Papers by Yang Yu
NeuralFSM: Adaptive Multi-Agent Coordination via Learning Finite-State Execution Policy (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multi-agent problem solving rely on hand-crafted protocols or automatically designed topologies. |
| Approach: | They propose a state-driven framework that formulates multi-agent problem solving as a finite-state execution process. |
| Outcome: | The proposed framework outperforms baselines on diverse benchmarks by 6.74%–19.39% while reducing token consumption. |
Product Question Answering in E-Commerce: A Survey (2023.acl-long)
Copied to clipboard
| Challenge: | Product question answering (PQA) aims to automatically provide instant responses to customer’s questions in E-commerce platforms. |
| Approach: | They categorize PQA studies into four problem settings in terms of the form of provided answers. |
| Outcome: | The proposed methods capture the unique challenges of product question answering (PQA) . |
Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
| Approach: | They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method . |
| Outcome: | The proposed methods outperform random selection on large datasets on large data pools. |
HSDreport: Heart Sound Diagnosis with Echocardiography Reports (2024.findings-emnlp)
Copied to clipboard
Zihan Zhao, Pingjie Wang, Liudan Zhao, Yuchen Yang, Ya Zhang, Kun Sun, Xin Sun, Xin Zhou, Yu Wang, Yanfeng Wang
| Challenge: | Existing methods for heart sound diagnosis are limited to a few fixed categories and do not utilize echocardiography reports, the gold standard in the diagnosis of related diseases. |
| Approach: | They propose a benchmark that mandates the direct utilization of heart sounds obtained from auscultation to predict echocardiography reports. |
| Outcome: | The proposed method outperforms existing methods and existing multimodal LLMs in detecting key abnormalities in heart sounds. |
Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement (2026.findings-acl)
Copied to clipboard
Cheng Yang, Xuemeng Yang, Licheng Wen, Daocheng Fu, Jianbiao Mei, Rong Wu, Pinlong Cai, Yufan Shen, Nianchen Deng, Jia Xu, Botian Shi, Yu Qiao, Haifeng Li
| Challenge: | Large Language Models struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. |
| Approach: | They propose a framework that organizes cross-domain insights to facilitate orchestration of long-horizon workflows. |
| Outcome: | The proposed framework outperforms existing methods on the TAC productivity benchmark and shows strong cross-task transferability. |
Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)
Copied to clipboard
| Challenge: | Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing. |
| Approach: | They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows. |
| Outcome: | The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows. |
ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis aims to predict the sentiment of video content. |
| Approach: | They propose a framework that performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. |
| Outcome: | The proposed framework outperforms baseline methods on CH-SIMS, MOSI and MOSEI datasets on a range of metrics. |
DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed. |
| Approach: | They propose a method which reformulates zero-shot tasks into token discrimination tasks without having to construct verbalizers. |
| Outcome: | The proposed method outperforms baselines on two zero-shot entity recognition datasets with higher inference speed and achieves 7.5% improvement over previous state-of-the-art models on Wiki-ZSL and FewRel. |
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models require associated image with input sentence, which is difficult to satisfy at inference. |
| Approach: | They propose to use synthetic and authentic images to generate translations using text-to-image generation models. |
| Outcome: | The proposed model achieves state-of-the-art performance on En-De and En-Fr datasets while remaining independent of authentic images during inference. |
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP (2024.findings-emnlp)
Copied to clipboard
Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu
| Challenge: | a new task is to generate lay definitions of medical terms in EHRs that are difficult to understand for patients. |
| Approach: | They propose a task of automatically generating lay definitions to simplify medical terms into patient-friendly lay language. |
| Outcome: | The proposed model can match or surpass state-of-the-art closed-source large language models like ChatGPT with high-quality data. |
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
Copied to clipboard
Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao
| Challenge: | generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap. |
| Approach: | They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback . |
| Outcome: | The proposed system significantly improves image reasoning and generation quality. |
Cross-Lingual Abstractive Summarization with Limited Parallel Resources (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual summarization use limited available cross-linguistic resources. |
| Approach: | They propose a multi-task framework for cross-lingual abstractive summarization that uses a single decoder to generate monolingual and cross-linguistic summaries. |
| Outcome: | Experiments on two CLS datasets show that the proposed model outperforms baseline models in low-resource and full-dataset scenarios. |
Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty (2025.findings-emnlp)
Copied to clipboard
Yu Feng, Phu Mon Htut, Zheng Qi, Wei Xiao, Manuel Mager, Nikolaos Pappas, Kishaloy Halder, Yang Li, Yassine Benajiba, Dan Roth
| Challenge: | Existing methods to gauge model’s uncertainty through self-consistency in responses to the target query are misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same query when answering a knowledge-preserving perturbation of the query. |
| Approach: | They propose a method that uses multi-agent interaction to estimate black-box LLMs' uncertainty. |
| Outcome: | The proposed method outperforms existing self-consistency based methods and improves hallucination detection. |
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)
Copied to clipboard
| Challenge: | Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions. |
| Approach: | They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra. |
| Outcome: | The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions. |
MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills (2026.eacl-long)
Copied to clipboard
Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu
| Challenge: | Current clinical LLM benchmarks fail to evaluate advanced clinical skills in AI and large language models (LLMs). |
| Approach: | They propose a framework to evaluate large language models (LLMs) using two instruction-following tasks designed to reflect real clinical scenarios. |
| Outcome: | The proposed framework evaluates LLMs through two instruction-following tasks designed to reflect real clinical scenarios. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)
Copied to clipboard
Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang
| Challenge: | Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies. |
| Approach: | They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states. |
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)
Copied to clipboard
| Challenge: | Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments. |
| Approach: | They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments. |
| Outcome: | The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability. |
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)
Copied to clipboard
| Challenge: | Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications. |
| Approach: | They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally. |
| Outcome: | The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance. |
Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing SBDH datasets lack detailed annotations and are limited in their availability and coverage. |
| Approach: | They propose a synthetic SBDH annotation dataset with detailed SBDH status, temporal information, and rationale across 15 categories. |
| Outcome: | The proposed dataset outperforms models with no Synth-SBDH training on three tasks using real-world clinical datasets from two distinct hospital settings. |
Text Fluoroscopy: Detecting LLM-Generated Text through Intrinsic Features (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing because of their excellent performance on various tasks. |
| Approach: | They propose a black-box method with better generalizability for detecting LLM-generated text by mining the intrinsic features of the text to be detected. |
| Outcome: | The proposed method achieves 7.36% and 2.84% improvement in detection performance compared to baselines in detecting texts from different domains generated by GPT-4 and Claude3 respectively. |
MultiCMET: A Novel Chinese Benchmark for Understanding Multimodal Metaphor (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on multimodal metaphors does not address categorizing the source and target domains in metaphors beyond the English language. |
| Approach: | They propose a Cascading Domain Knowledge Integration benchmark to detect metaphors by introducing domain-specific lexical features. |
| Outcome: | The proposed dataset includes 13,820 text-image pairs of advertisements with manual annotations of the occurrence of metaphors, domain categories, and sentiments metaphors convey. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
Copied to clipboard
| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
UnCo: Uncertainty-Driven Collaborative Framework of Large and Small Models for Grounded Multimodal NER (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to identify unseen multimodal entities struggle with limited knowledge and generalization. |
| Approach: | They propose a framework that leverages the strengths of small fine-tuned models and MLLMs to generate unambiguous predictions. |
| Outcome: | Extensive experiments show that the proposed framework retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen entities. |
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
Copied to clipboard
Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
Copied to clipboard
Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)
Copied to clipboard
| Challenge: | Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances. |
| Approach: | They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances. |
| Outcome: | The proposed framework is effective and compatible with supervised training. |
Instruction Fusion: Advancing Prompt Evolution through Hybridization (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEva+, MBPP, mbap+ and MultiPL-E. |
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure . |
| Approach: | They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments . |
| Outcome: | The proposed framework reduces the number of experts and memory usage, making it easier to deploy. |
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework (2025.acl-long)
Copied to clipboard
Hengyuan Zhang, Chenming Shang, Sizhe Wang, Dongdong Zhang, Yiyao Yu, Feng Yao, Renliang Sun, Yujiu Yang, Furu Wei
| Challenge: | Experiments show that ShifCon significantly enhances the performance of non-dominant languages due to the imbalance in training data across languages. |
| Approach: | They propose a Shift-based multilingual Contrastive framework that aligns the internal forward process of other languages toward that of the dominant one. |
| Outcome: | The proposed framework significantly improves performance of non-dominant languages, particularly for low-resource ones. |
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)
Copied to clipboard
Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar
| Challenge: | Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning. |
| Approach: | They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database. |
| Outcome: | The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods. |
Rethinking Sentiment Style Transfer (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluation methods for text style transfer are unsatisfactory. |
| Approach: | They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals. |
| Outcome: | The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation. |
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)
Copied to clipboard
Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu
| Challenge: | Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance . |
| Approach: | They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal. |
| Outcome: | The proposed method surpasses state-of-the-art methods for task-specific instruction tuning. |
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
Boosting Summarization with Normalizing Flows and Aggressive Training (2023.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that FlowSUM improves the quality of generated summaries with minimal impact on inference time. |
| Approach: | They propose a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization. |
| Outcome: | The proposed model improves the quality of generated summaries and reduces inference time. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
Copied to clipboard
Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences. |
| Approach: | They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal . |
| Outcome: | The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset . |
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)
Copied to clipboard
Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. |
| Approach: | They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation . |
| Outcome: | The proposed framework improves Java-to-C# translation quality at the repository level. |
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks. |
Intrinsic Test of Unlearning Using Parametric Knowledge Traces (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for “unlearning” information captured in large language models rely on behavioral tests without monitoring residual knowledge in model parameters. |
| Approach: | They propose a general evaluation methodology that uses vocabulary projections to inspect concepts encoded in model parameters. |
| Outcome: | The proposed method detects changes in parametric traces of unlearned concepts and localizes them in two open-source LLMs. |
Identifying the Periodicity of Information in Natural Language (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods to detect periodicity of information in natural language are based on a canonical periodicity detection algorithm. |
| Approach: | They propose a method to detect periods in surprisal sequences in natural language . they propose to use this method to identify periods outside the distributions of typical units . |
| Outcome: | The proposed method can detect significant periods in a single document. |
MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora. |
| Approach: | They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs . |
| Outcome: | The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties. |
DANLI: Deliberative Agent for Following Natural Language Instructions (2022.emnlp-main)
Copied to clipboard
Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Yu, Yuwei Bao, Joyce Chai
| Challenge: | Recent work on embodied AI agents that can perform tasks by following human language instructions is limited by reactive methods, which are insufficient for long-horizon complex tasks. |
| Approach: | They propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience. |
| Outcome: | The proposed agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark. |
Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)
Copied to clipboard
Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu
| Challenge: | Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages. |
| Approach: | They propose to use large language models as a general-purpose interface across multiple tasks and languages. |
| Outcome: | The proposed model performs better on 200K hours of 6-language data for voice generation applications. |
Culture Cartography: Mapping the Landscape of Cultural Knowledge (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can empower users to be more knowledgeable, productive, and creative, but their utility is often diminished for under-represented groups and cultures. |
| Approach: | They propose a methodology that operationalizes a mixed-initiative approach to finding culture-specific knowledge that is salient to in-group users but unknown to LLMs. |
| Outcome: | The proposed method improves the accuracy of LLMs on culturally-competent language models by 19.2%. |
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure. |
| Approach: | They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy. |
| Outcome: | The proposed method improves translation performance and robustness to noise on three benchmarks. |
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing defense methods rely on fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks. |
| Approach: | They propose a decoding-level defense mechanism that employs a lightweight discriminator to iteratively steer the decoding process toward safety. |
| Outcome: | The proposed method improves safety performance by up to 33.40% without fine-tuning on multiple MLLMs. |
ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Deep Learning Tensor Programs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Compilation-based methods with performance models have poor measurement accuracy and transferability between platforms. |
| Approach: | They propose a compiler that automatically generates tensors and automatically tunes them for different hardware platforms. |
| Outcome: | The proposed model reduces inference time and costs on modern DNN benchmarks. |
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence. |
| Approach: | They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights . |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
Copied to clipboard
Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE. |
| Approach: | They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM) |
| Outcome: | The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
Copied to clipboard
Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)
Copied to clipboard
Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance. |
| Approach: | They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning . |
| Outcome: | Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance . |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
Copied to clipboard
| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
Copied to clipboard
Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)
Copied to clipboard
| Challenge: | Extensive experiments show that CorrKG is capable of generating high-quality keyphrases. |
| Approach: | They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS . |
| Outcome: | The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques. |
NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness. |
| Approach: | They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment. |
| Outcome: | The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines. |
CLiMP: A Benchmark for Chinese Language Model Evaluation (2021.eacl-main)
Copied to clipboard
| Challenge: | Linguistically informed analyses of language models (LMs) contribute to understanding and improvement of such models. |
| Approach: | They introduce a corpus of Chinese linguistic minimal pairs (CLiMP) to investigate what knowledge Chinese LMs acquire. |
| Outcome: | The proposed corpus of Chinese linguistic minimal pairs (CLiMP) covers 9 major Chinese linguist phenomena. |
Inferring symmetry in natural language (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Empirical work on predicate symmetry has taken two main approaches: feature-based approach and context-based one denies the existence of absolute symmetry. |
| Approach: | They propose a methodological framework for inferring symmetry of verb predicates in natural language. |
| Outcome: | The proposed framework is based on a dataset of 400 naturalistic verbs spanning the spectrum of symmetry-asymmetry. |
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains. |
| Approach: | They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks. |
| Outcome: | Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information. |
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)
Copied to clipboard
Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
| Challenge: | Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities. |
| Approach: | They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. |
| Outcome: | The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics. |
MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
Evaluating the Knowledge Dependency of Questions (2022.emnlp-main)
Copied to clipboard
Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, Seungtaek Choi
| Challenge: | Existing evaluation metrics for MCQ generation focus on the n-gram based similarity of the generated MCq to the gold sample and disregard their educational value. |
| Approach: | They propose to use a human survey to measure the MCQ’s answerability given knowledge of the target fact. |
| Outcome: | The proposed methods measure the MCQ’s answerability given knowledge of the target fact. |
Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists? (2025.acl-long)
Copied to clipboard
| Challenge: | equivariance evaluations uncover weak but observable consistency through structured analyses of the intermediate latent space enabled by multiple editing operations. |
| Approach: | They use a dataset of 1,000 images paired with captions, editing instructions, and Q&A pairs to evaluate cross-modal transfers rigorously. |
| Outcome: | The proposed models do not consistently demonstrate greater cross-modal consistency than specialized models in pointwise evaluations such as cyclic consistency. |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
Copied to clipboard
Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
Copied to clipboard
Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
| Approach: | They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances. |
| Outcome: | The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets. |
Curriculum Pre-training for End-to-End Speech Translation (2020.acl-main)
Copied to clipboard
| Challenge: | End-to-end speech translation requires a powerful encoder to transcribe, understand and learn cross-lingual semantics simultaneously. |
| Approach: | They propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. |
| Outcome: | The proposed method improves on En-De and En-Fr speech translation benchmarks. |
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Recent approaches to reduce resource requirements for task-specific large language models have been developed. |
| Approach: | They propose a delta compression approach that optimizes for importance of a model . they use SVD to dynamically adjust the sparsity ratios of different vectors based on their importance . |
| Outcome: | The proposed approach achieves state-of-the-art in retaining task-specific knowledge even at high sparsity ratios. |
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks (2025.emnlp-main)
Copied to clipboard
Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, Jiajun Bu
| Challenge: | Existing Braille research focuses on isolated tasks while mixed-content Braille tasks face data scarcity and ambiguities. |
| Approach: | They propose a syntax tree-based augmentation method tailored for Braille data. |
| Outcome: | The proposed method improves Braille translation, formula-to-Braille conversion, and mixed-text translation. |
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem. |
| Approach: | They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies. |
| Outcome: | The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance. |
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts. |
| Approach: | They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error. |
| Outcome: | The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks. |
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)
Copied to clipboard
Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang
| Challenge: | Existing methods to integrate external information into a given table neglect the structured nature of the table. |
| Approach: | They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question. |
| Outcome: | The proposed method outperforms strong baselines on three table QA benchmarks. |
Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)
Copied to clipboard
| Challenge: | Empirical results show that attention mechanism can be improved from the energy consumption aspects. |
| Approach: | They propose to replace multiplications with either selective operations or additions to reduce energy consumption. |
| Outcome: | The proposed model achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure. |
Non-parallel Accent Transfer based on Fine-grained Controllable Accent Modelling (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing accent transfer methods rely on parallel data or speech recognition models. |
| Approach: | They propose to use mutual information learning to disentangle accent features and control the accent of the generated speech during the inference time. |
| Outcome: | The proposed framework achieves superior performance to baseline models in accentedness and audio quality. |
Using active learning to expand training data for implicit discourse relation recognition (D18-1)
Copied to clipboard
| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
| Approach: | They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives. |
| Outcome: | The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. |
Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge. |
| Approach: | They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs. |
| Outcome: | The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints. |
Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current work relies on pre-defined rules or templates to control the style of speech. |
| Approach: | They propose to use open-domain instructions to generate speech with the acoustic style that meets users’ needs based on their instructions. |
| Outcome: | The proposed model can be used to generate speech with the acoustic style that meets users’ needs based on open-domain instructions. |
ChemReason-Bench: Benchmarking Large Language Models for Procedural Reasoning in Experimental Chemistry (2026.acl-long)
Copied to clipboard
| Challenge: | Experimental protocols in organic synthesis specify not only the intended transformation, but also an executable sequence of operations and conditions. |
| Approach: | They propose a human-validated benchmark for verifiable experimental procedure reasoning . they instantiate 7306 benchmark tasks across six complementary formats . |
| Outcome: | The proposed benchmarks show that the evaluations are less diagnostic of procedure-level decision making. |
Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)
Copied to clipboard
| Challenge: | Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction. |
| Approach: | They propose a visual question answering task that provides a natural language answer to a question based on a given image and an automated pipeline to generate ambiguity-clarification question pairs. |
| Outcome: | The proposed benchmark targets three common categories of ambiguity in visual question answering (VQA) context and encompasses various VQA scenarios. |
PsyChain: A Collaborative Chain-of-Agents Framework for Generating Personalized and Professional Counseling Dialogues (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing psychological counseling datasets suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability. |
| Approach: | They propose a framework that evolves static counseling corpora into high-fidelity dialogues . they use a Client Profiler that pairs life scenarios with psychological personality archetypes based on client personality and stage progression . |
| Outcome: | The proposed framework achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling. |
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)
Copied to clipboard
| Challenge: | Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text. |
| Approach: | They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation. |
| Outcome: | The proposed model improves the performance of existing language models across a diverse set of language tasks. |
Large Language Models are In-context Teachers for Knowledge Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | In-context teaching is a method of providing in-concept example rationales to a student to reason over unseen cases. |
| Approach: | They propose to use an LLM's self-elicited explanations as in-context demonstrations to prompt a student to reason over unseen cases. |
| Outcome: | The proposed model outperforms human-crafted demonstrations on medical question answering and human-created models outperfect human-made demonstrations. |
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics (2024.findings-acl)
Copied to clipboard
| Challenge: | Structured data processing is a complex and complex process. |
| Approach: | They propose a framework that captures heterogeneity of structured data using large language models . they propose group positional encoding, hierarchical attention bias and optimal transport alignment layer . |
| Outcome: | The proposed framework outperforms baseline methods and few-shot GPT-4 on a medical lab report dataset. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
Copied to clipboard
Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem . |
| Approach: | They propose an EAE model with stage-customized entity type embedding to explore the role of entity types. |
| Outcome: | The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings. |
Dependency-aware Prototype Learning for Few-shot Relation Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for few-shot relation classification fail to distinguish multiple relations that co-exist in one sentence. |
| Approach: | They propose a dependency-aware prototype learning method for few-shot relation classification . they utilize dependency trees and shortest dependency paths as structural information . |
| Outcome: | The proposed method achieves better performance than baselines on the FewRel dataset. |
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)
Copied to clipboard
Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate . |
| Approach: | They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity . |
| Outcome: | The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds. |
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
Copied to clipboard
Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario. |
| Approach: | They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence . |
| Outcome: | The proposed method outperforms previous methods on sentence-level and token-level tasks. |
Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models (2025.findings-acl)
Copied to clipboard
Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, Ge Yu
| Challenge: | Existing evaluation metrics cannot fairly evaluate the outputs of RAG models during training and evaluation. |
| Approach: | They propose a method which prompts LLMs to generate different judgments based on various combinations of judgment dimensions and utilizes the judge-consistency to evaluate these judgments. |
| Outcome: | The proposed method generates more accurate evaluations for RAG models across different RAG model and datasets. |
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement (2026.acl-long)
Copied to clipboard
| Challenge: | Existing alignment methods for Large Language Models (LLMs) are expensive and lack the flexibility to fully activate their latent reasoning capabilities. |
| Approach: | They propose a modular framework that treats reasoning elicitation as an inference-time alignment task. |
| Outcome: | The proposed framework outperforms baselines by 2.1% on average across diverse architectures and benchmarks. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB). |
| Approach: | They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template. |
| Outcome: | The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation. |
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation (2025.emnlp-industry)
Copied to clipboard
Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu
| Challenge: | Existing algorithms for collaborative filtering are limited by their computational demands and latency. |
| Approach: | They propose a framework to integrate pre-trained LLM embeddings into CF models through selective initialization strategies. |
| Outcome: | The proposed framework improves recommendation performance while maintaining low computational costs. |
LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods struggle to capture the visual layout in complex document images. |
| Approach: | They propose to integrate layout knowledge into document image translation by using a layout-aware encoder and a multi-step conductive decoder to achieve the translation step by step. |
| Outcome: | The proposed model outperforms state-of-the-art methods with better parameter efficiency. |
GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)
Copied to clipboard
Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng
| Challenge: | Structured product information is a major bottleneck for the efficiency of e-commerce platforms. |
| Approach: | They propose a data-driven approach to generate product structured representations using product metadata. |
| Outcome: | Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms. |
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems (2022.acl-long)
Copied to clipboard
| Challenge: | Moral integrity corpus captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Approach: | They propose a resource that captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Outcome: | The proposed resource captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
Copied to clipboard
Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Mathematical reasoning has long been a key benchmark for evaluating large language models. |
| Approach: | They propose a framework that transforms math word problems into scalable tabular reasoning tasks. |
| Outcome: | The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks. |
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts (2026.acl-long)
Copied to clipboard
| Challenge: | Existing LLM-based agents lack inherent spatial awareness, relying on web search or text matching while hallucinating spatial relationships. |
| Approach: | They propose a spatial-based agent that can perform real-world geospatial computations . they use natural-language questions to parse into executable workflows based on geoFlow Graphs - directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. |
| Outcome: | The proposed agent outperforms existing baselines on MapEval-API and MapQA benchmarks while producing interpretable and executable geospatial workflows. |
Retrieval-augmented GUI Agents with Generative Guidelines (2025.emnlp-main)
Copied to clipboard
| Challenge: | GUI agents powered by vision-language models struggle with real-world tasks due to their complex nature and limited training data. |
| Approach: | They propose a lightweight vision-language model that leverages web tutorials at inferencetime to synthesize GUI agents. |
| Outcome: | The proposed agent outperforms baseline GUI agents and surpasses other inference baselines by 2.6% to 13.3% across two model sizes. |
ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training (2026.findings-acl)
Copied to clipboard
| Challenge: | Current approaches to enhancing tool use for LLM-based agents focus on post-training fine-tuning or test-time context extension. |
| Approach: | They propose to enhance tool knowledge for LLM-based agents during continuous pre-training . they curate 5.1 million code artifacts from large-scale, high-quality code repositories . |
| Outcome: | The proposed model outperforms existing methods on out-of-distribution tools on multiple benchmarks. |
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)
Copied to clipboard
| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
| Approach: | They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers. |
| Outcome: | The proposed framework improves on pre-trained models on natural language understanding datasets and shows that it can be used to train pre-structured neural networks. |
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset. |
| Approach: | They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning. |
| Outcome: | The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification. |
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)
Copied to clipboard
Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun
| Challenge: | Existing benchmarks focus on character-centric approach and fail to reflect real-world applications. |
| Approach: | RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds. |
| Outcome: | RMTBench features 80 diverse characters and over 8,000 dialogue rounds. |
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing (2020.acl-srw)
Copied to clipboard
| Challenge: | Recent deep learning methods for MeSH indexing fail to capture complex correlations between terms. |
| Approach: | They propose a model to learn the relationship between MeSH terms using Graph Convolution Network (GCN) they use two biGRUs to learn embedding representations of abstract and title of MeSH index text . |
| Outcome: | The proposed model is competitive with the state-of-the-art models on two datasets. |
AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)
Copied to clipboard
Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang
| Challenge: | Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs. |
| Approach: | They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure. |
| Outcome: | The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic. |
Taxonomy-Guided Zero-Shot Recommendations with LLMs (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to deploy large language models (LLMs) into RecSys have limited prompt length, unstructured item information, and un-constrained generation of recommendations. |
| Approach: | They propose a taxonomy-guided recommendation framework that empowers LLMs with category information in a systematic approach. |
| Outcome: | The proposed framework significantly improves recommendation quality compared to zero-shot approaches. |
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)
Copied to clipboard
Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song
| Challenge: | Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization. |
| Approach: | They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. |
| Outcome: | The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues. |
Language Scaling for Universal Suggested Replies Model (2021.naacl-industry)
Copied to clipboard
Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang
| Challenge: | We consider scaling automated suggested replies (SR) to multiple languages for a commercial email application. |
| Approach: | They propose a multi-lingual multi-task continual learning framework with auxiliary tasks and language adapters to train universal language representation across regions. |
| Outcome: | The proposed model reduces catastrophic forgetting and improves cross-lingual transfer across languages while reducing training costs. |
Retrieve & Memorize: Dialog Policy Learning with Multi-Action Memory (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent years have seen a rapid growth of interest in building task-oriented dialogue systems. |
| Approach: | They propose a retrieve-and-memorize framework to deal with unbalanced distribution of system actions in dialogue datasets. |
| Outcome: | The proposed framework achieves competitive performance among state-of-the-art models on a large-scale task-oriented dialogue dataset. |
Examining False Positives under Inference Scaling for Mathematical Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in language models have led to significant improvements in mathematical reasoning across benchmarks. |
| Approach: | They analyze the prevalence of false positives in language models by using heuristic evaluation methods . they find that false positive models produce correct final answers but with flawed deduction paths . |
| Outcome: | The proposed model performance improvements are based on the proposed model and its evaluation metrics. |
PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)
Copied to clipboard
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
| Challenge: | Currently, collecting high quality conversational data is expensive and infeasible for many applications . a promising direction is to generate synthetic dialogues by prompting large language models . |
| Approach: | They propose to use expert-written conversations as in-context examples to generate synthetic dialogues by prompting large language models. |
| Outcome: | The proposed approach is generalizable to multi-party conversations, compared to human-collected conversations. |
SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)
Copied to clipboard
Wenru Xu, Peixuan Xu, Ziqi Yang, Ming Hu, Zihui Wang, Jianzhong Qi, Rongshan Yu, Xiaoliang Fan, Cheng Wang
| Challenge: | Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification. |
| Approach: | They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification. |
| Outcome: | The proposed framework accelerates inference while reducing the LLM usage costs. |
SDA: Semantic Discrepancy Alignment for Text-conditioned Image Retrieval (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for textconditioned image retrieval are limited due to the scale of training and the complexity of attributes in modification texts. |
| Approach: | They propose a general boosting framework to address these issues by employing semantic discrepancy alignment by formulating distance consistency and neighbor consistency between the image and text domains. |
| Outcome: | The proposed framework improves retrieval performance on three prominent datasets with state-of-the-art results. |
CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction (2022.findings-acl)
Copied to clipboard
| Challenge: | Recent research on Chinese spelling correction methods has poor performance on multi-typo texts. |
| Approach: | They propose to use Bert-based Chinese spelling correction models to overcome these limitations by constructing a noisy context for each training sample and a copy mechanism to encourage the model to choose the input character when the miscorrected and input character are both valid. |
| Outcome: | The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves a remarkable gain. |
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)
Copied to clipboard
Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models. |
| Approach: | They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots. |
| Outcome: | Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines. |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
Copied to clipboard
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. |
Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs). |
| Approach: | They propose a framework that aligns natural language with logical symbols to establish a shared representation and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. |
| Outcome: | The proposed framework mitigates logical reasoning collapse at high complexity while improving generalization to unseen logical compositions. |
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)
Copied to clipboard
Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang
| Challenge: | Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality. |
| Approach: | They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data. |
| Outcome: | The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data. |
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches for low-resource relation extraction use only confident instances and uncertain instances. |
| Approach: | They propose a self-training approach for low-resource relation extraction using auto-annotated instances. |
| Outcome: | The proposed method improves on two widely used datasets with low-resource settings. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
Copied to clipboard
Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
Affect inTweets: A Transfer Learning Approach (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing machine learning models require considerable effort to design task specific features to understand affectual states of people. |
| Approach: | They propose a transfer-learning based approach to infer the affectual state of a person from tweets. |
| Outcome: | The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets. |
PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)
Copied to clipboard
| Challenge: | PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements. |
| Approach: | They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance . |
| Outcome: | The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries. |
Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages. |
| Approach: | They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs. |
| Outcome: | The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks. |
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)
Copied to clipboard
Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen
| Challenge: | a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens . |
| Approach: | They propose a timbre-controllable, end-to-end voice interaction system with single-stage training. |
| Outcome: | The proposed system outperforms previous models on 4 GPUs with limited data. |
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch. |
| Approach: | They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks. |
| Outcome: | The proposed method significantly outperforms baseline models on translation tasks and handling the entities. |
UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages. |
| Approach: | They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence. |
| Outcome: | The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments. |
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)
Copied to clipboard
| Challenge: | Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries. |
| Approach: | They propose a syntax tree network to address the complex text-to-SQL generation task. |
| Outcome: | The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus. |
Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts . |
| Approach: | They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base. |
| Outcome: | The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset. |
Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (2025.findings-acl)
Copied to clipboard
Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu
| Challenge: | Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively . |
| Approach: | They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias . |
| Outcome: | The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark. |
Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text. |
| Approach: | They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters. |
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extract relation facts from limited labeled corpora are laborintensive to obtain . Existing approaches use self-training to generate pseudo labels that will cause gradual drift problem or leverage meta-learning scheme which does not solicit feedback explicitly. |
| Approach: | They propose a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. |
| Outcome: | The proposed method handles two major scenarios in low-resource relation extraction when no unlabeled data is available. |
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning (2024.findings-acl)
Copied to clipboard
| Challenge: | Low-rank adaption (LoRA) is a low-level pruning method that can be expensive and slow to deploy. |
| Approach: | They propose a low-rank adaption pruning framework that provides an accurate structured pruned model in a memory-efficient manner. |
| Outcome: | The proposed pruning framework reduces perplexity and memory usage by 52.6% on LLaMA and T5 models while reducing memory usage. |
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to reliability of large language models often lack self-correction or use costly post-hoc verification. |
| Approach: | They propose a decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. |
| Outcome: | Extensive experiments across five benchmarks show the proposed framework improves truthfulness and factual accuracy. |
Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)
Copied to clipboard
Huanhuan Wei, Xiao Luo, Hongyi Yu, Jinping Liang, Luning Yang, Lixing Lin, Alexandra Popa, Xiting Yan
| Challenge: | Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously. |
| Approach: | They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts. |
| Outcome: | The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge. |
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, but their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns. |
| Approach: | They propose a topology-guided security lens and treatment for robust LLM-MAS that leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation. |
| Outcome: | Experiments show that the proposed security lens recovers 40% of the performance under various attack strategies and integrates with mainstream MAS with security guarantees. |
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
Copied to clipboard
Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
AttnPO: Attention-Guided Process Supervision for Efficient Reasoning (2026.acl-long)
Copied to clipboard
Shuaiyi Nie, null Dingsiyu, Wenyuan Zhang, Linhao Yu, Tianmeng Yang, Yao Chen, Weichong Yin, Yu Sun, Hua Wu, Tingwen Liu
| Challenge: | Existing trajectory-level length penalties fail to effectively shorten reasoning length and degrade accuracy, as they treat all reasoning steps uniformly and lack fine-grained signals to distinguish redundancy from necessity. |
| Approach: | They propose a low-overhead process-supervised RL framework that leverages the model’s intrinsic attention signals for step-level credit assignment. |
| Outcome: | The proposed framework reduces reasoning length while improving performance across 9 benchmarks. |
ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for knowledge distillation use Chain-of-Thought (CoT) and answer pairs, but they lack appropriate supervision signals. |
| Approach: | They propose a framework that decouples CoT and answer supervision . the framework applies semantic similarity constraints while maintaining strict literal matching for the answer . |
| Outcome: | The proposed framework decouples CoT and answer supervision while maintaining strict literal matching for the answer. |
SudokuFill: A Multi-Agent Progressive Filling Framework for Document-Level Scientific Information Extraction (2026.findings-acl)
Copied to clipboard
Yang Li, Yajiao Wang, Yu Zhang, Yuanzhe Zhang, Maodi Hu, Mengting Zhang, Xi Sun, Hua Yue, Zhixiong Zhang
| Challenge: | Scientific information extraction (SciIE) is a key bottleneck for turning unstructured papers into computable knowledge bases. |
| Approach: | They propose a scientific information extraction framework that solves a Sudoku problem as a progressive filling problem. |
| Outcome: | The proposed framework outperforms the GPT-4o model on a document-level adjuvant dataset. |
Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
| Approach: | They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations. |
| Outcome: | The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL). |
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on a single query language, resulting in limited generalizability . a new task paradigm is proposed to unify semantic parsing tasks across different query languages . |
| Approach: | They propose a task paradigm that unifies parsing tasks across query languages . they identify query skeletons as a shared optimization target of Text-to-Query tasks . |
| Outcome: | The proposed method achieves state-of-the-art performance using only a small amount of synthesized data. |
RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference (2026.acl-long)
Copied to clipboard
Siran Liu, Guoxia Wang, Sa Wang, Jinle Zeng, Haoyang Xie, Siyu Lou, Jiabin Yang, Dianhai Yu, Haifeng Wang, Chao Yang
| Challenge: | Existing approaches to dynamic sparse attention require preprocessing, lack global evaluation, violate query independence, or incur high computational overhead. |
| Approach: | They propose a dynamic sparse attention method that achieves all desirable properties through a head **r**ound-**r**obin (RR) sampling strategy. |
| Outcome: | Experiments on natural language understanding and multimodal video comprehension show that the proposed method achieves 2.4 speedup at 128K context length outperforming existing methods. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
Copied to clipboard
| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
| Outcome: | The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets. |
Generating Accurate Electronic Health Assessment from Medical Graph (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models based on medical domain-specific knowledge or patients’ prior diagnoses and clinical encounters were mainly based upon clinical diagnoses. |
| Approach: | They propose a graph neural network model that incorporates clinical knowledge into an end-to-end corpus-learning system and builds on it. |
| Outcome: | The proposed model significantly improves the BLEU and rouge score compared with baseline models and physicians’ evaluation showed that it generates high-quality assessments. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
Copied to clipboard
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Type Enhanced BERT for Correcting NER Errors (2023.findings-acl)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is the task of identifying spans that belong to particular categories, such as person, location, organization, etc. |
| Approach: | They propose a method that integrates named entity’s type information into BERT by an adapter layer and integrates it into a gazetteer. |
| Outcome: | The proposed method outperforms baselines in multiple corpus. |
Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. |
| Approach: | They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively. |
| Outcome: | The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning. |
MoKA:Parameter Efficiency Fine-Tuning via Mixture of Kronecker Product Adaption (2025.coling-main)
Copied to clipboard
| Challenge: | Low-Rank Adaptation (LoRA) is one of the most popular PEFT methods . low-rank update mechanism of LoRA somewhat limits its ability to approximate full-parameter fine-tuning during training process. |
| Approach: | They propose a parameter-efficient fine-tuning framework that combines Kronecker product with the Mixture-of-Experts method to achieve parameter efficiency and better model performance. |
| Outcome: | The proposed framework outperforms existing methods on the GLUE benchmark and instruction tuning tasks for large language models. |
Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts. |
| Approach: | They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps. |
| Outcome: | The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets. |
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that In-Context Learning (ICA) can align Large Language Models (LLMs) with human preferences without requiring parameter adjustments. |
| Approach: | They investigate the effectiveness of each part in enabling ICA to function effectively and examine how variants in these parts impact alignment performance. |
| Outcome: | The proposed model can comprehend human instructions without parameter adjustments. |
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning (2023.acl-long)
Copied to clipboard
Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren
| Challenge: | Existing methods to improve logical reasoning skills require complex data processing. |
| Approach: | They propose an adaptive pretraining approach to improve logical reasoning over text . they use a subset of Wikipedia sentences for pretraining and a sentence-level classification loss . |
| Outcome: | The proposed model outperforms baselines on LogiQA and ReClor. |
A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)
Copied to clipboard
Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang
| Challenge: | Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs . |
| Approach: | They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding . |
| Outcome: | The proposed model shows an increase in performance in KIE and VQA tasks. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
Copied to clipboard
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. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
Copied to clipboard
Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining (2021.emnlp-main)
Copied to clipboard
| Challenge: | Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. |
| Approach: | They propose a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict emergent compositional expressions through time. |
| Outcome: | The proposed framework predicts emergent compositions through time using cognitive mechanisms . it is based on modal knowledge and categorizing models of chaining in a syntactically parsed English corpus . |
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)
Copied to clipboard
Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu
| Challenge: | Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along. |
| Approach: | They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks. |
| Outcome: | The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
Copied to clipboard
Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)
Copied to clipboard
Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song
| Challenge: | Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances. |
| Approach: | They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. |
| Outcome: | The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement. |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Cultural competence is defined as the ability to understand and adapt to multicultural contexts. |
| Approach: | They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs. |
| Outcome: | The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs. |
Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue. |
| Approach: | They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages. |
| Outcome: | The proposed model improves the current dialogue generation while reducing the hallucination issue. |
On Large Language Models’ Hallucination with Regard to Known Facts (2024.naacl-long)
Copied to clipboard
Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, Jie Zhou
| Challenge: | Large language models are successful in answering factoid questions but are also prone to hallucination. |
| Approach: | They propose self-reporting to the model when faced with such limitations. |
| Outcome: | The proposed classifier can detect hallucinations with an 88% success rate and can be used to answer factoid questions with correct answer knowledge. |
Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms (2026.findings-acl)
Copied to clipboard
| Challenge: | Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). |
| Approach: | They use AI to learn English neologisms and write messages using the learned word to an NS friend. |
| Outcome: | The proposed model shows that AI Explanation yields the largest gains over no support in NS-rated competence, while contextual appropriateness judgments show indifference across support. |
Template-Based Named Entity Recognition Using BART (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters. |
| Approach: | They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework. |
| Outcome: | The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task. |
KeFVP: Knowledge-enhanced Financial Volatility Prediction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Current studies ignore the role of financial metrics knowledge in earnings calls and little consideration is given to integrating text and price information. |
| Approach: | They propose to integrate financial metrics knowledge into text comprehension by knowledge-enhanced adaptive pre-training and effectively incorporating text and price information by introducing a conditional time series prediction module. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three real-world datasets and is effective and reliable. |
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing (2020.lrec-1)
Copied to clipboard
| Challenge: | Clinical trials require that patients meet eligibility criteria to ensure safety and effectiveness of studies. |
| Approach: | They propose a dataset that includes the first-of-its-kind eligibility-criteria corpus and queries for criteria-to-sql . they propose 'neuro semantic parser' which can translate eligibility criteria to executable SQL queries . |
| Outcome: | The proposed parser outperforms existing state-of-the-art general-purpose models while highlighting the challenges presented by the new dataset. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
Copied to clipboard
Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent. |
| Approach: | They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system. |
| Outcome: | The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency. |
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition (C18-1)
Copied to clipboard
| Challenge: | Event relation recognition is a challenging language processing task because the query events are selected from different paragraphs in a document or even different documents, so there is lack of explicit clue. |
| Approach: | They propose to use image processing to acquire similar event instances and use image matching to approximate calculation between events. |
| Outcome: | The proposed model performs comparable to CNN while slightly better than LSTM on the ACE-R2 corpus. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
Copied to clipboard
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. |
Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale (2026.findings-acl)
Copied to clipboard
| Challenge: | a large-scale empirical dataset involving 57,954 essays from 10,195 students across 120 schools over two years is presented in this paper. |
| Approach: | They propose a triadic collaboration system that supports K-12 writing learning . they propose linguistic expansion as a pedagogical gatekeeper and bridge . |
| Outcome: | The proposed system improves writing quality through a strategic labor division . authors find that excessive linguistic expansion yields diminishing marginal utility . |
Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (CNER) is a widely used technology in various applications. |
| Approach: | They propose a method that uses a custom-designed relevance scoring function to learn the potential relevance between different flattened hierarchical labels. |
| Outcome: | The proposed method outperforms the state-of-the-art on the FiNE dataset. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
Copied to clipboard
Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
START: Self-taught Reasoner with Tools (2025.emnlp-main)
Copied to clipboard
Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu
| Challenge: | Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. |
| Approach: | They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. |
| Outcome: | Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). |
CF-TCIR: A Compositor-Free Framework for Hierarchical Text-Conditioned Image Retrieval (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to text-conditioned image retrieval use attention-driven compositors . instead, we reformulate the retrieval process as a cross-modal interaction between a synthesized image feature and its corresponding text descriptor. |
| Approach: | They propose a compositor-free framework for text-conditioned image retrieval . they propose combining a reference image and modification text to form a query tuple . |
| Outcome: | The proposed framework offers advantages in terms of computational efficiency, scalability, and performance. |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
Copied to clipboard
Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
Syntax-Aware Retrieval Augmented Code Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural code generation models with token-level retrieval capabilities are often noisy and time-consuming. |
| Approach: | They propose a token-level retrieval augmented code generation method that leverages syntax constraints for the retrieval of datastores. |
| Outcome: | The proposed method reduces the impact of retrieve noise on code generation on two datasets. |
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have found that information relevant to the next token prediction task accumulates in the hidden representations of just a few tokens. |
| Approach: | They propose a method that integrates attention preferences useful for a downstream task into the eviction process of hidden states. |
| Outcome: | The proposed method performs better on comprehension and retrieval tasks while preserving language modeling perplexity. |
Adversarial Category Alignment Network for Cross-domain Sentiment Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods for cross-domain sentiment classification focus on aligning marginal distribution without taking category-specific decision boundaries into consideration. |
| Approach: | They propose an adversarial category alignment network to enhance category consistency . experimental results show the proposed method can achieve state-of-the-art performance . |
| Outcome: | The proposed method achieves state-of-the-art performance and produces more discriminative features on benchmark datasets. |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
Copied to clipboard
Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to image paragraph captioning ignore the past alignment information, resulting in repetitive captioning and incomplete captioning. |
| Approach: | They propose an Interactive key-value Memory-augmented Attention model for image paragraph captioning to keep track of attention history along with update-chain of decoder state. |
| Outcome: | Extensive experiments on a benchmark dataset demonstrate the effectiveness of the proposed model. |
Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)
Copied to clipboard
Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li
| Challenge: | Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions. |
| Approach: | They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps. |
| Outcome: | The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%. |
Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)
Copied to clipboard
| Challenge: | Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios. |
| Approach: | They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization. |
| Outcome: | The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings. |
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities . |
| Approach: | They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses . |
| Outcome: | The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
Copied to clipboard
Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020.acl-main)
Copied to clipboard
| Challenge: | Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE . BERT is one of the largest models ever in NLP, but suffers from heavy model size and high latency . |
| Approach: | They propose a tool to compress and accelerate the popular BERT model by task-agnostic application. |
| Outcome: | The proposed model is 4.3x smaller and 5.5x faster than BERT_BASE . it achieves competitive results on well-known benchmarks . |
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are now commonplace in conversation applications, but their misuse for generating harmful responses has raised serious societal concerns. |
| Approach: | They provide a comprehensive overview of recent studies covering attacks, defenses, and evaluations of Large Language Models (LLMs) . |
| Outcome: | The proposed review summarizes three aspects of LLM conversation safety: attacks, defenses, and evaluations. |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
Copied to clipboard
| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
| Outcome: | The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset. |
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)
Copied to clipboard
Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
| Challenge: | Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images. |
| Approach: | They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images. |
| Outcome: | The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
Copied to clipboard
Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)
Copied to clipboard
| Challenge: | ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware . |
| Approach: | They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment. |
| Outcome: | The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment. |
When Large Language Models Meet Speech: A Survey on Integration Approaches (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have spurred interest in expanding their application beyond text-based tasks. |
| Approach: | They propose to categorize the integration of speech with LLMs into three main approaches . they demonstrate how these methods are applied across various speech-related applications . |
| Outcome: | The proposed methods are applied across speech-related applications and highlight the challenges in this field to offer inspiration for future research. |
Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing document-level relation extraction methods do not distinguish between mention-level features and entity-level feature . document-based methods are more challenging because of multiple mentions of entities. |
| Approach: | They propose a method which selectively attentions different entity mentions with respect to candidate relations and performs relation-specific representations of entities. |
| Outcome: | The proposed method improves relation-specific representations of entities on two benchmark datasets. |
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. |
| Approach: | They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% . |
| Outcome: | The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
Copied to clipboard
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)
Copied to clipboard
Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang
| Challenge: | Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities. |
| Approach: | They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments. |
| Outcome: | The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments. |
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model. |
| Approach: | They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights. |
| Outcome: | The proposed method matches or outperforms existing methods in safety alignment and long-form question answering. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
Copied to clipboard
Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)
Copied to clipboard
Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou
| Challenge: | Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems. |
| Approach: | They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets. |
| Outcome: | The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization. |
Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | State-of-the-art vision-language models require massive scaling that limits practical deployment. |
| Approach: | They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT). |
| Outcome: | Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks. |
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (2024.findings-acl)
Copied to clipboard
| Challenge: | Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios. |
| Approach: | They propose to use OCR/ASR-based generation to refine Chinese Spelling Correction models on random replacement-based corpora and filter them based on prediction confidence. |
| Outcome: | The proposed model outperforms existing models on three widely-used benchmarks while significantly alleviating over-correction. |
Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal Machine Translation (MMT) is effective in resolving linguistic ambiguities, but visual information often introduces redundancy or noise, potentially impairing translation quality. |
| Approach: | They propose a semantic-augmented framework that integrates "Imagination" and "Contemplation" they first generate synthetic images from source text and align them with authentic images via an optimal transport loss . |
| Outcome: | The proposed framework outperforms baselines on translation datasets with visually ambiguous or weakly correlated content. |
Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit impressive abilities in various domains such as text generation, instruction following, and reasoning. |
| Approach: | They propose a method to decompose the activations of Large Language Models into a sparse linear combination of SAE features. |
| Outcome: | The proposed method shows that some features are strongly related to specific languages, while others are unaffected by ablating them. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
Copied to clipboard
Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
SLoRA: Balancing Plasticity and Forgetting in Large Language Models for Continual Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse tasks through large-scale pretraining. |
| Approach: | They propose a framework that filters noisy components from LoRA updates via subspace similarity with the base model. |
| Outcome: | The proposed framework improves accuracy by 12%, reduces forgetting by 29%, and filters out over 30% of LoRA parameters identified as noisy. |
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models (2026.findings-acl)
Copied to clipboard
Lifan Zheng, Xue Yang, Jiawei Chen, Chenyan WU, Jingyuan Zhang, Fanheng Kong, Xinyi Zeng, Xiang Chen, Yu Tian
| Challenge: | Current methods for editing personality traits in large language models can change personalities but reduce performance. |
| Approach: | They propose a novel paradigm for personality editing that locates and edits LLM neurons and enables competitive personality control at inference time. |
| Outcome: | Experiments on LLaMA-3-8B-Instruct and Qwen2.5-7B-instruct show that the proposed approach can improve performance and improve performance. |
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest. |
| Approach: | They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding. |
| Outcome: | The proposed method can better capture multi-grained user interest in news. |
How Much Does Nonverbal Communication Conform to Entropy Rate Constancy?: A Case Study on Listener Gaze in Interaction (2024.findings-acl)
Copied to clipboard
| Challenge: | Whether the Entropy Rate Constancy principle applies to nonverbal communication signals is still under investigation. |
| Approach: | They perform empirical analyses of video-recorded dialogue data and investigate whether listener gaze adheres to the Entropy Rate Constancy principle. |
| Outcome: | The results show that the ERC principle holds for listener gaze, and that linguistic factors syntactic complexity and turn transition potential are weakly correlated with local entropy of listener gaze. |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
Copied to clipboard
Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work reveals only randomly permuted activations to the client, allowing adversaries to extract model weights. |
| Approach: | They propose an attack that aligns differently shuffled activations to a common permutation and exploits them to extract model weights. |
| Outcome: | The proposed attack can align shuffled activations to a common permutation and exploit them to extract model weights with a query cost of approximately $1. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
Copied to clipboard
Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
Word sense extension (2023.acl-long)
Copied to clipboard
| Challenge: | a long-standing effort in natural language processing has focused on word sense disambiguation, but little has been explored about how word meaning is extended toward new context. |
| Approach: | They propose a framework that partitions a word type into two pseudo-tokens that mark its different senses and infers whether the meaning can be extended to convey the sense denoted by the token. |
| Outcome: | The proposed framework outperforms other models in predicting plausible novel senses for over 7,500 English words. |
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)
Copied to clipboard
Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou
| Challenge: | Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks. |
| Approach: | They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets . |
| Outcome: | The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages. |
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)
Copied to clipboard
Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang
| Challenge: | Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios. |
| Approach: | They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts. |
| Outcome: | The proposed model performs comparable to state-of-the-art large models on the test set. |
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization via Multi-LLMs (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing jailbreak research exhibits limitations in universality, validity, and efficiency . Existing methods for jailbreaking LLMs have limited validity and effectiveness . |
| Approach: | They propose a black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts. |
| Outcome: | The proposed method efficiently identifies security vulnerabilities across various LLMs, achieving an average success rate of over 80% with fewer than 10 queries. |
MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition (2024.findings-emnlp)
Copied to clipboard
Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan
| Challenge: | emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. |
| Approach: | They propose a two-stage compression method tailored for Mixture of Experts to reduce the model size and decrease the computational cost. |
| Outcome: | The proposed method reduces model size and improves inference efficiency while maintaining performance in various zero-shot tasks. |
CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have focused on Knowledge Graph Completion as an end in itself, neglecting its potential impact on subsequent applications. |
| Approach: | They propose a benchmark to assess the impact of representative KGC methods on Knowledge Graph Question Answering (KGQA) they use a knowledge graph with 3 million triplets across 5 distinct domains to evaluate their results. |
| Outcome: | The proposed benchmark compares four well-known methods with two state-of-the-art systems to assess the impact of incomplete knowledge graphs on KGQA. |
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)
Copied to clipboard
Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu
| Challenge: | Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data. |
| Approach: | They propose a prompt tuning approach to extract attributes from product information using mixed prompts. |
| Outcome: | The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting. |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
Copied to clipboard
| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory (2024.findings-emnlp)
Copied to clipboard
Suyeon Lee, Sunghwan Kim, Minju Kim, Dongjin Kang, Dongil Yang, Harim Kim, Minseok Kang, Dayi Jung, Min Kim, Seungbeen Lee, Kyong-Mee Chung, Youngjae Yu, Dongha Lee, Jinyoung Yeo
| Challenge: | Existing models that use large language models are not available due to ethical concerns, and data privacy concerns are a concern. |
| Approach: | They propose a multi-turn dialogue dataset that emulates real-life counseling interactions using the goal-oriented approach of Cognitive Behavioral Therapy (CBT). |
| Outcome: | The proposed model outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent. |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
Copied to clipboard
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing sparsity methods lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. |
| Approach: | They propose a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. |
| Outcome: | The proposed framework achieves 20% average speedup with less than 2% accuracy degradation outperforming Griffin and TT. |
The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (2025.emnlp-main)
Copied to clipboard
| Challenge: | Embedding-based similarity metrics can be influenced by content dimensions and spurious attributes like the text’s source or language. |
| Approach: | They propose a debiasing algorithm that removes observed confounders from encoder representations and removes them from the encoder. |
| Outcome: | The proposed method improves on out-of-distribution benchmarks and on benchmarks, but performance is not affected. |
Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality. |
| Approach: | They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm. |
| Outcome: | The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
Copied to clipboard
Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract relationships from texts depend on memory size and replay these memorized samples in subsequent tasks. |
| Approach: | They propose to use a model to extract relations between entities from texts where the samples of different relations are delivered into the model continuously. |
| Outcome: | The proposed model outperforms the state-of-the-art models and avoids catastrophic forgetting. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
Copied to clipboard
Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
PUPPET: Neural-Symbolic Standardized Patients for Mental Health (2026.acl-long)
Copied to clipboard
Chen Xu, Yu ji, Zhenyu Lv, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong, Bin Hu
| Challenge: | Existing LLM-based training approaches lack faithful responses to clinical errors and explainable feedback. |
| Approach: | They propose a neural-symbolic virtual standardized patient governed by an OBSERVE-THINK-BEHAVE architecture that embeds LLM reasoning into a symbolic system where experts implant causal associations between intervention logic and patient mental states. |
| Outcome: | The proposed model outperforms baselines in faithfulness and pedagogical value. |
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues (P19-1)
Copied to clipboard
| Challenge: | Existing studies have shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. |
| Approach: | They propose to model CEO’s verbal (from text) and vocal (from audio) information in a conference call. |
| Outcome: | The proposed model reduces the error rate by comparing CEO’s verbal and vocal information in a conference call with other models. |
A Query-Response Framework for Whole-Page Complex-Layout Document Image Translation with Relevant Regional Concentration (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for document image translation rely on the vanilla encoder-decoder paradigm . a novel dynamic aggregation mechanism is designed to enhance the text semantics in query features toward translation. |
| Approach: | They propose a Query-Response DIT framework that reformulates the DIT task into a parallel response/translation process of multiple queries. |
| Outcome: | The proposed framework improves translation quality on four translation directions on three benchmarks. |
MIC: A Multi-task Interactive Curation Tool (2022.emnlp-demos)
Copied to clipboard
| Challenge: | MIC is a human-machine collaborative curation tool for multiple NLP tasks. |
| Approach: | They propose to use a multi-task interactive curation tool for multiple NLP tasks to help solve pain points in real NLP. |
| Outcome: | The proposed tool supports multiple projects with multiple users and allows for collaborative annotations. |
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)
Copied to clipboard
Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang
| Challenge: | Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases. |
| Approach: | They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. |
| Outcome: | The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have improved the performance of Large language models on well-defined mathematical benchmarks, but they often overlook ill-defined problems. |
| Approach: | They develop a large-scale benchmark that contains over 5,000 ill-defined mathematical problems. |
| Outcome: | The proposed framework improves the accuracy of identifying unsolvable problems by at least 12% across different LLMs, thus achieving stronger robust mathematical reasoning ability. |
OASum: Large-Scale Open Domain Aspect-based Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing generic summarization methods generate only one summary for all different requests which is not optimal for diverse demands. |
| Approach: | They use crowd-sourced knowledge on Wikipedia to create a large-scale open-domain aspect-based summarization dataset with 1 million different aspects on 2 million Wikipedia pages. |
| Outcome: | The proposed model can generate diverse aspect-based summarizations on Wikipedia with zero/few-shot and fine-tuning on seven downstream datasets. |
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Backchannels and fillers are important linguistic expressions in dialogue, but often ignored in modern transformer-based language models. |
| Approach: | They use clustering analysis to learn backchannels and fillers in dialogues in English and Japanese and use natural language generation metrics to confirm this. |
| Outcome: | The proposed models can learn representations of backchannels and fillers using three fine-tuning strategies. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)
Copied to clipboard
| Challenge: | Automated program repair (APR) aims to find an automatic solution to program language bugs without human intervention. |
| Approach: | They propose a grammar-based rule-rule model which regards the repair process as the transformation of grammar rules and employs a tree-based self-attention approach to guarantee grammar correctness. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a Java dataset in terms of generated code accuracy. |
Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel at complex math but fail on basic addition, raising the question of whether they grasp rules or are merely reproducing patterns. |
| Approach: | They systematically probe LLMs’ understanding of two-integer addition by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping and consistent accuracy scaling with operand length. |
| Outcome: | The proposed models achieve high numeric accuracy but fail basic addition tasks. |
UNCLE: Benchmarking Uncertainty Expressions in Long-Form Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing work lacks direct and fair evaluation of Large Language Models’ ability to express uncertainty effectively in long-form generation. |
| Approach: | They propose a benchmark to evaluate uncertainty expression in both long- and short-form question answering (QA) they propose prompt-based and training-based methods to improve models’ performance. |
| Outcome: | The proposed method mitigates this issue but a misalignment persists in uncertainty expression between long- and short-form generation. |
Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. |
| Approach: | They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step. |
| Outcome: | The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed. |
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)
Copied to clipboard
Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu
| Challenge: | Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. |
| Approach: | They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects. |
| Outcome: | The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation. |
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) sometimes produce untruthful responses despite knowing the correct knowledge. |
| Approach: | They propose an inference-time intervention method to activate the truthfulness of Large Language Models (LLMs) by editing the features within LLM’s internal representations that govern the truthful. |
| Outcome: | The proposed method improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. |
IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)
Copied to clipboard
| Challenge: | Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details. |
| Approach: | They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning . |
| Outcome: | IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation. |
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)
Copied to clipboard
Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. |
| Approach: | They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track. |
| Outcome: | Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. |
Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features. |
| Approach: | They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies. |
| Outcome: | The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets . |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
Copied to clipboard
Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are easily misled by untruthful contexts provided by users or knowledge augmentation tools, leading to hallucinations. |
| Approach: | They propose a lightweight method to adaptively recognize and mask untruthful context from the inputs and a new evaluation metric to further study the LLMs’ ability to accept truthful information and resist untrusted information. |
| Outcome: | The proposed method can detect and mask untruthful context from the inputs and significantly improve the quality of LLMs’ responses when presented with misleading information. |
LoGU: Long-form Generation with Uncertainty Expressions (2025.acl-long)
Copied to clipboard
Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang, Sen Yang, Nigel Collier, Dong Yu, Deqing Yang
| Challenge: | Large Language Models (LLMs) generate factually incorrect content, i.e., hallucinations, despite impressive performance. |
| Approach: | They propose a framework to enable models to express uncertainty when unsure . they propose atomic claims to refine uncertainty and refine it using supervised fine-tuning and direct preference optimization to enhance uncertainty expression. |
| Outcome: | The proposed framework significantly improves accuracy, reduces hallucinations, and maintains comprehensiveness of responses. |
Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (2025.acl-long)
Copied to clipboard
| Challenge: | Document Image Machine Translation (DIMT) faces generalization challenges due to limited training data and the complex interplay between visual and textual information. |
| Approach: | They propose a single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs) this framework aligns an imageonly encoder with multimodal representations of an MLLM pre-trained on large-scale document image datasets. |
| Outcome: | The proposed framework improves translation quality in cross-domain generalization and challenging document image scenarios. |
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback (2025.naacl-long)
Copied to clipboard
| Challenge: | Generating multiple-choice questions (MCQG) for professional exams is challenging due to outdated knowledge, hallucination issues, and prompt sensitivity. |
| Approach: | They propose a framework for converting medical cases into high-quality USMLE-style questions using a self-refine-based framework. |
| Outcome: | The proposed framework improves human expert satisfaction regarding quality and difficulty of medical questions. |
Attacking Vision-Language Computer Agents via Pop-ups (2025.acl-long)
Copied to clipboard
| Challenge: | Existing tools for analyzing and testing VLMs are lacking in understanding what types of attacks are possible and what types are effective. |
| Approach: | They propose to integrate pop-ups into existing agent testing environments to attack VLM agents by ignoring them. |
| Outcome: | The proposed attack success rate is 86% and decreases by 47% when integrating pop-ups into existing agent testing environments. |
Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
Copied to clipboard
Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing TIMT tasks focus on text-line-level images. |
| Approach: | They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation. |
| Outcome: | The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation. |
Generating Classical Chinese Poems from Vernacular Chinese (D19-1)
Copied to clipboard
| Challenge: | Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems. |
| Approach: | They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors . |
| Outcome: | The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems. |
You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)
Copied to clipboard
| Challenge: | Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary. |
| Approach: | They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution. |
| Outcome: | The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity. |
Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs. |
| Approach: | They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal . |
| Outcome: | The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective . |
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)
Copied to clipboard
Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability. |
| Approach: | They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain. |
| Outcome: | The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset. |
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)
Copied to clipboard
| Challenge: | XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications. |
| Approach: | They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy . |
| Outcome: | The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences. |
We Need to Talk About Reproducibility in NLP Model Comparison (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies show that standard splits produce low reproducible and unreliable conclusions . reproducibility of empirical experimental conclusions is a problem in NLP domain . |
| Approach: | They propose to transform the reproducibility of a model comparison into a probabilistic function . they propose to use a regularized corpus splitting strategy to estimate the model's performance . |
| Outcome: | The proposed estimator achieves a high SNR and significantly increases reproducibility. |
Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Prompt tuning is effective in extracting knowledge from foundation models, but its effectiveness is uncertain. |
| Approach: | They propose a parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. |
| Outcome: | The proposed approach improves performance across a wide range of tasks including NLP, vision recognition, and vision-language tasks. |
From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry. |
| Approach: | They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools. |
| Outcome: | The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research. |
UniTE: Unified Translation Evaluation (2022.acl-long)
Copied to clipboard
| Challenge: | Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities . |
| Approach: | They propose a unified framework engaged with abilities to handle all three evaluation tasks. |
| Outcome: | The proposed framework can universally surpass state-of-the-art or winner methods across tasks. |
ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)
Copied to clipboard
Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code. |
| Approach: | They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing open-source code translation models on two metrics. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
Copied to clipboard
Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)
Copied to clipboard
| Challenge: | relying on large language models for information has raised concerns about reliability and accuracy of outputs. |
| Approach: | They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process. |
| Outcome: | The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility. |
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)
Copied to clipboard
Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
| Challenge: | Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning. |
| Approach: | They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks. |
| Outcome: | The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
Copied to clipboard
Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
STAF: Pushing the Boundaries of Test-Time Adaptation towards Practical Noise Scenarios (2024.lrec-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on NLP tasks . however, when the training domain and testing domain are taken from different distributions, the deployed model often violates this assumption. |
| Approach: | They propose a Stable Test-time Adaptation Framework to stabilize the adaptation process. |
| Outcome: | The proposed framework boosts model robustness to noise distribution shifts while minimizing error accumulation and catastrophic forgetting. |
Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)
Copied to clipboard
Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang
| Challenge: | Existing methods for training effective AI agents often resort to synthetic data generation. |
| Approach: | They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance . |
| Outcome: | The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset. |
Explain the Synth: Interpretable Evaluation of LLM Data Synthesis (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly used to generate tabular data. |
| Approach: | They propose a framework that uses a rule-based model as a shared explanatory language to examine the explanation of real versus synthetic data. |
| Outcome: | The proposed framework compares the explanatory structure induced by real versus synthetic data. |
Red Teaming Large Reasoning Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, but they introduce safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies. |
| Approach: | They propose a unified benchmark to assess the trustworthiness of Large Reasoning Models. |
| Outcome: | The proposed benchmark evaluates truthfulness, safety and efficiency on 26 models. |
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021.naacl-main)
Copied to clipboard
Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
| Challenge: | Existing work on meeting summarization tasks is limited to short summaries that cover all the content of a meeting. |
| Approach: | They propose a query-based multi-domain meeting summarization task that generates a single short summary of meetings based on a transcript. |
| Outcome: | The proposed task is based on 1,808 query-summary pairs over 232 meetings in multiple domains. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
Copied to clipboard
Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)
Copied to clipboard
Jiawei Liu, Xun Gong, Muli Yang, Xingrui Yu, Fen Fang, Xulei Yang, Ivor Tsang, Yunfeng hu, Hong Chen, Qing Guo
| Challenge: | Recent large language model-based AD research offers new avenues to address this challenge. |
| Approach: | They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control. |
| Outcome: | The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
Copied to clipboard
Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
Copied to clipboard
Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
Copied to clipboard
Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
Text Style Transfer Back-Translation (2023.acl-long)
Copied to clipboard
Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
Copied to clipboard
Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies in multimodal sentiment analysis only use unified multimodal annotations, which do not reflect the independent sentiment of single modalities. |
| Approach: | They propose a Chinese single- and multi-modal sentiment analysis dataset with multimodal and independent unimodal annotations that can be used to study the interaction between modalities. |
| Outcome: | The proposed methods achieve state-of-the-art performance and learn more distinctive unimodal representations. |
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing goal-oriented dialogue datasets focus on identifying slots and values, but in reality, customer service agents follow multi-step procedures derived from explicit company policies. |
| Approach: | They propose to use a fully-labeled dataset to study customer service dialogue systems in real-world scenarios. |
| Outcome: | The proposed dataset outperforms existing models but still lacks 50.8% absolute accuracy to reach human-level performance on the dataset. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
Copied to clipboard
Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities. |
| Approach: | They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one . |
| Outcome: | The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs. |
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)
Copied to clipboard
| Challenge: | Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good. |
| Approach: | They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity. |
| Outcome: | The proposed system could change people's opinions and actions for social good. |
Improving Unsupervised Keyphrase Extraction using Background Knowledge (L18-1)
Copied to clipboard
| Challenge: | Existing methods of keyphrase extraction are supervised and unsupervised . Topical PageRank uses topical information to extract the top topics of a document . |
| Approach: | They propose an unsupervised method for keyphrase extraction based on Wikipedia . they construct a semantic graph and transform the extraction problem into an optimization problem . |
| Outcome: | The proposed method improves over other state-of-the-art models by more than 2% in F1-score. |
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. |
| Approach: | They propose a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities by extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy. |
| Outcome: | The proposed framework achieves significant improvement across 72 x2x directions while generalizing to enhance en2x performance. |
Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions (2023.findings-eacl)
Copied to clipboard
| Challenge: | Extreme Multi-label text classification (XMTC) is a tough challenge due to the sheer size of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions. |
| Approach: | They propose to use a trained bag-of-words classifier to generate pseudo label descriptions from a training bag- of-word classifier. |
| Outcome: | The proposed approach outperforms the existing models in the tail label prediction problem and achieves state-of-the-art (SOTA) performance on XMTC benchmark datasets. |
Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets. |
| Approach: | They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC). |
| Outcome: | The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset. |
Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues (2025.acl-long)
Copied to clipboard
Youngmin Kim, Jiwan Chung, Jisoo Kim, Sunghyun Lee, Sangkyu Lee, Junhyeok Kim, Cheoljong Yang, Youngjae Yu
| Challenge: | Existing large language models fail to incorporate nonverbal elements into conversational experiences. |
| Approach: | They propose a multimodal language model that generates nonverbal cues alongside text . their dataset is annotated with time-aligned text, facial expressions, and body language . |
| Outcome: | The proposed model generates nonverbal languages and text, corresponding to conversational input. |
Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data (2024.findings-acl)
Copied to clipboard
Yanda Li, Chi Zhang, Gang Yu, Wanqi Yang, Zhibin Wang, Bin Fu, Guosheng Lin, Chunhua Shen, Ling Chen, Yunchao Wei
| Challenge: | OpenAI's GPT-4 has demonstrated remarkable multimodal capabilities, but specific mechanics of GPT4 remain unknown. |
| Approach: | They propose a data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. |
| Outcome: | The proposed method improves on ten commonly assessed models and provides greater flexibility compared to existing methods. |
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information (2023.acl-long)
Copied to clipboard
| Challenge: | Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. |
| Approach: | They propose an unsupervised approach that incorporates sense definitions when sense information of the answer is not provided. |
| Outcome: | The proposed approach improves the performance of the existing definition generation method in OOD examples. |
Multilingual Knowledge Graph Completion with Language-Sensitive Multi-Graph Attention (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multilingual knowledge graph completion have two drawbacks: alignment dependency and training inefficiency. |
| Approach: | They propose a multilingual knowledge graph completion framework with language-sensitive multi-graph attention to predict missing links on all given KGs. |
| Outcome: | The proposed model improves on the DBP-5L and E-PKG datasets. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
Copied to clipboard
Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods focus on single-step reasoning, ignoring logical dependencies between steps. |
| Approach: | They propose a method that maximizes a structure-based return to facilitate structured reasoning and explanation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on EntailmentBank and STREET benchmarks. |
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)
Copied to clipboard
Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
| Challenge: | Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations. |
| Approach: | They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation. |
| Outcome: | The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement. |
CCIM: Cross-modal Cross-lingual Interactive Image Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on text image machine translation (TIMT) lacks recognized source language information resulting in a decrease in translation performance. |
| Approach: | They propose a cross-modal cross-lingual interactive model which incorporates source language information by synchronizing source and target language results. |
| Outcome: | The proposed model outperforms end-to-end models and has faster decoding speed with smaller model size than cascade models. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
Copied to clipboard
Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
| Outcome: | The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
Copied to clipboard
| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages . |
| Approach: | They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA. |
| Outcome: | The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
Copied to clipboard
Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing LLMs break down on long-horizon tasks due to unbounded context growth and accumulated errors. |
| Approach: | They propose a framework that externalizes persistent state into a file-centric state abstraction and keeps the agent’s reasoning context strictly bounded regardless of task duration. |
| Outcome: | Experiments on DeepResearch and an 80-paper literature review show that the proposed framework maintains higher long-horizon coverage than baseline models without task-specific fine-tuning. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
Copied to clipboard
Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing Large language model agents rely on increasingly long interaction histories, resulting in high computational cost and limited scalability. |
| Approach: | They propose a hierarchical reinforcement learning framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories. |
| Outcome: | The proposed framework outperforms baselines on ScienceWorld and ALFWorld benchmarks in terms of performance and generalization while reducing token usage. |
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models (2025.acl-long)
Copied to clipboard
Hieu Tran, Zonghai Yao, Zhichao Yang, Junda Wang, Yifan Zhang, Shuo Han, null Feiyun Ouyang, Hong Yu
| Challenge: | Existing work aims to improve reasoning accuracy and factual integrity across large language models for knowledge-intensive tasks such as medical and commonsense reasoning. |
| Approach: | They propose a versatile extension to the mutual reasoning framework (rStar) that enhances reasoning accuracy and factual integrity across large language models. |
| Outcome: | The proposed extension to the mutual reasoning framework improves reasoning accuracy and factual integrity across large language models for complex, knowledge-intensive tasks. |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
Copied to clipboard
Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language (2024.findings-emnlp)
Copied to clipboard
Soon Poh, Sze Jue Yang, Jeraelyn Tan, Lawrence Chieng, Jia Tan, Zhenyu Yu, Foong Mun, Chee Seng Chan
| Challenge: | Large Language Models (LLMs) and Large Vision Language Model (LVLMs) exhibit advanced proficiency in language reasoning and comprehension across a wide array of languages. |
| Approach: | They propose to use a multitask language understanding benchmark specifically designed for the Malay language to assess their proficiency. |
| Outcome: | The proposed model performs well in well-resourced languages, but in low-resource languages such as Bahasa Melayu, they are less studied due to a lack of studies and benchmarks. |
Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to retrieval-agmented generation fail to generalize effectively in black-box scenarios. |
| Approach: | They propose a framework that leverages the semantic importance of words to dynamically adjust retrieval thresholds and filter information. |
| Outcome: | The proposed framework achieves the highest score on four long-form, knowledge-intensive generation datasets. |
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to learn multi-modal tasks are based on chain-of-thought . however, human thought processes are non-linear and employ dynamic adjustment and updating mechanisms. |
| Approach: | They propose a chain-of-thought technique that adjusts the length of the chain to improve the performance of generated prompts. |
| Outcome: | The proposed model improves multi-modal representation learning in visual, visual, and audio-visual tasks and also has good domain generalization performance due to better reasoning. |
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)
Copied to clipboard
Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
| Challenge: | Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias . |
| Approach: | They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a . |
| Outcome: | Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets. |
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks . |
| Approach: | They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions. |
| Outcome: | The proposed approach achieves state-of-the-art on two benchmark datasets. |
"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) are reactive and respond only when prompted, limiting their effectiveness in collaborative settings. |
| Approach: | They introduce a proactive LLM assistant designed to enhance biomedical collaboration between AI systems and human experts through timely, context-aware interventions. |
| Outcome: | The proposed model outperforms baselines in intervention precision and collaborative task utility, highlighting the potential of proactive LLMs as intelligent scientific assistants. |
Gunrock: A Social Bot for Complex and Engaging Long Conversations (D19-3)
Copied to clipboard
Dian Yu, Michelle Cohn, Yi Mang Yang, Chun Yen Chen, Weiming Wen, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, Shreenath Iyer, Giritheja Sreenivasulu, Sam Davidson, Ashwin Bhandare, Zhou Yu
| Challenge: | Gunrock is a speech-based social chatbot that can be used to understand complex sentences and have in-depth conversations. |
| Approach: | They propose a system that allows users to understand complex sentences and have in-depth conversations in open domains. |
| Outcome: | The proposed system produces longer sentences, which are directly related to user engagement (e.g., ratings, number of turns). |
Importance-based Neuron Allocation for Multilingual Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multilingual neural machine translation tend to preserve general knowledge, but ignore language-specific knowledge. |
| Approach: | They propose to divide model neurons into general and language-specific parts based on their importance across languages. |
| Outcome: | The proposed model can preserve general knowledge but ignore language-specific knowledge on several languages, and is universal and cost-effective. |
MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios. |
| Approach: | They propose a Continual Multimodal Entity and Relation Joint Extraction task and a Multimodal Prompt-based Boundary-enhanced Continuum framework that stores task-specific knowledge via learnable multimodal prompts. |
| Outcome: | The proposed framework outperforms baseline methods in real-world scenarios by 5.5% and 7.2%. |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
Copied to clipboard
Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning. |
| Approach: | They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs). |
| Outcome: | The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions. |
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
Copied to clipboard
| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
Copied to clipboard
Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)
Copied to clipboard
| Challenge: | Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data. |
| Approach: | They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space . |
| Outcome: | The proposed method can learn weights for words to achieve fine-grained adaptation. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
Copied to clipboard
Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge editing methods suffer from performance degradation in batch knowledge editing. |
| Approach: | They propose an orthogonal representation editing method which decouples semantic entanglement from edit vectors and enforcing orthogonals on edit vector. |
| Outcome: | The proposed method outperforms existing methods and achieves superior performance in cross-lingual knowledge editing scenarios. |
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)
Copied to clipboard
Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang
| Challenge: | TableLLM is a robust large language model capable of handling tabular data manipulation tasks. |
| Approach: | They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy. |
| Outcome: | The proposed model has 8 billion parameters and is capable of handling tabular data tasks. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage. |
| Approach: | They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE. |
| Outcome: | The proposed framework can be used to infer whether a specific relation exists in a private client. |
MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to Multimodal Relation Extraction focus on single image scenarios . current approaches focus on text paired with a single image, ignoring valuable insights provided by remaining images. |
| Approach: | They propose a human-annotated dataset that includes multi-image and single-image instances for relation extraction. |
| Outcome: | The proposed model significantly improves relation extraction in multi-image scenarios. |
S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation (2026.findings-acl)
Copied to clipboard
| Challenge: | S2ST-Omni integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Approach: | They propose a compositional S2ST framework that integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Outcome: | The proposed framework outperforms existing frameworks in translation and synthesis . it integrates a speech-to-text translation frontend with a plug-and-play text-tospeech backend . |
Relabeling Minimal Training Subset to Flip a Prediction (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to identify and relabel training subsets that can flip a prediction are not efficient, argues a new study. |
| Approach: | They propose an algorithm to identify and relabel the smallest training subset St needed to flip a prediction. |
| Outcome: | The proposed algorithm can flip a prediction on a test point xt with 2% of training points . the proposed method can be used for multiple purposes including evaluating model robustness . |
MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)
Copied to clipboard
Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang
| Challenge: | Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. |
| Approach: | They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. |
| Outcome: | Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. |
Measuring Human Contribution in AI-Assisted Content Generation (2026.acl-long)
Copied to clipboard
Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
| Challenge: | generative AI has created a new way to generate content with humans . varying degrees of human contribution in content generation poses significant challenges for the delineation of originality . |
| Approach: | They propose a framework to measure human contribution in AI-assisted content generation by calculating mutual information between human input and AI-aided output relative to self-information of AI-assist output. |
| Outcome: | The proposed measure discriminates between varying degrees of human contribution across multiple creative domains and is validated in real-world applications. |
Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing research on text image machine translation (TIMT) is divided into two types: Cascade methods combine text image recognition and MT models to recognize source language text images. |
| Approach: | They propose a method which is optimized with hierarchical parental supervision to improve translation performance. |
| Outcome: | The proposed method significantly outperforms existing methods on synthetic and real-world tests on both synthetic and realistic images. |
How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factors (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on the effect of environmental variation on web agents have focused on robustness to adversarial attacks with less attention to agents’ preferences in benign scenarios. |
| Approach: | They propose a controlled evaluation pipeline to quantify how visual attributes influence web-agent decision-making by comparing variants and browsing interactions. |
| Outcome: | Extensive experiments on 8 variant families, 5 real-world websites and 4 representative web agents show that background color contrast, item size, position, and card clarity have a strong influence on agents’ actions, whereas font styling, text color, and item image clarity exhibit minor effects. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
Copied to clipboard
| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships. |
| Approach: | They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events. |
| Outcome: | The proposed system can make intelligent decisions regarding online events at a manageable cost. |
Improving MLLM’s Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency (2025.findings-acl)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges. |
| Approach: | They propose a novel fine-tuning paradigm that allows the model to generate OCR text before producing translation text, which allows it to leverage its strong monolingual OCR ability while learning to translate text across languages. |
| Outcome: | The proposed model can leverage its strong monolingual OCR ability while learning to translate text across languages. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
Copied to clipboard
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)
Copied to clipboard
Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu
| Challenge: | Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions . |
| Approach: | They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers . |
| Outcome: | The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. |
Inference Compute-Optimal Video Vision Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Using video vision language models, inference costs are often more expensive than finetuning. |
| Approach: | They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models. |
| Outcome: | The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets. |
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey. |
| Approach: | They propose a model to track customer satisfaction in chatbots using a dialogue-level classification module to represent the dynamic satisfaction states at each turn. |
| Outcome: | The proposed model outperforms baselines and shows that it significantly outperformed multiple baselines. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
Copied to clipboard
Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)
Copied to clipboard
Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li
| Challenge: | Existing methods overlook the challenge of effectively transforming structure information from NL to SQL. |
| Approach: | They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL. |
| Outcome: | The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
Copied to clipboard
Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| Challenge: | Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech . |
| Approach: | They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling. |
| Outcome: | The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness. |
Uncertainty-Aware Curriculum Learning for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages. |
| Approach: | They propose to use an uncertainty-aware curriculum learning approach to assess data difficulty and model competence to provide examples in an easy-to-hard order at different training stages. |
| Outcome: | The proposed approach outperforms baseline and related methods on translation quality and convergence speed. |
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment (2026.acl-long)
Copied to clipboard
| Challenge: | Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences. |
| Approach: | They propose a rubric-based reward model that uses a large collection of prompt, rubric pairs to generate a scalar score or preference label for each response. |
| Outcome: | The proposed model surpasses strong size-matched baselines by 8.4% across multiple benchmarks. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
Copied to clipboard
Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
| Challenge: | Large language models produce content lacking pedagogical depth when asked to generate lessons . |
| Approach: | They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications. |
| Outcome: | The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
Copied to clipboard
Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems (2026.acl-long)
Copied to clipboard
| Challenge: | EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments. |
| Approach: | They propose a model routing paradigm that transcends static, pre-defined model assignments. |
| Outcome: | Experiments on GAIA and BrowseComp+ show that EvoRoute reduces execution cost and latency by over 70%. |
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual natural language inference lack annotated parallel corpora. |
| Approach: | They propose a new prompt learning framework with the Multilingual Verbalizer for XNLI that uses a multilingual verbalizer to align the representations of original and augmented multilingual questions into a unified semantic space with consistency regularization. |
| Outcome: | The proposed framework outperforms existing methods under few-shot and full-shot cross-lingual transfer settings. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time. |
| Approach: | They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge. |
| Outcome: | The proposed approach improves performance on knowledge-intensive NLP tasks. |
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models with excessive information are inefficient and costly . |
| Approach: | They propose to integrate a Dialogue State Tracker with Slot Attention and Slot Information Sharing to reduce redundant information’s interference and improve long dialogue context tracking. |
| Outcome: | The proposed model significantly outperforms existing models on the MultiWOZ dataset. |
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)
Copied to clipboard
| Challenge: | Experimental results show that our method outperforms full-model tuning in few-shot abstractive summarization tasks. |
| Approach: | They propose a soft prompts architecture with prompt pre-training and prompt fine-tuning paradigm to support few-shot abstractive summarization. |
| Outcome: | The proposed model outperforms Prompt Tuning and Profix-Tuning on CNN/DailyMail and XSum datasets and outperfies Profix Tuning by a large margin. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods depend on predefined refusal templates detectable in output tokens or manual review. |
| Approach: | They propose a framework that optimally identifies steering directions and target layers using cosine similarity, entirely independent of output text. |
| Outcome: | The proposed framework achieves comparable steering effectiveness without any prior knowledge or assumptions of a model’s refusal behavior such as the use of certain refusal tokens. |
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)
Copied to clipboard
| Challenge: | MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains. |
| Approach: | They propose a method which combines tree search and verification to generate high-quality chain-of-thought data. |
| Outcome: | The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks. |
POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)
Copied to clipboard
Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou
| Challenge: | Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. |
| Approach: | They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. |
| Outcome: | The proposed model outperforms existing models and improves on annotated documents. |
URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios is a major challenge for end-to end spoken dialogue models. |
| Approach: | They propose to provide an extensive evaluation framework for end-to-end spoken dialogue models (SDMs) that includes both cognitive dimensions and paralinguistic cues . |
| Outcome: | The proposed benchmark is divided into two difficulty levels: basic track and pro track, each comprising 20 test sets, evaluating the spoken dialogue model’s abilities in U**nderstanding, **R**easoning, and **O**ral conversation. |
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping (2026.findings-acl)
Copied to clipboard
Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang, Zhenyu Zhang, Zefeng Zhang, Shuaiyi Nie, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang, Tingwen Liu
| Challenge: | Existing approaches to increasing effective depth of LLMs rely on parameter reuse, extending computation through recursive execution. |
| Approach: | They propose a training-time sparse depth allocation framework that progressively increases depth for a small subset of parameters as training evolves. |
| Outcome: | The proposed model outperforms existing approaches to increasing the effective depth of language models while reducing training FLOPs overhead from approximately 16–20% to only 1–3% relative to a standard Transformer backbone. |
From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)
Copied to clipboard
| Challenge: | Document Image Translation (DIT) aims to translate documents in images from one language to another. |
| Approach: | They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization. |
| Outcome: | The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality. |
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)
Copied to clipboard
Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu
| Challenge: | Existing code debugging benchmarks focus on the Code Repair stage of the code generation process. |
| Approach: | They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process. |
| Outcome: | The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5. |
DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing large language models exhibit unidirectional behavior when processing bidirectional relationships . authors propose a solution to alleviate the reversal curse in Diffusion LLMs . |
| Approach: | They propose a model that addresses the "reversal curse" of bidirectional behavior in large language models . they propose 'entity-aware training' and balanced data construction to alleviate asymmetry and missing relations . |
| Outcome: | The proposed model alleviates the "reversal curse" in Diffusion LLMs . the proposed model employs whole-entity masking to mitigate entity fragmentation . |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
Copied to clipboard
Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation. |
| Approach: | They propose a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences. |
| Outcome: | The proposed model outperforms the baseline model on R2D and D2A tasks by 10%. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
Copied to clipboard
Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)
Copied to clipboard
Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei
| Challenge: | Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. |
| Approach: | They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration. |
| Outcome: | The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks. |
A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)
Copied to clipboard
Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers. |
| Approach: | They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment. |
| Outcome: | The proposed model enables finer credit assignment, richer diagnostics, and improved robustness. |
Token-level Adaptive Training for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural machine translation (NMT). |
| Approach: | They propose to assign tokens with different frequencies to target tokens during training to encourage the model to pay more attention to low-frequency tokens. |
| Outcome: | The proposed model yields consistent improvements on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens. |
AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on factual correctness, semantic grounding, visual reasoning, or multimodal large language models. |
| Approach: | They propose a benchmark to assess AICA, which integrates perception, reasoning, and generation into a unified framework. |
| Outcome: | The proposed framework corrects intensity errors and significantly enhances descriptive depth. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets. |
| Approach: | They propose a human summarization preference alignment framework to align PLMs with human preferences. |
| Outcome: | The proposed framework narrows the gap between automatic and human evaluations by integrating three components. |
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies (2024.findings-emnlp)
Copied to clipboard
| Challenge: | CultureBank is a knowledge base built upon users’ self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. |
| Approach: | They construct a pipeline to construct cultural knowledge bases from different online communities on a massive scale. |
| Outcome: | The proposed pipeline improves cultural awareness of language models by evaluating them on two cultural tasks in a zero-shot setting. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
Copied to clipboard
Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
The Best of Both Worlds: Combining Parallel and Sequential Inference Scaling via Aggregation Fine-Tuning (2026.findings-acl)
Copied to clipboard
| Challenge: | Empirical results show that AFT-trained models achieve substantial gains with test-time scaling. |
| Approach: | They introduce a supervised fine-tuning paradigm where models synthesize multiple draft responses into a single, refined answer. |
| Outcome: | Empirical results show that AFT-trained models outperform baseline models while eliminating external guidance. |
Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)
Copied to clipboard
Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
| Challenge: | Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. |
| Approach: | They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. |
| Outcome: | The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks. |
Safe-FedLLM: Delving into the Safety of Federated Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on federated learning for large language models (FL) addresses privacy and data-silo issues in the training of large language model training. |
| Approach: | They propose a probe-based defense framework for FedLLM that constructs defenses across three levels: Step-Level, Client-Level and Shadow-Level. |
| Outcome: | The proposed framework improves FedLLM's robustness against malicious clients while maintaining competitive performance on benign data. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
Copied to clipboard
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. |
Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for active retrieval (AR) rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries. |
| Approach: | They propose a method to identify knowledge boundaries in active retrieval by retrieving historical queries as high-confidence in-context examples. |
| Outcome: | Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training. |
Unsupervised Speech-text word-level alignment with Dynamic Programming (2025.findings-naacl)
Copied to clipboard
| Challenge: | Word-level alignment in speech-text pretraining models is limited by word-level annotated data . authors propose an iterative training method for USDP that reduces the dependency on scarce annotation resources. |
| Approach: | They propose an Unsupervised Speech-text word-level alignment with Dynamic Programming (USDP) this method uses Dynamic programming principles to iteratively refine temporal alignment predictions . |
| Outcome: | The proposed method significantly improves on speech-text pretraining tasks compared to existing methods. |
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models (2025.acl-long)
Copied to clipboard
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Helen Li, Ziwei Liu, Kiyoharu Aizawa
| Challenge: | Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of Large Multimodal Models (LMMs). |
| Approach: | They propose a task to evaluate the robust understanding capability of Large Multimodal Models (LMMs) they introduce a benchmark to assess performance across various ability dimensions . |
| Outcome: | The proposed model can withhold answers when encountering unsolvable problems of MCQA, proving it understands the answer. |
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors (2026.acl-long)
Copied to clipboard
| Challenge: | Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model’s autonomous preferences. |
| Approach: | They propose to use response pattern similarity and action graph similarity to isolate non-mandatory behaviors from mandatory behaviors. |
| Outcome: | Evaluating 18 models from 8 providers on -Bench and 2-Bench against Claude Sonnet 4.5, the authors find that within-family model pairs score 5.9 pp higher in response pattern similarity and action graph similarity . |
MGCL: Multi-Granularity Clue Learning for Emotion-Cause Pair Extraction via Cross-Grained Knowledge Distillation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Traditional methods often rely on coarse-grained clause-level annotations, which overlook valuable fine-grain clues. |
| Approach: | They propose a method that captures fine-grained clues from a weakly-supervised perspective efficiently by using a teacher model to give sub-clause clues without needing fine-grain annotations. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering improved interpretability. |
R1-RE: Cross-Domain Relation Extraction with RLVR (2026.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) is a core task in natural language processing. |
| Approach: | They propose a supervised learning task for relation extraction (RE) based on annotation guidelines. |
| Outcome: | The proposed model achieves an average OOD accuracy of 70%, on par with leading proprietary models such as GPT-4o. |
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)
Copied to clipboard
Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
| Challenge: | Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges. |
| Approach: | They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. |
| Outcome: | The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs. |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
Copied to clipboard
Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
| Approach: | They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context. |
| Outcome: | The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
Copied to clipboard
Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing mPLM-based methods focus on designing costly model pre-training while ignoring equally crucial downstream adaptation. |
| Approach: | They propose a meta graph learning method that extracts meta-knowledge from historical CLT experiences to learn to cross-lingual transfer. |
| Outcome: | The proposed method can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks) it can also capture intrinsic language relationships to explicitly guide cross-linguistic transfer. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
Copied to clipboard
Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
Copied to clipboard
Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. |
| Approach: | They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence. |
| Outcome: | The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia. |
Multi-grained Named Entity Recognition (P19-1)
Copied to clipboard
| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
| Approach: | They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. |
| Outcome: | The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks. |
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. |
| Approach: | They propose a discourse-aware graph neural network (ERMC-DisGCN) that leverages contextual cues and speaker-specific features for ERMC. |
| Outcome: | The proposed method outperforms multiple baselines showing that discourse structures are of great value to ERMC. |
UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi
| Challenge: | Existing alignment paradigms for creative writing use static reward signals and supervised data. |
| Approach: | They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments. |
| Outcome: | The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references. |
How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation (2026.findings-acl)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. |
| Approach: | They reversely traced information flow across decoding, projection, and activation phases and found that CoT may serve as a decoding space pruner . |
| Outcome: | The proposed framework can be used to design more efficient and robust prompts. |
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)
Copied to clipboard
Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen
| Challenge: | Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems. |
| Approach: | They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests . |
| Outcome: | The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks. |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
Copied to clipboard
Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan
| Challenge: | Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback. |
| Approach: | They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations. |
| Outcome: | The proposed method significantly improves both automatic and human evaluations across four diverse LLMs. |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |
SetConv: A New Approach for Learning from Imbalanced Data (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for classification are biased towards the majority class when the Imbalance Ratio (IR) is high. |
| Approach: | They propose a set convolution operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution. |
| Outcome: | The proposed algorithm is permutation-invariant despite the order of inputs and shows superiority on multiple large-scale benchmark text datasets. |