust encoder for cognitive signals . |
| Outcome: | The proposed task bridges fMRI time series and human language with a baseline model. |
Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy (2026.acl-long)
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| Challenge: | Existing RAG methods focus on external retrieval, while ignoring the rich content of the model. |
| Approach: | They propose a framework that enhances explicit synergy over parametric and retrieved knowledge by integrating external retrieval components into the input context of the LLMs. |
| Outcome: | The proposed framework enhances explicit synergy over parametric and retrieved knowledge. |
Cross-Lingual Machine Reading Comprehension (D19-1)
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)
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| Challenge: | Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration. |
| Approach: | They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency. |
| Outcome: | The proposed model improves task accuracy and computational efficiency under two context-dependent scenarios. |
Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration (2026.acl-long)
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Yang Zhao, Yangou Ouyang, Xiao Ding, Hepeng Wang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu
| Challenge: | Existing data arbitration strategies for large language model training rely on surface-level heuristics that fail to diagnose intrinsic learning needs. |
| Approach: | They propose a framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge. |
| Outcome: | Extensive experiments on WebShop and ALFWorld show that PRISM outperforms state-of-the-art hybrid methods while reducing computational costs by up to 3.22 . |
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning (2026.acl-long)
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Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang, Vipul Gupta, Jaehwan Jeong, Anisha Gunjal, Tahseen Rabbani, Maria Mazzone, David Randolph IV, Mohammad Mahmoudi Meymand, Gurshaan Chattha, Paula Rodriguez, Diego A. Mares Buendia, Pavit Singh, Michael Liu, Subodh Chawla, Peter Cline, Lucy Ogaz, Ernesto Gabriel Hernández Montoya, Zihao Wang, Pavi Bhatter, Marcos Ayestaran, Bing Liu, Yunzhong He
| Challenge: | Frontier models often lack a view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most. |
| Approach: | They introduce a professional reasoning benchmark that recruits 182 qualified professionals to contribute questions inspired by their workflows. |
| Outcome: | The proposed model outperforms other models in 114 countries and 47 US jurisdictions on hard subsets. |
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations (2021.findings-emnlp)
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| Challenge: | Experiments show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods. |
| Approach: | They propose to extract and then normalise PICO information from clinical trial articles and use crowdsourced sentence-level annotations to detect spans. |
| Outcome: | The proposed method achieves much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
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Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)
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| Challenge: | Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs) labelled data is used to learn neural EA models, but this aspect is neglected . |
| Approach: | They propose a framework to integrate compatibility into neural EA models . they aim to find equivalent entities between two Knowledge Graphs (KGs) |
| Outcome: | The proposed framework can achieve comparable effectiveness with supervised training using 20% of labelled data. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)
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| Challenge: | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. |
| Approach: | They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity. |
| Outcome: | The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated. |
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection (2025.acl-long)
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Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu
| Challenge: | Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. |
| Approach: | They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. |
| Outcome: | The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios. |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |
Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
| Approach: | They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST. |
| Outcome: | The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains. |
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)
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| Challenge: | Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications. |
| Approach: | They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets. |
CR-LLM: A Dataset and Optimization for Concept Reasoning of Large Language Models (2024.findings-acl)
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Nianqi Li, Jingping Liu, Sihang Jiang, Haiyun Jiang, Yanghua Xiao, Jiaqing Liang, Zujie Liang, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han
| Challenge: | Existing concept reasoning related datasets suffer from modeledge leakage and context leakage. |
| Approach: | They propose a concept reasoning for large language models with modeledge leakage prevention and context leakage preventive methods to improve the models' conceptual reasoning abilities. |
| Outcome: | The proposed method significantly improves the existing models and reasoning methods, achieving a 7% increase in accuracy compared to CoT and showing better granularity. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing alignment methods struggle to balance general reasoning with instruction-following (IF) this is hindered by dependency on teacher models, reward hacking, and reasoning-answer inconsistencies. |
| Approach: | They propose a two-stage curriculum learning framework based on Reinforcement Learning from Verifiable Rewards to enhance both IF and general reasoning capabilities. |
| Outcome: | The proposed framework outperforms leading models on six representative IF tasks while achieving a 21.25% relative average improvement over the original model. |
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER (2021.acl-long)
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| Challenge: | Existing approaches to cross-lingual NER are labeled sequence translation and instance-based transfer via machine translation (MT) Existing methods to cross NER include label projection and labeling, but they are expensive and time-consuming. |
| Approach: | They propose a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids word order change and entity span determination. |
| Outcome: | The proposed method avoids word order change and entity span determination and can be generalized with the language-specific features from the target-language synthetic data and the language independent features from multilingual synthetic data. |
IHEval: Evaluating Language Models on Following the Instruction Hierarchy (2025.naacl-long)
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Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu, Haoming Jiang, Xianfeng Tang, Yifan Gao, Zheng Li, Haodong Wang, Zhaoxuan Tan, Yichuan Li, Qingyu Yin, Bing Yin, Meng Jiang
| Challenge: | Instruction-tuned language models (LMs) are increasingly deployed as interactive services across various applications. |
| Approach: | They propose a benchmark to evaluate models' ability to follow the instruction hierarchy by comparing their models to a set of benchmarks. |
| Outcome: | The proposed benchmark covers 3,538 examples across nine tasks covering cases where instructions in different priorities either align or conflict. |
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate (2023.findings-emnlp)
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| Challenge: | Existing studies focus on inconsistency issues within a single LLM, while we explore the inter-consistencies among multiple LLMs for collaboration. |
| Approach: | They propose a formal debate framework to examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal. |
| Outcome: | The proposed framework enables LLMs to achieve consensus in three real-world debate scenarios with real-time scenarios aligned to the LLM's goals. |
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) rank documents based on the probability of generating the query given the content of a document. |
| Approach: | They propose a ranking system that integrates LLMs with a hybrid zero-shot retriever. |
| Outcome: | The proposed system shows exceptional ranking in both zero-shot and few-shot scenarios. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
| Approach: | They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills. |
| Outcome: | The proposed system improves few-shot end-task learning in these domains. |
Is GPT-3 a Good Data Annotator? (2023.acl-long)
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| Challenge: | Data annotation is the process of labeling data that could be used to train machine learning models. |
| Approach: | They evaluate the performance of a large-scale language model developed by OpenAI . they compare it with traditional methods and analyze its output on a range of tasks . |
| Outcome: | The proposed model has shown impressive performance on a range of NLP tasks. |
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch (2025.findings-emnlp)
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Yirong Zeng, Xiao Ding, Yutai Hou, Yuxian Wang, Li Du, Juyi Dai, Qiuyang Ding, Duyu Tang, Dandan Tu, Weiwen Liu, Bing Qin, Ting Liu
| Challenge: | Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. |
| Approach: | They propose a dynamic generalization-guided reward design for rule-based RL that shifts rewards from exploratory to exploitative tool-use patterns. |
| Outcome: | The proposed model achieves over 7% performance improvement compared to SFT and RL-with-SFT models under the same experimental settings. |
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
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| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
| Approach: | They propose a method to fine-tune a multilingual pre-trained language model fine- tuned with one (source) language and four training policies to address the performance gap. |
| Outcome: | The proposed method outperforms baselines on the XNLI dataset by a clear margin. |
From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)
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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin
| Challenge: | Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization. |
| Approach: | They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. |
| Outcome: | ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks . |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (2023.findings-emnlp)
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| Challenge: | Existing methods for Continual Learning (CL) have limited KT and catastrophic forgetting . a new method overcomes CF by isolating the knowledge of each task . |
| Approach: | They propose a method to overcome catastrophic forgetting and encourage knowledge transfer . they propose to discover a sub-network for each task and a soft-masking mechanism to preserve the previous knowledge. |
| Outcome: | The proposed method outperforms baselines in classification, generation, information extraction and their mixture. |
On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)
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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu
| Challenge: | Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks. |
| Approach: | They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments. |
| Outcome: | The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation. |
MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)
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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data. |
| Approach: | They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages. |
| Outcome: | Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility. |
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)
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| Challenge: | Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce . |
| Approach: | They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions. |
| Outcome: | The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks . |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji
| Challenge: | Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. |
| Approach: | They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities. |
| Outcome: | The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin
| Challenge: | Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage. |
| Approach: | They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
| Outcome: | The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021.emnlp-main)
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| Challenge: | Existing studies have focused on continual learning of aspect sentiment classification (ASC) tasks in domain incremental learning (DIL) |
| Approach: | They propose a continual learning method that learns a sequence of tasks incrementally . they propose CLASSIC, which uses a domain incremental learning setting . |
| Outcome: | The proposed model is highly effective in a domain incremental learning setting. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance. |
| Approach: | They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics. |
| Outcome: | The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency. |
Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts . |
| Approach: | They propose a translation process that aligns the translation-specific understanding with the general understanding to improve translation quality and reduce translation literalness. |
| Outcome: | The proposed translation process improves translation quality and reduces translation literalness by -25% -51%. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions. |
| Approach: | They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics. |
| Outcome: | The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Hierarchical Pointer Net Parsing (D19-1)
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| Challenge: | Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps. |
| Approach: | They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks. |
| Outcome: | The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets. |
MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026.acl-long)
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Yang Zhao, Hepeng Wang, Xiao Ding, Yangou Ouyang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu
| Challenge: | Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), but its efficacy is confined to domains with verifiable ground truths. |
| Approach: | They propose a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model’s terminal hidden states as 'a semantic bottleneck' . Across seven benchmarks, MAESTRO consistently outperforms single-reward and static multi-objective baselines while preserving the efficiency advantages of GRPO. |
| Outcome: | The proposed model outperforms single-reward and static multi-objective baselines while preserving efficiency advantages. |
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)
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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
| Challenge: | Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain. |
| Approach: | They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner. |
| Outcome: | The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting. |
From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities (2025.findings-acl)
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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin
| Challenge: | MLLMs are able to integrate multiple modalities into a single model to tackle complex tasks in real-world scenarios. |
| Approach: | They propose a comprehensive survey of Omni-MLLMs to address the challenges and opportunities of multimodal modeling. |
| Outcome: | The proposed model can integrate multiple modalities into a single model and provide novel perspectives. |
Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)
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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
| Approach: | They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns. |
| Outcome: | The proposed model fails on the highest-performing model with 54.65% pass rate. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)
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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
| Challenge: | Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification . |
| Approach: | They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity. |
| Outcome: | The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues. |
M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025.emnlp-main)
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Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing
| Challenge: | Existing benchmarks for large multimodal models focus on short documents with less than 50 pages and are limited to extraction-based questions. |
| Approach: | They propose a retrieval-aware tuning approach to improve the accuracy of multimodal document reading by 4.6%. |
| Outcome: | The proposed framework improves the accuracy of model responses by 4.6% compared to existing benchmarks on documents with hundreds of pages and longer documents with more complex content. |
Bootstrapped Unsupervised Sentence Representation Learning (2021.acl-long)
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| Challenge: | Existing approaches to learn sentence representations rely on quality labeled data. |
| Approach: | They propose a Siamese Network which maximizes similarity between two augmented views of each sentence. |
| Outcome: | The proposed method outperforms state-of-the-art methods on STS and classification tasks. |
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning (2022.acl-long)
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| Challenge: | Existing causal reasoning models only learn to induce empirical causal patterns that are predictive to the label, while human beings seek for deep and conceptual understanding of the causality to explain the observed causal facts. |
| Approach: | They present a human-annotated CAusal REasoning dataset with conceptual explanations of the causality. |
| Outcome: | The presented dataset shows that human-annotated explanations can be useful for promoting the accuracy and stability of causal reasoning models. |
Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning (2026.acl-long)
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Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Ming Ma, Danyang Zhao, Shuren Qi, Ting Liu, Bing Qin
| Challenge: | Instruction Fine-Tuning (IFT) has emerged as a critical technique for customizing Large Language Models (LLMs) however, recent studies have revealed that IFT can compromise the built-in security mechanisms of LLMs, posing significant security risks. |
| Approach: | They propose a method that shifts learning burden onto security-robust parameters and propose 'warm-up' phase that preferentially trains Mods_Rob to learn low-level features with minimal security risk. |
| Outcome: | The proposed method reduces security risks without sacrificing performance gains across knowledge-intensive datasets. |
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs). |
| Approach: | They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs. |
| Outcome: | The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark. |
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)
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| Challenge: | Existing approaches to generate informative titles for products with limited labels are inadequate for novel products. |
| Approach: | They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products. |
| Outcome: | The proposed approach achieves state-of-the-art results on novel product categories with limited labels. |
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling. |
| Approach: | They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks. |
MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications (2025.emnlp-main)
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| Challenge: | Existing methods for solving complex problems are expensive and inefficient when handling large-scale, high-complexity problems. |
| Approach: | They propose a multi-agent framework that decomposes complex problems through agent collaboration by mapping implicitly expressed graph data into clear, structured graph representations and dynamically selecting the most suitable algorithm based on problem constraints and graph structure scale. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmarks with robust performance on both closed- and open-source models. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
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| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)
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| Challenge: | Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters. |
| Approach: | They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers. |
| Outcome: | The proposed method improves performance across sequence- and token-level lowresource tasks. |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (2022.coling-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings are sense agnostic . a novel framework to align contextual embeddables at the sense level is proposed . |
| Approach: | They propose a framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. |
| Outcome: | The proposed framework improves word sense disambiguation tasks by leveraging bilingual dictionaries . compared with baseline results, the proposed models achieve 0.52%, 2.09% and 1.29% performance improvements . |
Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities (2022.emnlp-main)
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| Challenge: | Existing topic-based novelty detection methods do not perform semantic reasoning involving relations between named entities in text and their background knowledge. |
| Approach: | They propose a model to detect whether a text is novel or not . they propose to use a factual text to characterize novelty. |
| Outcome: | The proposed model outperforms 10 baselines by large margins on the novelty detection task. |
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)
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| Challenge: | Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application. |
| Approach: | They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model. |
| Outcome: | The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity. |
RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)
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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth
| Challenge: | Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains. |
| Approach: | They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control . |
| Outcome: | The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control. |
STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals (2024.findings-emnlp)
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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, Yiqun Liu
| Challenge: | Existing statute retrieval benchmarks emphasize formal and professional queries from sources like bar exams and legal case documents . existing retrieval approaches that lack domain-specific knowledge may struggle to capture the meanings of specialized terms accurately. |
| Approach: | They propose a dataset that captures the complexity and diversity of real queries from the general public. |
| Outcome: | The proposed dataset captures the complexity and diversity of real queries from the general public. |
EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Tool-integrated reasoning (TIR) enables large language models to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse. |
| Approach: | They propose an algorithm that uses a composite reward to model tool costs and tool efficiency. |
| Outcome: | The proposed algorithm models heterogeneous tool costs and encourages more cost-effective tool-use strategies. |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
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| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)
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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
| Challenge: | Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages. |
| Approach: | They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base . |
| Outcome: | The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks. |
Introducing Semantics into Speech Encoders (2023.acl-long)
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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
Probing and Boosting Large Language Models Capabilities via Attention Heads (2025.emnlp-main)
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| Challenge: | Existing approaches to identifying capabilities rely on external signals with limited structural grounding . emergence of specific capabilities remains poorly understood . |
| Approach: | They propose a lightweight approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads. |
| Outcome: | The proposed approach improves accuracy on MMLU and BBH by 1 to 1.5 points over gradient-based method and 5 to 6 points over other intermediate-state baselines. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection (C18-1)
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| Challenge: | Existing approaches to spam detection focus on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. |
| Approach: | They propose a deep learning architecture for incorporating entities and their attributes into a unified framework. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two Yelp datasets. |
DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)
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| Challenge: | Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage. |
| Approach: | They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents. |
| Outcome: | Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget. |
Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have superior reasoning capabilities compared to small language models, but incur substantially higher inference costs. |
| Approach: | They propose a system that cascades an LLM with an SLM to achieve a balance between accuracy and cost in complex reasoning tasks. |
| Outcome: | The proposed system improves the SLM’s reasoning ability and confidence calibration across diverse datasets and model backbones. |
Towards Benchmarking Situational Awareness of Large Language Models:Comprehensive Benchmark, Evaluation and Analysis (2024.findings-emnlp)
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| Challenge: | Situational awareness is crucial for decision-making, anticipating potential issues, and adapting to dynamic circumstances. |
| Approach: | They propose a benchmark that covers three tiers of situational awareness capabilities . they conduct extensive experiments on advanced LLMs including GPT-4, LLaMA3, Qwen1.5 . |
| Outcome: | The proposed benchmark covers environment perception, situation comprehension and future projection. |
Transformation of Dense and Sparse Text Representations (2020.coling-main)
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| Challenge: | Existing approaches to NLP to leverage sparsity have been limited due to the gap with dense representations. |
| Approach: | They propose a Semantic Transformation method to bridge dense and sparse spaces and propose supervised NLP tasks to use both spaces. |
| Outcome: | Experiments with classification tasks and natural language inference tasks show that the proposed method is effective. |
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. |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
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| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |
SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across various English benchmarks, including both human exam datasets such as MMLU and instruction-following datasets. |
| Approach: | They introduce two new benchmarks to evaluate the capabilities of Large Language Models in Southeast Asian (SEA) application scenarios. |
| Outcome: | The proposed benchmarks show that they can discern LLM performance on SEA language tasks compared to their translated benchmarks. |
Enabling Unsupervised Neural Machine Translation with Word-level Visual Representations (2023.findings-emnlp)
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| Challenge: | Unsupervised neural machine translation methods have been observed to make particular errors in comparison to supervised machine translation, such as confusing nouns that pertain to the same semantic category. |
| Approach: | They propose a method that incorporates images at the word level to augment lexical mappings. |
| Outcome: | Experiments on a multi-lingual dataset show that the proposed method generates more accurate translations with only monolingual data. |
Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
| Approach: | They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data. |
| Outcome: | The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design. |
Using the Past Knowledge to Improve Sentiment Classification (2020.findings-emnlp)
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| Challenge: | Existing model retains knowledge learned from past tasks and selectively transfers it to new task to help it learn better. |
| Approach: | They propose a lifelong learning model that can retain and selectively transfer the knowledge learned in the past to help learn the new task. |
| Outcome: | The proposed model outperforms strong baselines, including even multiple task learning. |
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. |
| Approach: | They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty. |
| Outcome: | The proposed framework offers insights into optimal strategies for accelerating LVLMs. |
Learning with Noisy Labels for Sentence-level Sentiment Classification (D19-1)
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| Challenge: | Existing research on learning with noisy labels dates back to the 1980s, but it is still vibrant today. |
| Approach: | They propose a novel DNN model called NetAb to deal with noisy labels during training and train the networks using their respective loss functions in mutual reinforcement. |
| Outcome: | The proposed model can fit training data with noisy labels and predict clean labels. |
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution (2023.findings-acl)
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| Challenge: | State-of-the-art natural language processing models have been shown to achieve remarkable performance in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
| Approach: | They propose a multi-stage task to evaluate a system's performance on pipelined novelty ‘detection’ and ‘accommodation’ tasks. |
| Outcome: | The proposed model performs poorly in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)
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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin
| Challenge: | OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs. |
| Approach: | They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5. |
| Outcome: | The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL). |
On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)
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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye
| Challenge: | Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Approach: | They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Outcome: | Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020.findings-emnlp)
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| Challenge: | Existing pre-trained language models have shown tremendous improvements across various NLP tasks. |
| Approach: | They propose to revisit Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pretrained model series to the community. |
| Outcome: | The proposed model improves on RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). |
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time) (D19-1)
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| Challenge: | Seq2Seq models for table-to-text generation have achieved remarkable progress, but modeling table representation in one dimension is inadequate. |
| Approach: | They propose to model each table cell considering other records in the same row and to enrich table’s representation by modeling each cell in context of other cells in the similar column or with historical data respectively. |
| Outcome: | The proposed model outperforms baseline and state-of-the-art models on ROTOWIRE, a benchmark dataset of NBA basketball games. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)
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| Challenge: | Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. |
| Approach: | They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner. |
| Outcome: | The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks. |
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)
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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
| Challenge: | Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs. |
| Approach: | They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn. |
| Outcome: | The proposed framework improves strategy selection preferences at each dialogue turn. |
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure (2020.coling-main)
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| Challenge: | Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research. |
| Approach: | They propose to annotate a machine reading comprehension dataset with discourse structure built over multiparty dialog using a modified Segmented Discourse Representation Theory (SDRT) style. |
| Outcome: | The proposed dataset contributes large-scale discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT) style for all of its multiparty dialogs, and achieves only 67.7% F1 on Molweni’s questions, a 20+% significant drop as compared against its SQuAD 2.0 performance. |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
| Approach: | They propose a generative model to map natural language questions into SQL queries. |
| Outcome: | The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset. |
Natural Logic at the Core: Dynamic Rewards for Entailment Tree Generation (2025.findings-acl)
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| Challenge: | Existing approaches to generating entailment trees lack logical consistency . static reward structures or intricate dependencies within multi-step reasoning are often ignored . |
| Approach: | They propose a method that integrates natural logic principles into reinforcement learning to guide entailment tree generation. |
| Outcome: | Experiments on EntailmentBank show that the proposed method improves interpretability and generalization. |
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding (2024.findings-emnlp)
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Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu
| Challenge: | Existing methods to enhance length extrapolation of large language models have been developed, but a systematic survey is lacking. |
| Approach: | They propose to examine the effects of positional encoding on length extrapolation. |
| Outcome: | The proposed methods improve the extrapolation of large language models, but they are still lacking a systematic survey. |
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. |
| Approach: | They extend the evaluation to real-world user queries and non-English-centric LLMs . they show that translation into English can boost LLM performance on NLP tasks . |
| Outcome: | The proposed evaluation extends to user queries and non-English-centric LLMs . it shows that translation into English can boost performance on NLP tasks, but not universally optimal . |
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models (2021.eacl-main)
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| Challenge: | a large-scale unsupervised pretraining has been shown to greatly boost the performance of natural language processing models. |
| Approach: | They propose an intuitive finetuning strategy to regularize the finetune process . they propose a mix-review strategy to alleviate the forgetting problem . |
| Outcome: | The proposed strategy regularizes the finetuning process, and the forgetting problem is alleviated . the proposed strategy also improves the performance of the resulting model . |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
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. |
MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026.findings-acl)
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Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Existing causal datasets focus on the commonsense domain, but LLMs perform poorly when answering complex questions. |
| Approach: | They propose a multidisciplinary causal evaluation benchmark to assess LLMs' knowledge and skills. |
| Outcome: | The proposed model improves in domain specialization, structural diversity, and task complexity. |
Length Controlled Generation for Black-box LLMs (2025.acl-long)
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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua
| Challenge: | Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. |
| Approach: | They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters. |
| Outcome: | The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead. |
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon (2021.findings-acl)
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| Challenge: | Existing studies on sentiment lexicons have focused on domain-dependent sentiment words. |
| Approach: | They propose a graph-based technique to detect and correct domain-dependent sentiment words . they propose to use a sentiment lexicon to classify sentiments in a lexical-based classifier . |
| Outcome: | The proposed method is effective on multiple datasets from different domains. |
Distilling Knowledge for Search-based Structured Prediction (P18-1)
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| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
| Outcome: | The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines. |
Exploring Large Language Models for Effective Rumor Detection on Social Media (2025.naacl-long)
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| Challenge: | Large-scale contexts hinder LLMs’ reasoning abilities while moderate contexts perform better for LLM. |
| Approach: | They propose a semantic-propagation collaboration-base framework that integrates small language models with LLMs for effective rumor detection. |
| Outcome: | The proposed framework bridges the gap between LLMs and LLM in facing long, structured data and offers a novel solution for rumor detection on social media. |
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)
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| Challenge: | Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness. |
| Approach: | They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed. |
| Outcome: | The proposed method can lead to performance increase of 8.5% compared to the best performing text-only model. |
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner (2024.acl-long)
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| Challenge: | Existing approaches to provide LLMs with textual task-solving experience rely on manual efforts to acquire and apply such experience for each task. |
| Approach: | They propose a lifelong autonomous experiential learning framework based on LLMs that learns and accumulates experience through experience transfer and induction. |
| Outcome: | The proposed framework performs reliably in each intermediate step and improves GPT-3.5 and GPT-4 on widely used NLP datasets. |
FineReason: Evaluating and Improving LLMs’ Deliberate Reasoning through Reflective Puzzle Solving (2025.acl-long)
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Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong
| Challenge: | Recent advances in large language models (LLMs) highlight an important shift from the “System 1” way of quick reactions to the “system 2” style of reflection-and-correction problem solving. |
| Approach: | They propose a logic-puzzle benchmark for systematic evaluation of large language models' reasoning capabilities that decomposes each puzzle into atomic steps. |
| Outcome: | The proposed model improves on state checking and state transition tasks and demonstrates gains in reasoning by up to 5.1%. |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)
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| Challenge: | Existing approaches to combining knowledge Graphs (KGs) are incomplete but complementary to each other. |
| Approach: | They propose a novel Active Learning framework for neural EA that creates highly informative seed alignments to obtain more effective models with less annotation cost. |
| Outcome: | The proposed framework significantly improves sampling quality with good generality across different datasets, EA models and amount of bachelors. |
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt (2024.lrec-main)
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Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, LinZheng Chai, Liqun Yang, Zhoujun Li
| Challenge: | Existing multimodal neural machine translation models focus on bilingual translation, but experimental results show that they outperform the text-only baselines and multilingual multimodal methods by a large margin. |
| Approach: | They propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual Neural Machine Translation (m3P) this framework aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. |
| Outcome: | The proposed framework outperforms previous text-only baselines and multilingual multimodal methods by a large margin. |
UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)
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Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li
| Challenge: | Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin. |
| Approach: | They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages. |
| Outcome: | The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions . |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |
AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender (2025.emnlp-main)
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Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Activation steering offers training-free defense but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. |
| Approach: | They propose an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. |
| Outcome: | The proposed method outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility. |
Semantic Novelty Detection in Natural Language Descriptions (2021.emnlp-main)
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Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
| Challenge: | Existing novelty detection algorithms are coarse-grained, working at the document or topic level. |
| Approach: | They propose to use a fine-grained semantic novelty detection problem to solve a novel novel scene problem. |
| Outcome: | The proposed model outperforms baseline models on the proposed task by large margins. |
Neural Natural Logic Inference for Interpretable Question Answering (2021.emnlp-main)
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| Challenge: | Existing question answering models are based on textual entailment tasks . prior work has focused on QA on premise-based questions . |
| Approach: | They propose a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures towards developing effective question answering models. |
| Outcome: | The proposed model outperforms previous work on multiple-choice science questions . it integrates natural logic reasoning within deep learning architectures to build proof paths . |
Class-Incremental Learning based on Label Generation (2023.acl-short)
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| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
| Approach: | They propose a method which solves CIL based on label generation by using sparse vocabulary and creates pseudo-replay samples by using label semantics. |
| Outcome: | The proposed method outperforms baseline models by a large margin in the class-incremental learning setting. |
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)
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| Challenge: | Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools. |
| Approach: | They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators . |
| Outcome: | The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance. |
Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)
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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning. |
| Approach: | They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps. |
| Outcome: | The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets. |
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining (2020.findings-emnlp)
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| Challenge: | Existing methods to classify and resolve coreferences in opinionated reviews require domain-specific knowledge. |
| Approach: | They propose to automatically mine domain-specific knowledge for opinionated reviews by combining it with commonsense knowledge. |
| Outcome: | The proposed approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage commonsense knowledge for the task. |
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021.naacl-main)
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| Challenge: | Existing studies on continual learning of a sequence of aspect sentiment classification tasks have not addressed these issues. |
| Approach: | They propose a capsule network based model called B-CL to address these issues . it uses continual learning adapters and capsule networks to encourage knowledge transfer . |
| Outcome: | The proposed model improves the performance on both the new task and the old tasks via forward and backward knowledge transfer. |
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. |
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing domain adaptation paradigms for reading comprehension require large amounts of annotation data to achieve the desired task performance. |
| Approach: | They propose a few-shot domain adaptation paradigm for reading comprehension . they introduce self-attention attribution to weigh parameters and refine the lottery subnetwork . |
| Outcome: | The proposed model outperforms the full model fine-tuning adaptation on four out of five domains with a small amount of data available for adaptation. |
End-to-End Learning of Task-Oriented Dialogs (N18-4)
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| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
| Outcome: | The proposed system can track dialog state, interface with knowledge bases, and integrate structured query results into system responses to successfully complete task-oriented dialog. |
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text (D19-1)
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| Challenge: | Existing methods to extract factual tuples from scientific text do not consider conditions. |
| Approach: | They propose a new sequence labeling framework to jointly extract fact and condition tuples from scientific sentences. |
| Outcome: | The proposed framework improves F1 score relative to existing methods by 4.2% and 6.2% on bioNLP2013. |
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
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| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
| Approach: | They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories. |
| Outcome: | The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models. |
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
| Outcome: | The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets. |
Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)
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| Challenge: | Using partially-aligned data is an alternative way of solving the dataset scarcity problem. |
| Approach: | They propose a task to generate human-readable text for describing some given structured data enabling more interpretability. |
| Outcome: | The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data. |
Analyzing the Rapid Generalization of SFT via the Perspective of Attention Head Activation Patterns (2025.acl-long)
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| Challenge: | Currently, LLMs learn in a data-driven schema while the instructions about complex tasks are both scarce and hard to collect or construct. |
| Approach: | They employ a gradient-based method to dissect the process that the Supervised Fine-tuning Process (SFT) adapts LLMs to downstream tasks via the perspective of attention patterns. |
| Outcome: | The proposed method dissects the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation (2023.findings-emnlp)
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| Challenge: | Existing methods to evaluate ChatGPT's causal reasoning abilities are based on pre-trained language models, but they rely on supervised training. |
| Approach: | They conduct the first comprehensive evaluation of ChatGPT’s causal reasoning capabilities using four state-of-the-art (STA) simulations. |
| Outcome: | The proposed model is not a good causal reasoner, but a great causal interpreter. |
Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing (2023.acl-long)
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| Challenge: | Existing approaches to debiase Natural Language Understanding models use dataset biases instead of learning the intended task. |
| Approach: | They propose a debiasing framework that detects and purifies dataset biases using information entropy. |
| Outcome: | The proposed framework improves the stability of performance on out-of-distribution datasets for a set of widely adopted NLU models. |
KETOD: Knowledge-Enriched Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | Existing studies treat task-oriented dialogue and chit-chat as separate domains . a new dataset is created to integrate both types of dialogue into a single system . |
| Approach: | They propose to integrate task-oriented dialogue and knowledge-grounded chit-chat into a single model by using a dataset. |
| Outcome: | The proposed models improve the performance of knowledge-enriched dialogues while maintaining a competitive task-oriented dialog performance. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning (2024.emnlp-main)
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| Challenge: | Experimental results demonstrate that OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. |
| Approach: | They propose to integrate parametric user knowledge into the personal PEFT parameters and non-parametric knowledge from retrieval and profiles, adapting LLMs to user behavior shifts. |
| Outcome: | The proposed method outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. |
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning (2021.acl-long)
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| Challenge: | Existing work infers the causation between events based on knowledge from annotated causal event pairs, but additional evidence information is unexploited. |
| Approach: | They propose an Event graph knowledge enhanced explainable CAusal Reasoning framework that acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in human evaluation and in animal models. |
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
| Challenge: | Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. |
| Approach: | They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory. |
| Outcome: | The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset. |
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)
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| Challenge: | Grasping the concept of time is a fundamental facet of human cognition. |
| Approach: | They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena. |
| Outcome: | The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning . |
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)
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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
| Challenge: | Health coaching is cost-prohibitive due to its highly personalized nature. |
| Approach: | They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation. |
| Outcome: | The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations. |
STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing (2022.emnlp-main)
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| Challenge: | Existing work to mitigate the effect of noisy labels is limited to specific tasks or training procedures, making it hard to be widely used. |
| Approach: | They propose a stochastic tailor-made gradient noise to mitigate the effect of noisy labels by introducing benign noise into stochistic gradient descent. |
| Outcome: | The proposed method can be used to discriminate correct samples from incorrect ones and boost existing training methods. |
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)
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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song
| Challenge: | Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. |
| Approach: | They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. |
| Outcome: | The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
An Inversion Attack Against Obfuscated Embedding Matrix in Language Model Inference (2024.emnlp-main)
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| Challenge: | Recent studies have explored transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service providers. |
| Approach: | They propose to transform user inputs to obfuscated embedded vectors so that the data will not be eavesdropped by service providers. |
| Outcome: | The proposed inversion attack can recover user input 100% from the obfuscated vectors without a solid and deliberate security design and analysis . |
Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)
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| Challenge: | Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked. |
| Approach: | They propose to use model-aware glass-box features to evaluate an LLM's output. |
| Outcome: | The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks. |
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. |
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing (2023.findings-acl)
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| Challenge: | Large-scale datasets in the real world often contain label noise, which can cause model overfitting and degrade generalization. |
| Approach: | They propose to use label noise to imitate human errors in annotations . they use a noisy label noise benchmark to evaluate their methods . |
| Outcome: | The proposed benchmarks are different from data with heterogeneous label noises in the real world. |
Modeling Multi-Action Policy for Task-Oriented Dialogues (D19-1)
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| Challenge: | Existing approaches to learn dialogue management only predict one action per turn, limiting expressive power of the conversational agent and introducing unnecessary turns of interactions. |
| Approach: | They propose a model based on a recurrent cell called gated Continue-Act-Slots that overcomes the limitations of existing models and proposes a novel policy model that predicts multiple acts for each turn. |
| Outcome: | The proposed model outperforms existing models on the task of predicting multiple acts for each turn. |
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Recent studies show impressive results on aspects-based sentiment analysis tasks. |
| Approach: | They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks. |
| Outcome: | The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks. |
A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning (2026.findings-acl)
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Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin
| Challenge: | Large Language Models (LLMs) have made strong progress in reasoning. |
| Approach: | They propose a dual-phase test-time scaling framework that separates planning and execution and performs search over each phase independently. |
| Outcome: | Experiments on math reasoning and code generation benchmarks show that the proposed approach improves accuracy while reducing redundant computation. |
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning (2021.naacl-main)
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| Challenge: | Existing zero-shot learning methods for multi-label text classification mostly learn a matching model between the feature space of text and the label space. |
| Approach: | They propose to use a graph encoder to incorporate label hierarchies to learn effective label representations on the zero-shot multi-label text classification problem. |
| Outcome: | The proposed approach outperforms previous non-pretrained methods on the zero-shot multi-label text classification task. |
Large Language Models Are Still Misled by Simple Bias Ensembles (2026.findings-acl)
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| Challenge: | Existing benchmarks for large language models are constrained to datasets where each sample is manually injected with only one type of bias. |
| Approach: | They propose a multi-bias benchmark where each sample contains multiple types of biases. |
| Outcome: | The proposed benchmark shows that existing LLMs and debiasing methods perform poorly on this benchmark, highlighting the challenge of eliminating compounded biases. |
Continual Learning in Task-Oriented Dialogue Systems (2021.emnlp-main)
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Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, Zhiguang Wang
| Challenge: | Existing continuous learning systems are not designed to add new domains and functionalities through time without incurring the high cost of retraining the whole system. |
| Approach: | They propose a first-ever continual learning benchmark for task-oriented dialogue systems . they propose 'architecture' method based on residual adapters to implement continual training . |
| Outcome: | The proposed architectural method performs better than multitask learning while being 20X faster in learning new domains. |
MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)
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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu
| Challenge: | Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing. |
| Approach: | a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials. |
| Outcome: | The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance . |
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021.findings-emnlp)
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| Challenge: | Existing conversational systems are agent-centric, which assumes the user utterances will closely follow the system ontology. |
| Approach: | They build a dataset that maps user preferences to an ontology and model user preferences as estimated distributions over the system ontologies. |
| Outcome: | The proposed system can be used to push existing research from agent-centric to user-centric. |
Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)
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| Challenge: | Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus. |
| Approach: | They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances. |
| Outcome: | The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing research on multi-modal Named Entity Recognition (MNER) does not integrate all multi-modal representations to provide rich contextual information to improve NER. |
| Approach: | They propose an iterative reasoning framework that integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate" . they propose to use hierarchically connected fusion layers to prioritize transitions from "easy-to-hard" and "coarse-to fine" |
| Outcome: | The proposed framework integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate". |
Extending Context Window of Large Language Models from a Distributional Perspective (2024.emnlp-main)
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Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Existing scaling methods for extending context window rely on empirical approaches and lack understanding of the internal distribution within RoPE resulting in suboptimal performance. |
| Approach: | They propose to optimize the context window extending task from the view of rotary angle distribution by minimizing disturbance between rotary angles to maintain consistency with the pre-training phase. |
| Outcome: | The proposed approach reduces by up to 72% of the distributional disturbance when extending LLaMA2’s context window to 8k, and reduces it by up 32% when extending to 16k. |
iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use (2025.emnlp-main)
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Yirong Zeng, Xiao Ding, Yuxian Wang, Weiwen Liu, Yutai Hou, Wu Ning, Xu Huang, Duyu Tang, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Synthesizing tool-use data through real-world simulations is effective for enhancing large language models (LLMs) however, training gains decay as synthetic data increases, and the model struggles to benefit from more synthetic data. |
| Approach: | They propose an iterative reinforced fine-tuning strategy to improve LLMs with external tools to augment their capabilities. |
| Outcome: | The proposed method achieves 13.11% better performance than the same-size base model and outperforms larger open-source and closed-source models. |
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks. |
| Approach: | They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question. |
| Outcome: | The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)
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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin
| Challenge: | Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications. |
| Approach: | They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models. |
| Outcome: | The proposed method improves the model’s robustness and reliability in temporal analysis. |
Parsing Tweets into Universal Dependencies (N18-1)
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| Challenge: | a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD). |
| Approach: | They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. |
| Outcome: | The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed. |
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)
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| Challenge: | Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks. |
| Approach: | They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs. |
| Outcome: | The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks. |
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)
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| Challenge: | Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task. |
| Approach: | They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones. |
| Outcome: | The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions. |
A Graph Enhanced BERT Model for Event Prediction (2022.findings-acl)
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| Challenge: | Existing methods to predict subsequent events use sparsity of event graph to improve performance. |
| Approach: | They propose to automatically build event graph using a BERT model by adding a structured variable to the model to learn to predict event connections. |
| Outcome: | The proposed model outperforms state-of-the-art models on two event prediction tasks. |
On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)
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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi
| Challenge: | Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. |
| Approach: | They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model. |
| Outcome: | Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning. |
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (2020.findings-emnlp)
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| Challenge: | Table-to-text models that select and order salient data and verbalize them fluently are lacking in content planning stage. |
| Approach: | They propose to enhance neural content planning by understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning. |
| Outcome: | The proposed model outperforms existing systems with respect to content planning metrics on ROTOWIRE and MLB datasets. |
Probing Language Models for Pre-training Data Detection (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, while raising concerns about the data contamination due to privacy issues and leakage of benchmark datasets in the pre-training phase. |
| Approach: | They propose to utilize the probing technique to examine the model’s internal activations to detect pre-training data contamination by examining the model's internal activates. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy. |
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. |
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)
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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
| Challenge: | Pre-trained models perform poorly with limited data and rare biomedical words. |
| Approach: | They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach. |
| Outcome: | The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings. |
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. |
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance across numerous NLP tasks, but fine-tuning them for Machine Translation (MT) often introduces catastrophic forgetting, compromising the broad general abilities of LLMs and introducing potential security risks. |
| Approach: | They propose a method that harnesses the strong generative capabilities of Large Language Models to create rationales for training data, which are then "replayed" to prevent forgetting. |
| Outcome: | The proposed approach harnesses the strong generative capabilities of LLMs to create rationales for training data, which are then “replayed” to prevent forgetting. |
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging (2025.findings-emnlp)
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| Challenge: | a new adaptive merging method is proposed to improve fine-tuning performance . traditional methods often encounter task interference when merging full fine-uning models . |
| Approach: | They propose an adaptive merging method that directly measures model parameters using the Frobenius norm . |
| Outcome: | The proposed method outperforms baseline methods in various fine-tuning scenarios. |
Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
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| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |
MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document (2023.findings-emnlp)
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| Challenge: | Existing work models time implicitly, making it difficult to handle complex relationships . a novel temporal reasoning framework explicitly models the temporal relationships among facts by multi-view temporal graphs . |
| Approach: | They propose a multi-view temporal graph-based temporal reasoning framework that explicitly models the temporal relationships among facts by multi-visit temporal charts. |
| Outcome: | The proposed framework gives more consistent answers under question perturbations. |
Infrared-LLaVA: Enhancing Understanding of Infrared Images in Multi-Modal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for infrared modeling ignore supervisory signals of infra-modality-specific attributes, which may lead to biased understanding of in-frarea images. |
| Approach: | They propose a multi-agent generation system which transfers knowledge from visible images to generate infrared image-text pairs and infra-instructional data. |
| Outcome: | The proposed system generates infrared image-text pairs and infra-response data and is able to answer common infreas tasks with the proposed model. |