Papers by Hu Chen
Towards Reward Fairness in RLHF: From a Resource Allocation Perspective (2025.acl-long)
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| Challenge: | if rewards are imperfect, they can adversely affect the alignment of large language models (LLMs). |
| Approach: | They propose a bias-agnostic method to address the issue of reward unfairness from a resource allocation perspective without specifically designing for each type of bias . they apply methods Fairness Regularization and Fairness Coefficient to achieve fairness in rewards. |
| Outcome: | The proposed method achieves fairness in rewards while minimizing biases . it can be applied to verification and reinforcement learning scenarios . |
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)
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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang
| Challenge: | ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines . |
| Approach: | They propose a Macro-to-Micro progressive learning approach that improves UIE without external information. |
| Outcome: | ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone. |
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on simple attribution that retrieves textual evidence as references. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to generate reliable attributions. |
| Outcome: | The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
SP3: Enhancing Structured Pruning via PCA Projection (2024.findings-acl)
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| Challenge: | Structured pruning is a widely used technique for reducing the size of pre-trained language models, but current methods overlook the potential of compressing the hidden dimension d in PLMs. |
| Approach: | They propose a structured pruning approach that projectes features into a space defined by principal components before masking the hidden dimension d in pre-trained language models. |
| Outcome: | Experiments on benchmarks show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy. |
Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)
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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou
| Challenge: | Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes. |
| Approach: | They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset. |
| Outcome: | The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets. |
Word Graph Guided Summarization for Radiology Findings (2021.findings-acl)
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| Challenge: | Existing studies focus on introducing salient word information to general text summarization framework to guide selection of key content in radiology findings. |
| Approach: | They propose a method for automatic impression generation using word graphs and a Word Graph guided Summarization model to capture critical words and their relations. |
| Outcome: | The proposed method is validated on two datasets, OPENI and MIMIC-CXR. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates (2022.emnlp-main)
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| Challenge: | Existing methods to improve confidence calibration of pre-trained language models are still a mystery. |
| Approach: | They propose a method that leverages model explanations to make models less confident with non-inductive attributions. |
| Outcome: | The proposed method improves confidence calibration in all settings and reduces calibration errors when combined with temperature scaling. |
Large Language Models are Limited in Out-of-Context Knowledge Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. |
| Approach: | They evaluated a dataset with seven representative OCKR tasks to assess their OCKr capabilities. |
| Outcome: | The model's OCKR abilities are limited regardless of whether the knowledge is trained in a separate or adjacent training setting. |
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for code editing, yet the full-code generation paradigm suffers from severe efficiency bottlenecks. |
| Approach: | They propose to use a structure-aware diff format to train LLMs to choose the most token-efficient format between a given diff format and full code. |
| Outcome: | The proposed approach matches the most token-efficient format with full-code generation while reducing latency and cost by over 30% on long-code editing tasks. |
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting (2022.naacl-main)
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Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
| Challenge: | Existing knowledge-grounded dialogue generation models only produce pedantic responses, which lacks emotion and attraction compared with the responses with polite style, positive and negative sentiments. |
| Approach: | They propose a method which generates responses via combing disentangled style templates and content templates. |
| Outcome: | The proposed method improves on evaluation metrics compared with state-of-the-art methods. |
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning (2021.acl-long)
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| Challenge: | Existing single-hop graph reasoning in Graph convolutional networks may miss some important non-consecutive dependencies. |
| Approach: | They propose a graph convolutional network with the high-order dynamic Chebyshev approximation which augments multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutionalist layer. |
| Outcome: | The proposed model improves on four transductive and inductive NLP tasks and the ablation of the existing model. |
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment (2024.acl-long)
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| Challenge: | Recent Large Multimodal Models (LMMs) focus on visual knowledge-dimension alignment, but ignore visual knowledge. |
| Approach: | They propose a cognitive visual-language mapper that integrates visual-linguistic knowledge alignment with a fine-grained knowledge Adapter. |
| Outcome: | The proposed model significantly improves LMMs on knowledge-based visual question answering (VQA) it also improves the performance of other models, including GPT-4V and Gemini-Pro. |
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)
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Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers . |
| Approach: | They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. |
| Outcome: | The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence . |
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)
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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang
| Challenge: | PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks. |
| Approach: | They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks. |
| Outcome: | The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. |
Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)
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| Challenge: | Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness. |
| Approach: | They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities. |
| Outcome: | The proposed framework can abstain from predicting alignment for detected dangling entities. |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods focus on single editing, failing to meet the requirements for lifelong editing. |
| Approach: | They propose an approach that selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. |
| Outcome: | The proposed method improves on GPT2-XL and GPT-J in lifelong editing compared to state-of-the-art methods . |
Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective (2026.findings-acl)
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| Challenge: | Reasoning-tuned large language models (LLMs) with long Chain-of-Thought excel at single-answer tasks, yet their ability to model Human Label Variation remains underexplored. |
| Approach: | They conduct systematic disentanglement experiments to isolate the effect of reasoning text from intrinsic model priors on distribution-based tasks. |
| Outcome: | The proposed model improves distributional alignment, but distributional ranking is governed by model priors. |
DRBO: Mitigating Short Board Effect via Dynamic Reward Balancing in Multi-reward LLM Optimization (2025.findings-emnlp)
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| Challenge: | a new framework to optimize large language models (LLMs) for evaluation metrics is needed to balance weaker metrics. |
| Approach: | They propose a Dynamic Reward Balancing Optimization framework to mitigate the "short-board effect" they apply it to single-task and multi-type task scenarios . |
| Outcome: | The proposed framework improves performance and balances performance across multiple metrics. |
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large Language Models (LLMs) have achieved remarkable success across diverse domains. |
| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)
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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models. |
| Approach: | They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data . |
| Outcome: | The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities. |
Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach (2024.emnlp-industry)
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| Challenge: | Automatic Speech Recognition (ASR) errors in voice-based dialog systems pose significant impediments to downstream tasks. |
| Approach: | They propose an automatic speech recognition (ASR) error-aware loss function to inject failed mentions and resolved entity names into the knowledge graph to enhance its awareness of unresolved mentions. |
| Outcome: | The proposed system enhances the knowledge graph's awareness of unresolved mentions by injecting pairs of failed mentions and resolved entities into the knowledge map. |
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization (2026.findings-acl)
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| Challenge: | NLCO evaluates large language models for combinatorial optimization (CO) . existing evaluations emphasize relatively simple reasoning competencies . |
| Approach: | They propose a combinatorial optimization benchmark that evaluates large language models on CO reasoning. |
| Outcome: | The proposed model can handle combinatorial optimization without writing code or calling external solvers. |
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)
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| Challenge: | Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes. |
| Approach: | They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading. |
| Outcome: | The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs. |
Tailoring Vaccine Messaging with Common-Ground Opinions (2024.findings-naacl)
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Rickard Stureborg, Sanxing Chen, Roy Xie, Aayushi Patel, Christopher Li, Chloe Zhu, Tingnan Hu, Jun Yang, Bhuwan Dhingra
| Challenge: | Vaccine interventions aim to answer concerns expressed about vaccination. |
| Approach: | They propose a dataset to evaluate how well responses are tailored to a common-ground opinion . they find that GPT-4-Turbo performs significantly better than others . |
| Outcome: | The proposed dataset outperforms fine tuned LLMs on the task of tailoring vaccine responses to common-ground opinions. |
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)
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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma
| Challenge: | Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments. |
| Approach: | They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods. |
| Outcome: | Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG. |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
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| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022.emnlp-main)
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| Challenge: | Pre-trained language models store a massive amount of world knowledge implicitly in their parameters, but large models often fail to encode information about rare entities and events. |
| Approach: | They propose a retrieval-augmented model which accesses an external non-parametric memory to augment language generation. |
| Outcome: | The proposed model outperforms existing models by 10-20% absolute on two datasets and under distractor and full-wiki settings. |
Towards the Law of Capacity Gap in Distilling Language Models (2025.acl-long)
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| Challenge: | Language model (LM) distillation aims at distilling knowledge in a large teacher LM to a small student one. |
| Approach: | They propose to use the law of capacity gap to distill knowledge from a large teacher to a small student model. |
| Outcome: | The proposed model outperforms other language models on a larger scale by using the law of capacity gap inducted from a preliminary study on small-scale (3B) LMs. |
Refusal-Aware Red Teaming: Exposing Inconsistency in Safety Evaluations (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) require rigorous safety evaluations to be effective. |
| Approach: | They propose a red teaming framework that detects internal model refusals and contrasts them with judgments from an external safety evaluator to generate test cases that expose such discrepancies. |
| Outcome: | The proposed framework outperforms existing reinforcement learning-based approaches in generating diverse test cases and achieves a substantially higher discovery rate of refusal gaps. |
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)
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Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li, Wenpeng Hu, Wenliang Chen, Zhunchen Luo, Guotong Geng, Min Zhang
| Challenge: | Existing models with reasoning capabilities suffer from a severe length collapse in open-ended writing . |
| Approach: | They propose a framework that embeds a dynamic plan-write-reflect cycle into the generation process and train a model with interleaved reasoning traces. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-form benchmarks compared to other models on the same dataset. |
ASTRO: Automatic Strategy Optimization For Non-Cooperative Dialogues (2025.findings-acl)
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| Challenge: | Existing methods for optimizing dialogues require substantial human effort for strategy optimization. |
| Approach: | They propose a fully automated solution that leverages large language models’ self-envolving capabilities to optimize dialogue strategies. |
| Outcome: | The proposed solution significantly improves on baseline models across non-cooperative dialogue tasks, highlighting the potential for autonomously developing such agents without human intervention. |
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
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| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)
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| Challenge: | Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings. |
| Approach: | They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information . |
| Outcome: | The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method . |
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)
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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen
| Challenge: | Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios. |
| Approach: | They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance. |
| Outcome: | The proposed framework improves model capabilities across all domains and scales. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing (2024.findings-acl)
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Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
| Challenge: | Current benchmarks focus on coarse-grained knowledge, leaving the intricacies of fine-grounded knowledge unexplored. |
| Approach: | They propose a benchmark and dataset specifically designed for FG multimodal entity knowledge editing. |
| Outcome: | The proposed benchmark underscoring the complexity of FG knowledge editing in MLLMs. |
InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation (2023.acl-long)
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| Challenge: | Existing image captioning metrics provide a single score to measure caption qualities, which are less explainable and informative. |
| Approach: | They propose an Informative Metric for Reference-free Image Caption evaluation to support this feedback . they propose to provide a text precision score, a vision recall score and an overall quality score . |
| Outcome: | The proposed method improves on existing metrics on multiple benchmarks and compares coarse-grained scores with human judgements. |
CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)
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| Challenge: | Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors. |
| Approach: | They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers. |
| Outcome: | The proposed dataset can detect and correct Chinese spelling errors and grammatical errors. |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Beyond Noise: Characterizing Creative Potential in Unverifiable LLM Hallucinations (2026.acl-long)
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Yu Yan, Chunhong Zhang, Haiyu Zhao, Ziyang Zeng, Zihao Liu, Yongkang Wu, Jianzhou Diao, YiJie Chen, Shujie Wang, Zheng Hu
| Challenge: | Large Language Models generate outputs that extend beyond established knowledge . prior work does not characterize the unverifiable space as a whole . |
| Approach: | They propose a novelty-verifiability characterization that distinguishes Creative Synthesis from Groundless Fabrication by a conceptual creation task. |
| Outcome: | The proposed model distinguishes Creative Synthesis (Region A) from Groundless Fabrication (Regium B) it shows that Region A is non-negligible and robust, persisting across generation strategies, models, domains, and embedding choices. |
Exploring Model Kinship for Merging Large Language Models (2025.findings-emnlp)
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| Challenge: | Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models. |
| Approach: | They propose a model merging strategy that incorporates model kinship to improve model performance. |
| Outcome: | The proposed model merging strategy can yield better performance on benchmark datasets. |
Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. |
| Approach: | They propose a behavior-constrained policy gradient with negative sample augmented (BCPG-NSA) negative steps are valuable components in long CoT models, authors argue . |
| Outcome: | The proposed framework outperforms baselines on math/coding reasoning benchmarks using the same training dataset. |
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)
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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang
| Challenge: | Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored. |
| Approach: | They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces. |
| Outcome: | The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces . |
Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models (2026.acl-long)
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Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh, David Zhang, Eric Hsin, Li Chen, Ankit Jain, Matt Fredrikson, Akash Bharadwaj
| Challenge: | Existing approaches to red teaming are based on example-based evaluation, where a static list of specific prompts is used to define and measure "unsafe content" |
| Approach: | They propose a new automated red teaming framework that shifts from example-based to policy-based evaluation that focuses on risk coverage, semantic diversity, and fidelity. |
| Outcome: | The proposed method achieves superior, human-readable attacks against open-source and proprietary models even for unseen safety policies. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models (2025.naacl-long)
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| Challenge: | Low-resource relation extraction aims to identify semantic relationships using scarce labeled data. |
| Approach: | They propose a framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales to obtain reliable pseudo annotations from massive unlabeled samples without human supervision. |
| Outcome: | The proposed framework achieves state-of-the-art on benchmark datasets in few-shot scenarios. |
Are LLM-based Evaluators Confusing NLG Quality Criteria? (2024.acl-long)
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| Challenge: | Existing studies show that LLMs confuse evaluation criteria, which reduces their reliability. |
| Approach: | They propose a hierarchical classification system for 11 common aspects with corresponding different evaluation criteria. |
| Outcome: | The proposed system is based on 11 common aspects with different evaluation criteria. |
CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts (2025.naacl-long)
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Zhenpeng Su, Xing W, Zijia Lin, Yizhe Xiong, Minxuan Lv, Guangyuan Ma, Hui Chen, Songlin Hu, Guiguang Ding
| Challenge: | Large language models (LLMs) have been attracting much attention due to their impressive performance in all kinds of downstream tasks. |
| Approach: | They propose a mix-of-experts model that allows the model size to grow without raising training costs. |
| Outcome: | The proposed model outperforms existing models in perplexity and robustness tests. |
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference (2025.emnlp-main)
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| Challenge: | Low-rank compression can reduce memory usage and computational demand, but results are poor during decoding. |
| Approach: | They propose a fine-grained low-rank compression algorithm that determines optimal rank allocation for each layer and incorporates progressive low-ranked decoding to maintain text generation quality. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on summarization tasks and on understanding tasks. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)
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| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
| Approach: | They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases . |
| Outcome: | The proposed framework explores the capability boundaries of large language models under different paradigms. |
Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
| Approach: | They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts. |
| Outcome: | The proposed model outperforms existing methods on three commonly-used datasets. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)
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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen
| Challenge: | Existing approaches to program repair are based on correctness alone. |
| Approach: | They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits. |
| Outcome: | The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing. |
Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures (2026.acl-long)
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Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, Liangming Pan
| Challenge: | Recent research has shown that reinforcement learning can elicit intriguing emergent reasoning behaviors. |
| Approach: | They propose a comprehensive survey of the mechanistic understanding of large reasoning models . they organize findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. |
| Outcome: | This paper synthesizes the mechanistic understanding of large reasoning models into three dimensions . authors outline a roadmap for future studies including improved interpretability and methodologies . |
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. |
| Approach: | They propose a generative paradigm for translation tasks that integrates the diverse translation versions in N-best list. |
| Outcome: | The proposed model outperforms the state-of-the-art model on speech and machine translation benchmarks on various languages. |
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering (2026.acl-long)
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| Challenge: | Existing long document question answering systems process texts as flat sequences or use heuristic chunking, which overlooks the discourse structures that guide human comprehension. |
| Approach: | They propose a discourse-aware hierarchical framework that leverages rhetorical structure theory for long document question answering. |
| Outcome: | The proposed framework exhibits strong robustness across diverse document types and linguistic settings. |
KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |
Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)
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| Challenge: | Existing approaches to optimize pre-trained language models are expensive and slow to scale. |
| Approach: | They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM . |
| Outcome: | The proposed method can achieve comparable results with other gradient-free and optimization-free baselines. |
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
UnifEE: Unified Evidence Extraction for Fact Verification (2023.eacl-main)
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| Challenge: | Existing models extract evidence in both sentences and table cells from Wikipedia dumps, ignoring potential connections between them. |
| Approach: | They propose a model which uses a mixed evidence graph to extract the evidence in both formats without manually designed conversion rules. |
| Outcome: | The proposed model outperforms existing models and improves the verification step. |
FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain (2025.emnlp-main)
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| Challenge: | Recent LLMs have demonstrated promising ability in solving finance related problems, but applying them in real-world finance applications remains challenging due to its high risk and high stakes property. |
| Approach: | They propose a benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. |
| Outcome: | The proposed benchmark outperforms proprietary models in most tasks while open-source models have advantage in specific areas like industry-level fairness. |
TruthReader: Towards Trustworthy Document Assistant Chatbot with Reliable Attribution (2024.emnlp-demo)
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| Challenge: | Document assistant chatbots are empowered with extensive capabilities by Large Language Models (LLMs) however, they suffer from hallucinations that are difficult to verify in the context of given documents. |
| Approach: | They propose a document assistant chatbot with reliable attribution that enables users to seek relevant information from given documents. |
| Outcome: | The proposed system generates answers with detailed inline citations, which can be attributed to the original document paragraphs, facilitating verification of factual consistency of the generated text. |
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)
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| Challenge: | MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models. |
| Approach: | They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy. |
| Outcome: | The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set. |
Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection Prompting (2026.findings-acl)
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| Challenge: | Existing work relies on fine-tuning specialized modules to bridge this gap, but a novel approach is proposed to leverage off-the-shelf LLMs without any fine- tuning whatsoever. |
| Approach: | They propose a method to inject noise into the raw time series before tokenization to induce the model to extrapolate based on robust underlying temporal patterns rather than superficial numerical artifacts. |
| Outcome: | The proposed approach overcomes the brittleness of fully frozen models by injecting noise into the raw TS before tokenization. |
ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)
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| Challenge: | Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent. |
| Approach: | They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video. |
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art results on all datasets. |
Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation (2026.acl-long)
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| Challenge: | Existing fine-grained entity typing models are susceptible to misclassify unknown-type instances . manual collection and annotation of large unknown-Type instances is time-consuming and labor-intensive in open environments. |
| Approach: | They propose a novel task that uses open-set entity typing to classify entities of unknown types . they propose 'two-stage generation model' that automatically produces large-scale pseudo unknown-type instances . |
| Outcome: | The proposed framework surpasses baselines on two newly established benchmark datasets. |
SCoder: Progressive Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs (2025.findings-emnlp)
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Xinyu Zhang, Changzhi Zhou, Linmei Hu, Luhao Zhang, Xiancai Chen, Haomin Fu, Yang Yang, Mengdi Zhang
| Challenge: | Existing code large language models rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs. |
| Approach: | They propose an iterative self-distillation approach to bootstrap small-scale LLMs . they use large-scale instruction data distilled from proprietary LLM for fine-tuning . |
| Outcome: | The proposed method reduces reliance on proprietary LLMs and minimizes costs. |
Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)
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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang
| Challenge: | Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process. |
| Approach: | They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors. |
| Outcome: | The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs. |
Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)
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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu
| Challenge: | Despite the success of speech recognition, how to encode the speech features effectively remains an open problem. |
| Approach: | They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. |
| Outcome: | The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x. |
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Recent work on learning from multiple tasks has shown that adding an extra fusion layer to implement knowledge composition is non-scalable for some applications. |
| Approach: | They propose a two-stage knowledge distillation algorithm to extract task specific knowledge by using local data to train a student adapter. |
| Outcome: | Experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |
MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for complex multi-shot video are anchored to single-shot paradigms, lacking comprehensive story assets and cross-shot metrics. |
| Approach: | They propose a framework that synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models. |
| Outcome: | The proposed framework synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models. |
Improving Radiology Summarization with Radiograph and Anatomy Prompts (2023.findings-acl)
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| Challenge: | Recent studies focus on automatic impression generation, but this task is time-consuming and in high demand. |
| Approach: | They propose to use an anatomy-enhanced multimodal model to generate automatic impressions by combining radiology images with textual features. |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets and compares with existing models. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)
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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang
| Challenge: | retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures . |
| Approach: | They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents. |
| Outcome: | The proposed method reduces offline indexing costs and accelerates retrieval. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Take Off the Training Wheels! Progressive In-Context Learning for Effective Alignment (2024.emnlp-main)
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| Challenge: | Recent studies have explored the working mechanisms of In-Context Learning (ICL) however, they mainly focus on classification and simple generation tasks, limiting their broader application to more complex generation tasks in practice. |
| Approach: | They propose an efficient Progressive In-Context Alignment method that embeds the task function learned from demonstrations into the separator token representation. |
| Outcome: | The proposed method surpasses vanilla ICL and achieves comparable performance to other alignment tuning methods. |
Token-level Inference-Time Alignment for Vision-Language Models (2026.findings-acl)
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Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, Mingli Song
| Challenge: | Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity . despite widespread adoption, VLMs often exhibit a critical failure mode: hallucination . |
| Approach: | They propose a framework for Token-level Inference-Time Alignment that steers the decoding process without updating the base model parameters. |
| Outcome: | The proposed framework improves performance on 13 benchmarks across architectures . it boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score . |
Improving Continual Relation Extraction through Prototypical Contrastive Learning (2022.coling-1)
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| Challenge: | Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. |
| Approach: | They propose a Continual Relation Extraction framework with Contrastive Learning which is built with a classification network and a prototypical contrastive network to achieve incremental-class learning of CRE. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two public datasets and proves its effectiveness on improving performance. |
TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)
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| Challenge: | Large Language Models excel in general domains but lack real-world practical capabilities. |
| Approach: | They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios. |
| Outcome: | The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios. |
MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control (2026.findings-acl)
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| Challenge: | Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation. |
| Approach: | They propose an autoregressive framework with a two-stage training paradigm for controllable multimodal image generation. |
| Outcome: | Extensive experiments on DreamBench++ and DreamBech show that the proposed framework achieves a strong balance between textual and visual guidance for controllable image generation. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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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% . |
Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications (2024.emnlp-main)
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| Challenge: | Recent studies have focused on how large language models process multiple languages, but internal mechanisms of LLMs remain insufficiently explored. |
| Approach: | They propose to convert dense LLMs into fine-grained MoE architectures and analyze their activation patterns using expert activation frequency heatmaps. |
| Outcome: | The proposed method outperforms random expert pruning and exceeds models in some languages. |
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)
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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. |
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps (2020.acl-main)
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| Challenge: | Existing state-of-the-art VLN agents do not generalize well for long navigation tasks. |
| Approach: | They propose a VLN agent that is learned to navigate by decomposing long instructions into shorter ones and completing them sequentially. |
| Outcome: | The proposed agent can follow long instructions better than existing ones, but it does not generalize well. |
Proactive Hearing Assistants that Isolate Egocentric Conversations (2025.emnlp-main)
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| Challenge: | Existing hearing assistants are "reactive" in that users manually prompt them to pick specific sound sources via spatial filtering or phone-based interfaces. |
| Approach: | They propose a dual-model architecture that uses the wearer's self-speech as an anchor to infer conversational partners and suppress others. |
| Outcome: | The proposed system can identify and separate conversation partners in multi-conversation settings without explicit user commands or prompts. |
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)
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Mingkuan Zhao, Wentao Hu, Tianchen Huang, Yuheng Min, Suquan Chen, Yide Gao, Yanbo Zhai, Shuangyong Song, Xuelong Li
| Challenge: | Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints. |
| Approach: | They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream. |
| Outcome: | The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods. |
Fine-Tuning Large Language Models with Sequential Instructions (2025.naacl-long)
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| Challenge: | Existing instruction-tuned models struggle to adhere to a query with multiple intentions, which impairs their performance when the completion of several tasks is demanded by a single command. |
| Approach: | They develop an automatic process that turns existing data into diverse and complex task chains and a new benchmark to evaluate a model’s ability to follow all the instructions in a sequence. |
| Outcome: | The proposed model can follow instructions better and deliver higher results in coding, maths, and open-ended generation. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping (2024.emnlp-main)
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| Challenge: | Autoregressive Large Language Models (LLMs) are omnipresent but typically come with a substantial model size. |
| Approach: | They propose a novel fine-grained skip strategy for autoregressive large language models . they observe the saturation of computationally expensive feed-forward blocks of LLMs . |
| Outcome: | The proposed method can skip 25-30% of FFN blocks with marginal change in performance on knowledge-intensive generation tasks. |
Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning (2026.findings-acl)
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| Challenge: | Reasoning ability is a defining capability of Large Language Models (LLMs), but RLVR training suffers from policy entropy collapse, hindering exploration and limiting reasoning performance. |
| Approach: | They propose a framework that dynamically balances exploration and exploitation via three components: difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment. |
| Outcome: | The proposed framework outperforms baselines on multiple mathematical reasoning benchmarks. |
CoachMe: Decoding Sport Elements with a Reference-Based Coaching Instruction Generation Model (2025.acl-long)
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Wei-Hsin Yeh, Yu-An Su, Chih-Ning Chen, Yi-Hsueh Lin, Calvin Ku, Wenhsin Chiu, Min-Chun Hu, Lun-Wei Ku
| Challenge: | Existing multimodal models for motion related tasks have shown significant progress. |
| Approach: | They propose a reference-based model that analyzes the differences between a learner’s motion and a physical reference under temporal and physical aspects. |
| Outcome: | The proposed model outperforms GPT-4o on figure skating and boxing by 31.6% and 58.3% respectively. |
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)
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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen
| Challenge: | Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property. |
| Approach: | They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness. |
| Outcome: | The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness. |
CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)
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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu
| Challenge: | CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation. |
| Approach: | They propose a LLM-based agent framework dedicated to repo-level compilation. |
| Outcome: | The proposed method significantly improves compilation success rate, ranging from 10% to 71%. |
Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models (2026.findings-acl)
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Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, Junbo Zhao
| Challenge: | Prior work focused on data preprocessing, focusing on filtering and cleaning data . a study aimed to improve fine-grained scheduling of data order in epochs . |
| Approach: | They propose a fine-grained scheduling method of data order in epochs to fill this gap . they define data difficulty based on relevance between data and model . |
| Outcome: | The proposed method improves on pre-training and small-scale fine-tuning experiments 2.4% over baselines. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models (2024.acl-long)
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| Challenge: | Existing methods of model editing and knowledge updating add additional network parameters, knowledge bases, knowledge base, and model parameters. |
| Approach: | They propose a new paradigm for fine-tuning called F-Learning that employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. |
| Outcome: | The proposed model outperforms existing models on two datasets and is comparable to full fine-tuning and LoRA fine-uning. |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024.findings-acl)
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| Challenge: | Emotional Intelligence (EI) is a key concept in the field of human intelligence. |
| Approach: | They propose a method to enhance EI of large language models by naive fine-tuning on EI-related tasks. |
| Outcome: | The proposed method improves EI of two LLM-based assistants without compromising GI. |
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)
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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen
| Challenge: | Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. |
| Approach: | They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance. |
| Outcome: | The proposed method achieves a superior balance between downstream learning and general capability retention. |
RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning (2025.findings-acl)
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Junhao Hu, Wenrui Huang, Weidong Wang, Zhenwen Li, Tiancheng Hu, Zhixia Liu, Xusheng Chen, Tao Xie, Yizhou Shan
| Challenge: | Large Language Models (LLMs) have demonstrated strong capabilities across various domains, but their large-scale deployment faces a major obstacle: the high computational cost of long-sequence inference. |
| Approach: | They propose an algorithm that retains key-value vectors until they are no longer needed to solve reasoning tasks. |
| Outcome: | The proposed algorithm achieves high accuracy with O(L) time but O(N) memory complexities. |
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026.acl-long)
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Zhicong Li, Lingjie Jiang, Yulan Hu, Xingchen Zeng, Yixia Li, Xiangwen Zhang, Guanhua Chen, Zheng Pan, Xin Li, Yong Liu
| Challenge: | Current methods for training Large Language Model agents rely on static or offline critic models, which fail to adapt as the policy evolves. |
| Approach: | They propose a framework that integrates a critique and a policy to optimize the policy and critic through a synchronized co-evolutionary loop. |
| Outcome: | The proposed framework yields more stable training and higher long-horizon task success across open-world environments. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . |
| Approach: | They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension . |
| Outcome: | The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) |
OSCBench: Benchmarking Object State Change in Text-to-Video Generation (2026.acl-long)
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Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen
| Challenge: | Existing benchmarks focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding unexplored. |
| Approach: | They introduce a benchmark specifically designed to assess OSC performance in T2V models. |
| Outcome: | The proposed benchmark assesses the performance of open-source and proprietary T2V models on object state change (OSC) in the context of novel and compositional scenarios. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)
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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu
| Challenge: | Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains. |
| Approach: | They propose a training framework that operationalizes this principle through coarse-to-fine budgeting. |
| Outcome: | Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines. |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks . |
| Approach: | They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates . |
| Outcome: | The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories . |
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer (2023.findings-emnlp)
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| Challenge: | Existing studies explore performing text style transfer on attributes like age, gender, formality, politeness, and formality. |
| Approach: | They propose a framework that freezes the pre-trained model’s original parameters and enables the development of a multiple-attribute text style transfer model. |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment transfer and multiple-attribute transfer tasks with significantly less computational resources. |
Ensembling Prompting Strategies for Zero-Shot Hierarchical Text Classification with Large Language Models (2025.emnlp-main)
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| Challenge: | Hierarchical text classification is a challenging task in natural language processing. |
| Approach: | They propose a method which integrates the results of diverse prompting strategies to promote LLMs’ reliability. |
| Outcome: | The proposed method boosts the performance of single prompting strategies and achieves SOTA results on three benchmark datasets. |
Localized Low-Rank Adaptation within Clustered Parameter Subspaces (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) for large language models has been successful in various domains. |
| Approach: | They propose to perform low-rank updates within clustered parameter subspaces . they group rows/columns of update matrix into locally coherent, uncorrelated subspace blocks . |
| Outcome: | Empirical results show that low-rank Adaptation (LoRA) is better than global adaptations in various domains. |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
ELAD: Explanation-Guided Large Language Models Active Distillation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are hindered by their memory inefficiency, computational demands, and the high costs of API inferences. |
| Approach: | They propose an Explanation-Guided LLMs Active Distillation framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. |
| Outcome: | The proposed framework significantly improves the efficiency of LLMs knowledge distillation. |
Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
| Outcome: | The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting. |
Better Red Teaming via Searching with Large Language Model (2025.findings-acl)
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| Challenge: | Existing methods for evaluating large language models face challenges in managing semantic intricacies and optimizing the efficiency of the search process. |
| Approach: | They propose a framework that reconceptualizes test case generation as a strategic planning problem, leveraging Monte Carlo Tree Search. |
| Outcome: | Experiments on a range of LLM architectures show that the proposed framework achieves state-of-the-art attack success rates without sacrificing computational efficiency. |
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)
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| Challenge: | Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference. |
| Approach: | They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code. |
| Outcome: | The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters. |
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)
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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu
| Challenge: | Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps. |
| Approach: | They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling. |
| Outcome: | The proposed method outperforms open- and closed-source models on most metrics. |
A structure-enhanced graph convolutional network for sentiment analysis (2020.findings-emnlp)
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| Challenge: | Recent work on sentiment analysis and aspect-based sentiment analysis does not exploit syntactic information from dependency parsing. |
| Approach: | They propose a weighted graph convolutional network which exploits syntactic information . they use BERT instead of Bi-LSTM to generate contextualized representations as inputs . |
| Outcome: | The proposed model can exploit rich syntactic information based on feature combination . it can improve on four ABSA tasks out of six and two SA tasks out . |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
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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. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation (2026.findings-acl)
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Zhenran Xu, Yiyu Wang, Yunxin li, Muyang Ye, null Yangxue, Kai Chen, Longyue Wang, Weihua Luo, Baotian Hu, Min Zhang
| Challenge: | Large language models (LLMs) have shown promising advances in tackling human-level tasks, but generating workflows for collaborative AI systems remains a critical and challenging step. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to generate executable and instruction-following AIGC workflows in ComfyUI. |
| Outcome: | The proposed benchmarks show that LLMs can generate executable and instruction-following AIGC workflows in ComfyUI. |
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
| Challenge: | Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection. |
| Approach: | They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. |
| Outcome: | The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners. |
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
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| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions (2025.findings-acl)
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Xunzhi Wang, Zhuowei Zhang, Gaonan Chen, Qiongyu Li, Bitong Luo, Zhixin Han, Haotian Wang, Zhiyu Li, Hang Gao, Mengting Hu
| Challenge: | Existing methods for benchmarking the uncertainty of large language models face challenges . existing methods require internal model access, additional training, or high computational costs . |
| Approach: | They propose a new benchmark for evaluating the uncertainty of large language models based on confidence intervals . UBench encompasses 11,978 multiple choice questions spanning knowledge, language, understanding, and reasoning capabilities. |
| Outcome: | The proposed method outperforms existing methods for benchmarking the uncertainty of large language models. |
Red-Teaming NSFW Image Classifiers as Text-to-Image Safeguards (2026.findings-acl)
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Tinghao Xie, Yueqi Xie, Alireza Zareian, Shuming Hu, Felix Juefei-Xu, Xiaowen Lin, Ankit Jain, Prateek Mittal, Li Chen
| Challenge: | Not Safe for Work (NSFW) image classifiers play a critical role in safeguarding text-to-image systems. |
| Approach: | They propose an automated red-teaming framework that leverages a set of generative AI tools to uncover NSFW image failures. |
| Outcome: | The proposed framework uncovers and interprets failure modes and enables it to be applied to real-world T2I and T2V systems. |
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)
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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen
| Challenge: | Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals. |
| Approach: | They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training. |
| Outcome: | The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training . |
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)
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Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu
| Challenge: | Large language model (LLM) routing assigns each query to the best suitable model from an ensemble. |
| Approach: | They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation . |
| Outcome: | The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR). |
| Approach: | They propose a multimodal LLM to receive source speech as extra input and reformat it as a cloze test with logits calibration to remove input information redundancy and simplify GER with clear instructions. |
| Outcome: | The proposed model improves on 9 popular ASR datasets and is faster than vanilla GER. |
ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models (2025.findings-acl)
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Hao Chen, Haoze Li, Zhiqing Xiao, Lirong Gao, Qi Zhang, Xiaomeng Hu, Ningtao Wang, Xing Fu, Junbo Zhao
| Challenge: | Prior research has focused on optimizing general-purpose large language models to downstream tasks . however, these approaches inherently introduce data dependency, which hinders generalization and reusability. |
| Approach: | They propose an algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. |
| Outcome: | The proposed algorithm achieves 2% performance improvement over baselines on three tasks while localizing the most task-sensitive attention heads. |
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)
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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong
| Challenge: | Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models. |
| Approach: | They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
| Outcome: | The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space (2026.findings-acl)
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| Challenge: | Existing multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. |
| Approach: | They propose a method that integrates visual and visual information into the reasoning process to improve the performance of multimodal LLMs. |
| Outcome: | The proposed method achieves an average performance increase of 5.45% while achieving a speed increase of over 5 times compared to existing methods. |
YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)
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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen
| Challenge: | prevailing pre-training approaches for large language models involve several complexities. |
| Approach: | They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data . |
| Outcome: | The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data . |
Geo-BERT Pre-training Model for Query Rewriting in POI Search (2021.findings-emnlp)
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| Challenge: | Existing methods to solve the word mismatch between queries and documents are often inadequate to integrate geographic information into the pre-training model. |
| Approach: | They propose to train a pre-training model to integrate semantics and geographic information in the pre-trained representations of POIs. |
| Outcome: | The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services. |
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)
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| Challenge: | End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it. |
| Approach: | They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency . |
| Outcome: | The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks. |
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation (2024.emnlp-main)
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| Challenge: | Existing methods for enhancing RAG performance rely on heuristic-based augmentation . Existing approaches rely heavily on a heuriistic-driven approach, resulting in poor generalization and skews in the evidence length. |
| Approach: | They propose a model-based evidence extraction learning framework that optimizes a vanilla model as an evidence extractor with desired properties through self-aligned learning. |
| Outcome: | The proposed method reduces the evidence length by 9.25 times and improves reliability and reliability. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)
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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. |
| Approach: | They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations. |
| Outcome: | The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives. |
Learning to Compress Prompt in Natural Language Formats (2024.naacl-long)
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| Challenge: | Existing work rely on compressing long contexts into soft prompts, but soft prompt compression encounters limitations in transferability . natural language (NL) prompts are incompatible with back-propagation, and NL prompts lack flexibility in imposing length constraints. |
| Approach: | They propose a framework that compresses long prompts into NL formatted Capsule Prompts. |
| Outcome: | The proposed framework reduces 81.4% of the original length, decreases inference latency up to 4.5x, and saves 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets. |
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026.findings-acl)
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| Challenge: | Existing routing strategies rely on local token probabilities or post-hoc verification, introducing significant inference overhead. |
| Approach: | They propose a step-wise collaboration framework that generates only the first token of each reasoning step and routes it to a larger model only when initial token entropy exceeds a threshold. |
| Outcome: | The proposed approach reduces inference latency while preserving accuracy. |
Data Contamination Calibration for Black-box LLMs (2024.findings-acl)
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| Challenge: | Despite the rapid advancements of Large Language Models, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination. |
| Approach: | They propose a method to detect contaminated training data and diminish the contamination effect by using a to-be-released dataset. |
| Outcome: | The proposed method outperforms existing methods by at least 4.5% on more 4 dataset formats, with more than 10 base LLMs. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
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| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)
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| Challenge: | Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts. |
| Approach: | They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge . |
| Outcome: | The proposed dataset elicits models to use fine-grained knowledge learned during pre-training. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Hybrid-Regressive Paradigm for Accurate and Speed-Robust Neural Machine Translation (2023.findings-acl)
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| Challenge: | Autoregressive translation (NAT) is less robust in decoding batch size and hardware settings than NAT. |
| Approach: | They propose a two-stage translation prototype that prompts a small number of AT predictions and fills in previously skipped tokens at once. |
| Outcome: | The proposed translation prototype achieves comparable translation quality with AT while having 1.5x faster inference speed regardless of batch size and device. |
A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
PUPPET: Neural-Symbolic Standardized Patients for Mental Health (2026.acl-long)
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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. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
FOCUS: A Fine-Grained Customer-Oriented Sentiment Dialogue Summarization Dataset for Chinese Customer Service (2026.findings-acl)
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| Challenge: | Existing studies largely overlook fine-grained sentiment dynamics expressed by customers . current methods often exhibit misalignment between aspects and sentiments . |
| Approach: | They propose a three-stage approach to building an aspect-aware sentiment dataset . they use a fine-grained customer-oriented Chinese dialogUe summarization dataset based on this scheme . |
| Outcome: | The proposed model improves faithfulness and interpretability of the proposed dataset. |
Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)
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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen
| Challenge: | Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles . |
| Approach: | They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning . |
| Outcome: | The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks. |
MoDification: Mixture of Depths Made Easy (2025.naacl-long)
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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song
| Challenge: | Long-context efficiency is a trending topic in large language model (LLM) serving. |
| Approach: | They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory. |
| Outcome: | The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications. |
Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)
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Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence . |
| Approach: | They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions. |
| Outcome: | Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods. |
Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning (2025.acl-long)
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Chengwei Qin, Wenhan Xia, Fangkai Jiao, Chen Chen, Yuchen Hu, Bosheng Ding, Ruirui Chen, Shafiq Joty
| Challenge: | Existing methods to train student models on the generated outputs of teacher models are not efficient for ICL. |
| Approach: | They propose to align the output of smaller (student) models with that of larger (teacher) models by incorporating a ranking loss and aligning the token-level output distribution. |
| Outcome: | The proposed model outperforms baseline models on a variety of tasks involving language understanding, reasoning, and coding. |
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data (2023.findings-emnlp)
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| Challenge: | Various machine learning methods for tabular data lack accurate confidence estimation, which is needed for high-risk sensitive applications such as credit modeling and financial fraud detection. |
| Approach: | They propose a general post-training confidence calibration framework to calibrate the confidence of current machine learning models by employing graph neural networks to model the relationships between different samples. |
| Outcome: | The proposed framework improves the confidence estimation on tabular datasets by using graph neural networks to model the relationships between different samples. |
Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)
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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang
| Challenge: | Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens . |
| Approach: | They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths. |
| Outcome: | Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed . |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction (2025.findings-emnlp)
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Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan
| Challenge: | NL2SQL provides a model-centric paradigm that simplifies database access for non-technical users . challenges such as inaccurate task decomposition and keyword extraction remain major bottlenecks . |
| Approach: | They propose a RAG-based NL2SQL pipeline that employs three modules for query understanding, entity retrieval, and generation to improve SQL generation accuracy. |
| Outcome: | The proposed pipeline improves the accuracy of query generation on BIRD and Spider datasets. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
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| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. |
| Approach: | They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances . |
| Outcome: | The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks. |
Beyond the Surface: Measuring Self-Preference in LLM Judgments (2025.emnlp-main)
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| Challenge: | Existing methods measure self-preference bias by comparing the scores a judge model assigns to its own responses with those assigned to other models. |
| Approach: | They propose to use gold judgments as proxies for the actual quality of responses . they propose to measure self-preference bias as the difference between the judge model's own and other models' scores . |
| Outcome: | The proposed method can assess self-preference bias across large language models . it uses gold judgments as proxies for the ground truth scores of the judge model . |
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)
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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data. |
| Approach: | They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process. |
| Outcome: | Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities. |
Revisiting Sparse Retrieval for Few-shot Entity Linking (2023.emnlp-main)
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| Challenge: | Entity linking (EL) aims to link ambiguous mentions to their corresponding entities in a knowledge base. |
| Approach: | They propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. |
| Outcome: | The proposed method outperforms state-of-the-art models on the ZESHEL dataset by a significant margin. |
Improving Multilingual Sign Language Translation with Automatically Clustered Language Family Information (2025.coling-main)
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| Challenge: | Recent research has focused on bilingual translation models, but multilingual sign language translation presents unique challenges due to the diversity of sign languages across nations. |
| Approach: | They propose a method that leverages sign language families to improve MSLT performance. |
| Outcome: | The proposed approach can achieve balance between translation accuracy and computational cost by regulating the number of language families. |
FastSeq: Make Sequence Generation Faster (2021.acl-demo)
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Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang
| Challenge: | Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process. |
| Approach: | They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy. |
| Outcome: | The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models. |
GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. |
| Approach: | They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. |
| Outcome: | The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task. |
ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation (2025.findings-emnlp)
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| Challenge: | Currently, legal claims are not being used by non-professionals. |
| Approach: | They construct a dataset for Chinese legal claim generation task and then use it to evaluate the generated claims. |
| Outcome: | The proposed dataset is the first for the Chinese legal claim generation task and will be made publicly available. |
EDSD: Entropy-Driven Design for Faster Speculative Decoding (2026.acl-long)
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| Challenge: | Existing methods for speculative decoding incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding. |
| Approach: | They propose an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design. |
| Outcome: | Experiments on seven large language models show that EDSD improves training efficiency by 24.8% and increases acceptance length by 4.0% compared to state-of-the-art methods. |
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)
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| Challenge: | Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data. |
| Approach: | They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure. |
| Outcome: | The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data. |
EEE-QA: Exploring Effective and Efficient Question-Answer Representations (2024.lrec-main)
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| Challenge: | Current approaches to question answering rely on pre-trained language models like RoBERTa. |
| Approach: | They propose a pooling approach that embeds all answer candidates with the question . they also propose enabling cross-reference between answer choices . |
| Outcome: | The proposed methods improve throughput and memory efficiency with little sacrifice in performance. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
Predicting Entity Salience in Extremely Short Documents (2024.emnlp-industry)
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| Challenge: | False positive: ES is a natural language understanding task that determines which entities are most salient to a passage . Falsity: Popsicle, Frank Epperson and San Francisco are salient entities . |
| Approach: | They propose a lightweight and data-efficient approach for entity salience detection on short documents . they propose he use of a human-labeled dataset to evaluate entity salient on short questions . |
| Outcome: | The proposed approach achieves competitive performance over state-of-the-art models at significant cost and latency advantages. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation (2024.lrec-main)
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| Challenge: | Existing non-simultaneous sign language translation methods suffer from inherent inference delays in real-time scenarios. |
| Approach: | They propose an adaptive policy for simultaneous sign language translation that progressively converts incrementally received sign video into its corresponding natural sentence. |
| Outcome: | The proposed policy excels in situations requiring extremely low latency. |
Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)
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| Challenge: | Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices . |
| Approach: | They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details . |
| Outcome: | The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems. |
Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning (2026.acl-long)
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| Challenge: | Large Language Models face quadratic computational costs, information forgetting, and context fragmentation . current methodologies diverge into three paradigms, each facing inherent trade-offs between Equal contribution. |
| Approach: | They propose a framework that enables efficient long-context inference via chunk-wise compression and selective memory recall. |
| Outcome: | The proposed framework reduces peak GPU memory usage and speeds up inference on multi-hop reasoning benchmarks. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly (2024.naacl-long)
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| Challenge: | Current large language models show imbalance abilities in different languages . authors propose two approaches to improve cross-lingual knowledge alignment . |
| Approach: | They propose a framework to assess cross-lingual knowledge alignment of large language models . they propose multilingual pretraining and multilingual instruction tuning to address this problem . |
| Outcome: | The proposed framework assesses the cross-lingual knowledge alignment of LLMs in performance, consistency and conductivity levels. |
LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)
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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen
| Challenge: | Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data. |
| Approach: | They propose a location-based approach that leverages locational data to optimize interaction preferences. |
| Outcome: | The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations. |
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning Tasks (2025.emnlp-main)
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| Challenge: | Existing methods for large language models rely on sequential queries . however, existing methods rely heavily on sequential querying . |
| Approach: | They propose a training-free framework that transforms a single LLM into an effective inference-time ensemble. |
| Outcome: | The proposed framework outperforms existing models on reasoning benchmarks, such as MATH, and improves on a DIPPER ensemble of three Qwen2-MATH-1.5B instances. |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering (2023.acl-long)
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| Challenge: | Existing approaches to short text clustering are prone to degenerate solutions and noisy data. |
| Approach: | They propose a model to improve robustness against imbalanced and noisy data . they propose self-adaptive optimal transport and class-wise contrastive learning . |
| Outcome: | The proposed model outperforms the state-of-the-art models on eight short text clustering datasets. |
A Generation-based Deductive Method for Math Word Problems (2023.emnlp-main)
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| Challenge: | Existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations. |
| Approach: | They propose a multivariate directed acyclic graph (mDAG) which generates the topological ordering of mDAg by equipping a generation model with a re-encoder to keep the deductive property but avoid the expensive enumeration of deductive methods. |
| Outcome: | The proposed model performs well on the widely used benchmarks and solves multivariate operators on the CMWPA benchmark. |
Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)
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| Challenge: | Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors. |
| Approach: | They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. |
| Outcome: | The proposed paradigm achieves integration intervention throughout model reasoning processes. |
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)
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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
| Challenge: | Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities. |
| Approach: | They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks. |
| Outcome: | The proposed benchmark renders fragmented documents directly from Markdown. |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)
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| Challenge: | Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. |
| Approach: | They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. |
| Outcome: | The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways. |
A Read-and-Select Framework for Zero-shot Entity Linking (2023.findings-emnlp)
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| Challenge: | Existing methods focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking prediction. |
| Approach: | They propose a read-and-select framework that models the main components of entity disambiguation . they use mention context to output mention-aware entity representations . |
| Outcome: | The proposed framework achieves state-of-the-art performance on established zero-shot entity linking dataset ZESHEL with 2.55% micro-average accuracy gain, with no need for laborious multi-phase pre-training used in most of the previous work. |
Dynamic Multi-granularity Attribution Network for Aspect-based Sentiment Analysis (2024.emnlp-main)
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| Challenge: | Existing methods for predicting sentiment polarity of aspects are susceptible to interference caused by irrelevant contexts and lack sentiment knowledge at a data-specific level. |
| Approach: | They propose a novel Aspect-based sentiment analysis method that leverages attention scores to model the relationships between aspects and contexts. |
| Outcome: | The proposed method is able to predict sentiments from a set of five benchmark datasets. |
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing knowledge rewriting methods may include irrelevant information, omit crucial details, or fail to align with the question’s semantics. |
| Approach: | They propose a new rewriting method CoTKR for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewrite. |
| Outcome: | The proposed method mitigates the limitations of single-step knowledge rewriting and bridges the preference gap between the knowledge reactor and the question answering (QA) model. |
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)
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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen
| Challenge: | Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody . |
| Approach: | They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis. |
| Outcome: | The proposed datasets provide richer contextual information, which is lacking in existing datasets. |
AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)
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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang
| Challenge: | Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns. |
| Approach: | They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback. |
| Outcome: | The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
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J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)
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| Challenge: | Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences. |
| Approach: | They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization. |
| Outcome: | The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment. |
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
| Approach: | They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation. |
| Outcome: | The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling. |
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models (2025.findings-acl)
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Haoyang Li, Xuejia Chen, Zhanchao Xu, Darian Li, Nicole Hu, Fei Teng, Yiming Li, Luyu Qiu, Chen Jason Zhang, Li Qing, Lei Chen
| Challenge: | Existing benchmarks focus on linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate numerical capabilities for large language models . they use a dataset to assess number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
| Outcome: | The proposed benchmark evaluates six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents. |
| Approach: | They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning (2025.findings-emnlp)
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| Challenge: | Recent advances in parameter-efficient fine-tuning techniques allow for adjustments to only a minor fraction of the parameters of language models. |
| Approach: | They propose a low-rank direct attention adapted method for efficient LLM fine-tuning . they propose LMAM, which can bring negative attention to self-attention modules . |
| Outcome: | The proposed method outperforms the full fine-tuning method by 2.1% on GLUE benchmark. |
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation (2024.acl-long)
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| Challenge: | e.g., GPT-4 still lag behind humans in effective multitasking, a study finds . current textual simulations do not adequately address the notion of time . |
| Approach: | They propose a textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
| Outcome: | The proposed model incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict (2026.findings-acl)
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| Challenge: | Existing evaluations measure privacy-related attitudes or sharing intentions in isolation, making it difficult to determine whether a model’s expressed values jointly predict its downstream data-sharing actions as in real human behaviors. |
| Approach: | They propose a framework that sequentially administers standardized questionnaires for privacy attitudes, prosocialness, and acceptance of data sharing within a bounded, history-carrying session. |
| Outcome: | The proposed model shows that it is stable but model-specific Privacy-PSA-AoDS profiles, and substantial heterogeneity in value-action alignment. |
Temporal Knowledge Question Answering via Abstract Reasoning Induction (2024.acl-long)
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| Challenge: | a new method to enhance temporal knowledge reasoning in large language models addresses this challenge . Abstract Reasoning Induction (ARI) framework provides factual knowledge support to LLMs . |
| Approach: | They propose an abstract reasoning induction framework which divides temporal reasoning into two phases: Knowledge agnostic and Knowledge-based. |
| Outcome: | The proposed method achieves significant gains on two temporal QA datasets. |
Language Agnostic Multilingual Information Retrieval with Contrastive Learning (2023.findings-acl)
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| Challenge: | Annotated training data is costly to obtain in many languages . |
| Approach: | They propose a semantic contrastive loss to align parallel sentences that share the same semantics in different languages and a language contrastive gain to leverage parallel sentence pairs to remove language-specific information from non-parallel corpora. |
| Outcome: | The proposed model improves retrieval performance while requiring less computational effort. |
CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking (2025.findings-emnlp)
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| Challenge: | CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation. |
| Approach: | a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese. |
| Outcome: | CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models (2024.emnlp-main)
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Xinrong Zhang, Yingfa Chen, Shengding Hu, Xu Han, Zihang Xu, Yuanwei Xu, Weilin Zhao, Maosong Sun, Zhiyuan Liu
| Challenge: | Large language models (LLMs) are increasingly permeating daily lives and require real-time interactions that mirror human conversations. |
| Approach: | They propose to use time-division-multiplexing to process queries and responses pseudo-simultaneously. |
| Outcome: | The proposed model can listen to users while generating output and adjust to provide instant feedback. |
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)
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Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu
| Challenge: | distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction. |
| Approach: | They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections. |
| Outcome: | The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets. |
NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)
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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King
| Challenge: | Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. |
| Approach: | They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs. |
| Outcome: | The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets. |
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition (2023.acl-long)
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| Challenge: | Existing efforts to improve robustness of audio-visual speech recognition with visual information focus on audio modality . current approaches introduce noise adaptation techniques to improve reliability of AVSR task . |
| Approach: | They propose a visual-invariant modality to strengthen robustness of audio-visual speech recognition (AVSR) it can adapt to any testing noises without dependence on noisy training data, a.k.a., unsupervised noise adaptation. |
| Outcome: | The proposed method outperforms existing state-of-the-arts on visual speech recognition task under various noisy and clean conditions. |
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)
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| Challenge: | Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions. |
| Approach: | They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments. |
| Outcome: | The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments. |
MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds (2025.emnlp-main)
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| Challenge: | Existing methods neglect stylistic modeling and rely on static thresholds, which greatly limits the detection performance. |
| Approach: | They propose a framework that enables stylistics-aware uncertainty quantification through conditional threshold estimation. |
| Outcome: | The proposed framework achieves an average improvement 11.34% in detection performance compared to baselines. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation (2025.acl-long)
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| Challenge: | Existing methods for LLM alignment optimize tokens using a sparse, response-level reward or preference annotation. |
| Approach: | They propose an RLHF-equivalent distillation method for token-level reward optimization that incorporates the reward learned by DPO into the RLHG objective and builds a token-based teacher distribution. |
| Outcome: | The proposed method bridges the accuracy gap between the reward from the DPO model and the pure reward model by building a contrastive DPO reward with a normal and a reverse DPO. |
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism (2020.coling-main)
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| Challenge: | Existing non-autoregressive models generate target words in parallel, but with a large latency due to the left-to-right dependency. |
| Approach: | They propose to train a conditional masked translation model and refine results within several iterations to remedy a flawed translation by non-autoregressive models. |
| Outcome: | The proposed model outperforms state-of-the-art models by over 1 BLEU while using less training computations. |
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning (2024.findings-emnlp)
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| Challenge: | Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. |
| Approach: | They propose a novel approach that leverages In-Context Learning to integrate graph data and task-specific information into large language models (LLMs) they employ a Graph Neural Network-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. |
| Outcome: | Experiments on three tasks and seven LLMs show that AskGNN performs better than existing methods. |
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)
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| Challenge: | Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs. |
| Approach: | They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory. |
| Outcome: | The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory. |
CloneMem: Benchmarking Long-Term Memory for AI Clones (2026.acl-long)
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| Challenge: | Existing memory benchmarks rely on user–agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. |
| Approach: | They propose a benchmark for evaluating long-term memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. |
| Outcome: | Experiments show that existing memory benchmarks struggle in this setting, highlighting open challenges for life-grounded personalized AI. |
Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection (2023.acl-long)
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| Challenge: | Existing methods for multi-modal fake news detection neglect the fact that some label-specific features cannot generalize well to the testing set, thus suffering from the latent data bias. |
| Approach: | They propose a Causal intervention and Counterfactual reasoning based debiasing framework for multi-modal fake news detection that eliminates the image-only bias by deducting the direct effect of the image from the total effect on labels. |
| Outcome: | The proposed framework eliminates the psycholinguistic bias in the text and the bias of inferring news label based on only image features. |
Mind’s Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have achieved significant advances in natural language processing, but their scale and computational demands pose challenges to their practical application. |
| Approach: | They propose a method for distilling the self-evaluation capability from LLMs into SLMs and advocate for more comprehensive thinking by incorporating multiple distinct CoTs and self-estimation outputs. |
| Outcome: | The proposed method significantly improves the performance of distilled SLMs on three NLP benchmarks. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)
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| Challenge: | Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know". |
| Approach: | They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content. |
| Outcome: | The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
FIER: Fine-Grained and Efficient KV Cache Retrieval for Long-context LLM Inference (2025.findings-emnlp)
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Dongwei Wang, Zijie Liu, Song Wang, Yuxin Ren, Jianing Deng, Jingtong Hu, Tianlong Chen, Huanrui Yang
| Challenge: | Key-Value (KV) cache reading latency increases with context lengths hindering LLM inference . important tokens are sparsely distributed across the long context, making existing retrieval inaccurate . |
| Approach: | They propose a method to retain a small fraction of KV cache based on token importance . important tokens are often sparsely distributed across the long context . |
| Outcome: | The proposed method reduces decoding latency by 1.2 to 1.5. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens (2025.findings-acl)
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Shaoshen Chen, Yangning Li, Zishan Xu, Yongqin Zeng, Shunlong Wu, Xinshuo Hu, Zifei Shan, Xin Su, Jiwei Tang, Yinghui Li, Hai-Tao Zheng
| Challenge: | Existing semantic vector-based compression methods do not account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunk. |
| Approach: | They propose a method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression. |
| Outcome: | The proposed method surpasses state-of-the-art methods on long context tasks. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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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. |
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors (2026.findings-acl)
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| Challenge: | Existing LLMs rely on surface-level similarity or manual heuristics to evaluate personalization . Existing evaluation protocols for personalization are lacking sufficient data-driven validation. |
| Approach: | They propose a benchmark to assess personalization by mining CIPDs to quantify individual preferences. |
| Outcome: | The proposed benchmark provides a more comprehensive and discriminative standard than generic metrics. |
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning (2025.findings-acl)
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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, Xuming Hu
| Challenge: | Existing methods for MU forget quality and model utility are not fully explored for safety in MLLMs. |
| Approach: | They propose a safety unlearning benchmark for MLLMs to measure over-forgetting . they propose MU methods to forget quality and model utility . |
| Outcome: | The proposed method reduces over-forgetting by 79.5% while maintaining forget quality and model utility. |
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System (2025.findings-acl)
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| Challenge: | X-WebAgentBench evaluates the planning and interaction performance of language agents across multiple languages. |
| Approach: | They propose a multilingual agent benchmark that evaluates the interaction performance of language agents across multiple languages. |
| Outcome: | The proposed benchmark evaluates the planning and interaction performance of language agents across multiple languages. |
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)
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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah
| Challenge: | Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns. |
| Approach: | They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP. |
| Outcome: | Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines. |
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model (2025.acl-long)
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| Challenge: | Existing approaches to optimize agent performance by incorporating entire historical action-observation pairs into LLMs are redundant in long-horizon tasks. |
| Approach: | They propose a framework that leverages subgoals as memory chunks to manage working memory of LLM-based agents hierarchically. |
| Outcome: | The proposed framework achieves a twofold increase in success rate and reduces the average number of steps required by 3.8. |
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)
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| Challenge: | Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability. |
| Approach: | They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning. |
| Outcome: | The proposed approach improves on two data sets and shows 4.8% gain on the PMR. |
MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)
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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. |
A Multi-modal Debiasing Model with Dynamical Constraint for Robust Visual Question Answering (2023.findings-acl)
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| Challenge: | Recent studies have shown that many well-developed Visual Question Answering systems suffer from bias problem. |
| Approach: | They propose a way to mitigate bias problem by subtracting bias score from standard VQA base score. |
| Outcome: | The proposed method improves on the VQA v2.0 and VQA-CP V2,0 datasets. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)
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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo
| Challenge: | Existing large language models (LLMs)-backed generative search engines may not always be accurate. |
| Approach: | They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access. |
| Outcome: | The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval. |
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning? (2025.findings-acl)
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| Challenge: | Analogical reasoning is a unique ability of humans to address unfamiliar challenges by transferring strategies from relevant past experiences. |
| Approach: | They propose to use self-generated random examples to improve performance on a variety of reasoning tasks by incorporating relevant examples from relevant past experiences. |
| Outcome: | The proposed methods achieve comparable or even better performance on GSM8K with random biological examples. |
Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models (2025.findings-acl)
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| Challenge: | Existing research focuses on object-level or attribute-level hallucinations, neglecting the more complex relation hallucinosities. |
| Approach: | They propose a comprehensive benchmark targeting relation hallucinations comprising over 20,000 real-world samples and a confidence-based mitigation strategy which reduces the halluciation rate by an average of 9.75% across three datasets. |
| Outcome: | The proposed approach reduces the hallucination rate by an average of 9.75% across three datasets, including Reefknot. |
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs (2024.naacl-demo)
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| Challenge: | Recent advances in machine learning (ML) are attributed to large language models (LLMs), but their escalating memory requirements require developers to partition a large model to distribute it across multiple GPUs or TPUs. |
| Approach: | They propose a lightweight and user-friendly tool to automate distributed training and inference for LLMs and to simplify ML pipeline development. |
| Outcome: | The proposed tool automates distributed training and inference for LLMs, and simplifies ML pipeline development. |
TextBox 2.0: A Text Generation Library with Pre-trained Language Models (2022.emnlp-demos)
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Tianyi Tang, Junyi Li, Zhipeng Chen, Yiwen Hu, Zhuohao Yu, Wenxun Dai, Wayne Xin Zhao, Jian-yun Nie, Ji-rong Wen
| Challenge: | TextBox 2.0 focuses on the use of pre-trained language models (PLMs) to generate text. |
| Approach: | They propose a library that integrates pre-trained language models into 13 common text generation tasks and 83 datasets. |
| Outcome: | The proposed library covers 13 common text generation tasks and their corresponding datasets and incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLM. |
ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance (2026.acl-long)
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Haoran Li, Yulin Chen, Huihao Jing, Wenbin Hu, Tsz Ho Li, Chanhou Lou, Hong Ting Tsang, Sirui Han, Yangqiu Song
| Challenge: | Existing approaches to contextualize safety and privacy assessments assume the availability of complete and clear context, whereas real-world contexts tend to be ambiguous and incomplete. |
| Approach: | They propose a semi-rule-based framework that leverages large language models to ground the input context in the legal domain and explicitly identify both known and unknown factors for legal compliance. |
| Outcome: | The proposed framework can significantly improve existing baselines without training and can identify the ambiguous and missing factors. |
Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation (2026.acl-long)
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Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao, Shuicheng Yan
| Challenge: | Recent agentic search frameworks are text-centric, overlooking multimodal evidence . a pressing task is multimodal long-form generation, a new paper argues . |
| Approach: | They propose a unified agentic framework for grounded multimodal long-form generation. |
| Outcome: | The proposed framework is based on a unified agentic framework for grounded multimodal long-form generation. |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents (2026.acl-long)
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Xiangyu Wu, Yuwei Hu, Tianyu Cui, Yueying Tian, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yang Yang, Jianfeng Lu
| Challenge: | Existing agent benchmarks fail to evaluate an agent's real-world capacity to handle CAPTCHA . Existing benchmarks ignore this practical challenge, failing to evaluate agents' ability to handle complex visual CAPTchas. |
| Approach: | They propose a benchmark annotated with Weighted Pass Rate and a new metric to measure agent's ability to handle CAPTCHA. |
| Outcome: | The proposed benchmark outperforms current state-of-the-art closed-source models on mirrorCAPTCHA and achieves 9.4% higher average weighted pass rate and 2.13% higher average Completion degree. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
| Approach: | They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data. |
| Outcome: | The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc. |
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)
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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
| Challenge: | Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks. |
| Approach: | They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs. |
| Outcome: | The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks. |
TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval (2026.acl-long)
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| Challenge: | Composed Image Retrieval (CIR) is an image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image and modification text. |
| Approach: | They propose a text-oriented entity mapping architecture that allows users to use a reference image and modification text to retrieve a target image. |
| Outcome: | The proposed framework is superior in both original and multi-modification scenarios while maintaining an optimal balance between retrieval accuracy and computational efficiency. |
Open-Set Semi-Supervised Text Classification via Adversarial Disagreement Maximization (2024.acl-long)
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| Challenge: | Open-Set Semi-Supervised Text Classification (OSTC) aims to train a classification model on a limited set of labeled texts along with plenty of unlabeled examples. |
| Approach: | They propose to train a classification model on a limited set of labeled texts alongside plenty of unlabeled examples that include both in-distribution and out-of-difference examples. |
| Outcome: | The proposed model improves on outlier detection and abnormal example detection and calibration. |
RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction (2026.findings-acl)
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Haonan Bian, Zhiyuan Yao, Sen Hu, Zishan Xu, Shaolei Zhang, Yifu Guo, Ziliang Yang, Xueran Han, Huacan Wang, Ronghao Chen
| Challenge: | Existing benchmarks focus on casual conversation or task-oriented dialogue, failing to capture “long-term project-oriented” interactions where agents must track evolving goals. |
| Approach: | They propose a benchmark that simulates the dynamic evolution of memory in real-world projects. |
| Outcome: | The proposed benchmarks simulate the dynamic evolution of memory in real-world projects. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time (2026.findings-acl)
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Mingkuan Zhao, Yide Gao, Wentao Hu, Suquan Chen, Tianchen Huang, Zhenhua An, Zetao Chang, Xiayu Sun, Yuheng Min
| Challenge: | Existing mitigation strategies rely on suppressing specific neuron activations or employing computationally expensive contrastive decoding mechanisms, which often result in increased perplexity or significantly elevated inference latency. |
| Approach: | They propose a lightweight inference-time intervention method grounded in the perspective of residual stream signal dynamics to resolve the signal attenuation of external evidence during its propagation through deep networks. |
| Outcome: | The proposed method improves contextual faithfulness across multiple factual consistency and strong knowledge-conflict tasks while maintaining the model’s general language understanding capabilities. |
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing (2020.acl-main)
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| 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. |
Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)
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| Challenge: | Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world. |
| Approach: | They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge . |
| Outcome: | The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation. |
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs (2025.acl-long)
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Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Hongru Wang, Sheng Bi, Yongrui Chen, Tongtong Wu, Jeff Z. Pan
| Challenge: | Attributed Question Answering (AQA) has attracted wide attention, but there are several limitations in evaluating the attributions. |
| Approach: | They propose a large-scale benchmark containing comprehensive attribution categories . they compare 25 automatic evaluators with human evaluers and tested LLM evalators . |
| Outcome: | The proposed method can compare attributions with subtle differences and provide feedback to improve them. |
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)
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| Challenge: | Existing privacy studies focus on sub-fields, but they focus on a few sub-domains. |
| Approach: | They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations. |
| Outcome: | The proposed checklist covers social identities, private attributes, and existing privacy regulations. |
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)
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| 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. |
From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)
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| Challenge: | Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling. |
| Approach: | They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language. |
| Outcome: | The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. |
PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
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| Challenge: | Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. |
| Approach: | They propose a dataset that provides a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. |
| Outcome: | The proposed model significantly improves proactive questioning capacity, conversation depth, and response quality. |
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)
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| Challenge: | Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered . |
| Approach: | They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts. |
| Outcome: | The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script. |
Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)
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| Challenge: | Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. |
| Approach: | They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme. |
| Outcome: | The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning (D19-57)
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| Challenge: | Using natural language processing to discover and mine drug-related knowledge from text has been a hot topic in recent years. |
| Approach: | They propose to use a pre-trained biomedical language representation model to extract mutation-disease knowledge from PubMed. |
| Outcome: | The proposed approaches achieve 0.60 (ranks 1) and 0.25 (rank 2) on task 1 and task 2 respectively in terms of F1 metric. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning (2025.emnlp-main)
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| Challenge: | Recent studies have introduced legal theories into LLM workflows to improve their understanding of legal texts and reasoning accuracy. |
| Approach: | They evaluate an expert-annotated four-element knowledge base covering 155 criminal charges. |
| Outcome: | The proposed model can be used to analyze criminal charges and retrieve them in legal cases. |
LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)
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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao
| Challenge: | Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL). |
| Approach: | They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. |
| Outcome: | The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. |
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)
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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu
| Challenge: | Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks. |
| Approach: | They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi . |
| Outcome: | The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level . |
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System (2024.findings-acl)
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| Challenge: | Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics. |
| Approach: | They propose a method to overcome catastrophic forgetting in task-oriented dialogue systems by tracing their hyper-gradients and a retraining strategy that uses influential exemplars for periodic retrains. |
| Outcome: | The proposed method achieves state-of-the-art on the largest CL benchmark of ToDs in terms of all metrics. |
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning (2023.findings-acl)
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| Challenge: | Existing multimodal fusion methods ignore inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance. |
| Approach: | They propose a multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. |
| Outcome: | The proposed method outperforms current state-of-the-art multimodal learning methods on image-text classification benchmarks UPMC-Food-101 and N24News. |
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021.findings-emnlp)
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| Challenge: | Existing conversational systems are agent-centric, which assumes the user utterances will closely follow the system ontology. |
| Approach: | They build a dataset that maps user preferences to an ontology and model user preferences as estimated distributions over the system ontologies. |
| Outcome: | The proposed system can be used to push existing research from agent-centric to user-centric. |
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)
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Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang
| Challenge: | Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences. |
| Approach: | They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions. |
| Outcome: | The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. |
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space (2025.findings-acl)
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| Challenge: | Existing methods for instruction-tuning datasets prioritize instance quality and use heuristic rules to maintain diversity. |
| Approach: | They propose a method that quantifies diversity based on the distribution of information within a label graph. |
| Outcome: | The proposed method outperforms state-of-the-art methods on 5% Tulu3 datasets and base models. |
SAM Decoding: Speculative Decoding via Suffix Automaton (2025.acl-long)
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| Challenge: | Speculative decoding (SD) methods are inefficient and rely on single retrieval resources. |
| Approach: | They propose a retrieval-based speculative decoding method that adapts the suffix automaton for efficient draft generation by utilizing the generating text sequence and static text corpus. |
| Outcome: | The proposed method can find the longest suffix match and can be integrated with existing methods to generalize to broader domains. |
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Long-tail Knowledge (2026.acl-long)
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| Challenge: | Existing studies have shown that large language models can handle knowledge with varying familiarity. |
| Approach: | They propose a benchmark to evaluate multi-hop question answering on new and tail knowledge . they use RAG to integrate external knowledge into large language models . |
| Outcome: | The proposed benchmark evaluates the multi-hop reasoning ability of large language models . it primarily evaluates their ability to handle knowledge with different levels of familiarity . |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
Integral Transformer: Denoising Attention, Not Too Much Not Too Little (2025.emnlp-main)
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| Challenge: | Existing methods to reduce attention noise by integrating signals from logit distributions are prone to attention noise. |
| Approach: | They propose a self-attention mechanism that integrates signals from the logit distribution to denoise attention. |
| Outcome: | The proposed model outperforms vanilla, Cog, and Differential attention variants on knowledge and reasoning benchmarks. |
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)
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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event . |
| Approach: | They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s. |
| Outcome: | The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance . |
SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)
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| Challenge: | Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity. |
| Approach: | They propose a framework for the minimalist rectification of non-compliant image ads. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency. |
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition (2023.acl-long)
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| Challenge: | Audio-visual speech recognition (AVSR) leverages multimodal signals to understand human speech. |
| Approach: | They propose an adversarial network to refine frame-level modality-invariant representations to bridge the distribution gap between modalities. |
| Outcome: | The proposed approach outperforms the state-of-the-art on public benchmarks LRS3 and LRS2 on the modalities of AVSR. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)
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| Challenge: | Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words. |
| Approach: | They propose to fit a representation function to predict an oracle embedding vector based on limited contexts. |
| Outcome: | The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized. |
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)
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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai
| Challenge: | Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP) |
| Approach: | They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic. |
| Outcome: | The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism. |
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)
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| Challenge: | Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history . |
| Approach: | They propose to use unlabeled data to make further improvements using contrastive co-training paradigm. |
| Outcome: | The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios. |
Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)
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| Challenge: | Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness. |
| Approach: | They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation. |
| Outcome: | Experiments on entity alignment and type inference show the proposed method is effective and efficient. |
Harnessing and Evaluating the Intrinsic Extrapolation Ability of Large Language Models for Vehicle Trajectory Prediction (2025.naacl-long)
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| Challenge: | Emergent abilities of large language models (LLMs) have advanced their application in autonomous vehicle research. |
| Approach: | They propose a framework that leverages LLMs’ built-in extrapolation capabilities for vehicle trajectory prediction, enabling them to understand traffic agents' behavior and interactions over time. |
| Outcome: | The proposed framework enables off-the-shelf, frozen LLMs to achieve competitive trajectory prediction performance with qualitative analyses revealing their enhanced understanding of complex, multi-agent traffic scenarios. |
MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search (2025.findings-emnlp)
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| Challenge: | MCTS-RAG combines structured reasoning with adaptive retrieval . compared to conventional MCTLs, MCTR-RAg relies on internal model knowledge without external facts . |
| Approach: | a new approach integrates retrieval-augmented generation and Monte Carlo Tree Search to enhance reasoning capabilities of small language models. |
| Outcome: | MCTS-RAG integrates retrieval-augmented generation and Monte Carlo Tree Search to improve reasoning paths. |
Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
| Challenge: | Recent advances in neural models have shown promising progress on this task, but key challenges remain . |
| Approach: | They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial. |
Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation (2026.findings-acl)
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Zhen Bi, Zhenlin Hu, Xueshu Chen, Mingyang Chen, Cheng Deng, Yida Xue, Zhen Wang, Qing Shen, Ningyu Zhang, Jungang Lou
| Challenge: | Existing data-centric paradigms equate quality with factuality or diversity and ignore the internal logical complexity of training samples. |
| Approach: | They propose a density-aware re-cognizing optimization strategy that prioritizes high-density logical samples to align training with the model's reasoning boundary. |
| Outcome: | The proposed metric outperforms existing methods and improves reasoning performance without increasing total data volume. |
Prompt-based Text Entailment for Low-Resource Named Entity Recognition (2022.coling-1)
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| Challenge: | Pre-trained Language Models (PLMs) have been applied in NLP tasks but require labeled data for downstream tasks. |
| Approach: | They propose a method for low-resource named entity recognition that uses prompts to get entailment scores for each candidate and inject tagging labels into prompts. |
| Outcome: | The proposed method achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD datasets in low-resource settings. |
Learnable Privacy Neurons Localization in Language Models (2024.acl-short)
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| Challenge: | Large Language Models (LLMs) memorize and disclose private information, especially Personally Identifiable Information (PII) concerns regarding privacy and security within human society remain poorly understood. |
| Approach: | They propose to use learnable binary weight masks to localize PII-sensitive neurons within LLMs by deactivating localized privacy neurons. |
| Outcome: | The proposed method localizes PII-sensitive neurons across all layers and shows the property of PI I specificity. |
Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems (2026.acl-long)
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| Challenge: | Existing personalized dialogue systems struggle to reconcile unbounded interactions with finite context constraints. |
| Approach: | They propose a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling. |
| Outcome: | The proposed framework outperforms existing systems in suppressing contextual noise and persona inconsistency. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)
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Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise . |
| Approach: | They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following. |
| Outcome: | The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |
Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework (2026.acl-long)
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Chenyuan Zhang, Qiguang Chen, Xie Chen, Zhuotao Tian, Bowen Xing, Meishan Zhang, Libo Qin, Baotian Hu, Min Zhang
| Challenge: | Existing methods for cross-lingual chain-of-thought (XCoT) with self-consistency are costly due to extensive sampling of full trajectories across languages. |
| Approach: | They propose a cross-lingual chain-of-thought framework that minimizes redundancy in token usage and latency. |
| Outcome: | Experiments on polymath show that UL-XCoT reduces decoding token costs and latency by 50% . UL XCot also aggregates remaining high-quality reasoning paths via voting . |
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions (2026.acl-long)
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Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang, Ronghao Chen
| Challenge: | Existing long-horizon memory benchmarks use multi-turn dialogues or synthetic user histories . despite rapid progress on long-term memory evaluation, there are gaps in existing benchmarks . |
| Approach: | They propose a long-form autobiographical narrative benchmark that reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions. |
| Outcome: | The proposed benchmarks build from long-form autobiographical narratives . they show that retrieval-augmented systems improve factual accuracy while errors persist on temporally grounded explanations and higher-level inferences. |
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation (2021.acl-demo)
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Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen
| Challenge: | TextBox is an open-source text generation framework that is modularized and extensible. |
| Approach: | They propose to provide a unified, modularized, and extensible text generation framework that implements 21 text generation models on 9 benchmark datasets. |
| Outcome: | The proposed framework implements 21 models on 9 benchmark datasets and is available under the Apache License 2.0 license. |
Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)
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| Challenge: | Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions. |
| Approach: | They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph. |
| Outcome: | The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm. |
PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance (2025.acl-long)
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Haoran Li, Wenbin Hu, Huihao Jing, Yulin Chen, Qi Hu, Sirui Han, Tianshu Chu, Peizhao Hu, Yangqiu Song
| Challenge: | Recent advances in generative large language models (LLMs) have enabled wider applicability, accessibility, and flexibility. |
| Approach: | They propose a contextual privacy evaluation benchmark that covers the entire relevant social context through private information flows. |
| Outcome: | The proposed benchmarks cover legal compliance, real court cases, privacy policies, and synthetic data. |
P2 Law: Scaling Law for Post-Training After Model Pruning (2025.acl-long)
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| Challenge: | Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs). |
| Approach: | They propose to use model pruning techniques to maintain high performance while reducing hardware requirements for large language models (LLMs). |
| Outcome: | The proposed model pruning law can be generalized to larger dataset sizes, larger model sizes, and higher pruning rates, offering valuable insights for resource allocation in pruned LLMs. |
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Multimodal large language models combine visual and textual data for tasks like image captioning and visual question answering. |
| Approach: | They propose temperature scaling and iterative prompt optimization to calibrate MLLMs and enhance model reliability. |
| Outcome: | The proposed techniques improve MLLMs and improve model reliability. |
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)
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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen
| Challenge: | Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance. |
| Approach: | They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents. |
| Outcome: | The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
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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. |
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)
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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
| Challenge: | Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench . |
| Approach: | They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool . |
| Outcome: | The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions . |
Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling (2026.findings-acl)
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| Challenge: | Existing retrieval-augmented strategies for large language models fail to capture dynamic reasoning required to resolve execution failures. |
| Approach: | They propose a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. |
| Outcome: | The proposed framework improves model accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%. |
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)
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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
| Challenge: | a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias. |
| Approach: | They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness . |
| Outcome: | The proposed evaluation metric is based on two components: desirability and information mass. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)
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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu
| Challenge: | Recent advances in pre-trained language models have made it possible to generate human-like text. |
| Approach: | They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached. |
| Outcome: | The proposed game lacks incentives and relies on players to explore on their own. |
CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality, multi-step reasoning are limited . we present a new framework for synthesising rigorous, cognitively diverse problems . |
| Approach: | They propose a cognitive atom-based framework for synthesizing mathematically rigorous problems. |
| Outcome: | The proposed framework outperforms existing methods in accuracy, reasoning depth and diversity while exceeding the difficulty of AIME. |
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models (2025.coling-main)
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Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
| Challenge: | Activation sparsity is a promising paradigm for accelerating model inference . few large language models achieve high activation spar and comparable performance . |
| Approach: | They propose a method to achieve activation sparsity and acceleration in large language models . they introduce ReLU activation and adopt progressive sparse regularization . |
| Outcome: | The proposed method achieves high activation sparsity and comparable model performance. |
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)
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Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang
| Challenge: | Existing methods to train a model on a mixture of domain datasets require separate correction language models. |
| Approach: | They propose a multi-task correction MoE that trains experts to become an "expert" of speech-to-text, language-totext and vision-to text datasets by learning to route each dataset’s tokens to its mapped expert. |
| Outcome: | The proposed model outperforms GPT-3.5 and Claude-3.5-Sonnet on the Open ASR Leaderboard and reaches an average relative 5.0% WER reduction and substantial improvements in BLEU scores. |
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency (2025.emnlp-main)
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Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao
| Challenge: | Existing methods for instruction tuning rely on expensive human-annotated seed data or powerful external teacher models. |
| Approach: | They propose a framework that achieves fully seed-free instruction tuning by employing a dual self-training loop where two models are bootstrapped solely from raw, unlabeled text. |
| Outcome: | The proposed framework outperforms seed-driven back-translation baselines and achieves comparable performance to strongly supervised methods. |
Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)
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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian
| Challenge: | ambiguity, polysemy, or uncertainty remain significant challenges in natural language processing. |
| Approach: | They introduce a framework that integrates LLM semantic priors with continuous fuzzy membership degrees to create an explicit interaction between probability-based reasoning and fuzzy membership reasoning. |
| Outcome: | The proposed framework integrates semantic priors with continuous fuzzy membership degrees . it allows ambiguous inputs to be gradually transformed into clear and interpretable decisions . |