Papers by He Zhou
Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)
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| Challenge: | Existing LLMs struggle to reliably detect subtle reasoning errors in ASAS tasks. |
| Approach: | They propose a dual-model framework with a dedicated Critic model trained for effective reflection that generates precise verbal feedback. |
| Outcome: | The proposed framework outperforms existing ASAS benchmarks and provides valuable insights into the performance of the proposed framework. |
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
| Outcome: | The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)
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| Challenge: | Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures. |
| Approach: | They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues. |
| Outcome: | The proposed defense improves safety alignment while maintaining helpfulness. |
Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack (2025.findings-naacl)
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| Challenge: | Visual-Language Pre-training (VLP) models are vulnerable to adversarial examples . previous studies have focused on improving adversariality of models . |
| Approach: | They propose a local shuffle and sample-based attack that randomly shufts one of the local image blocks and generates adversarial images and samples around them. |
| Outcome: | The proposed attack outperforms other advanced attacks on Large Vision-Language Models and outperformed previous attacks on Visual-Langue Pre-training models. |
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
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| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
| Outcome: | The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships. |
Distilling ChatGPT for Explainable Automated Student Answer Assessment (2023.findings-emnlp)
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| Challenge: | Existing automated student answer assessment models lack explainable and faithful feedback. |
| Approach: | They propose a framework that leverages ChatGPT for student answer scoring and rationale generation. |
| Outcome: | The proposed method improves the overall QWK score by 11% compared to ChatGPT. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)
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| Challenge: | a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance . |
| Approach: | They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance . |
| Outcome: | The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies focus on improving the performance of domain-specific models based on the target dataset. |
| Approach: | They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities. |
| Outcome: | The proposed model obtains new state-of-the-art over 19 datasets. |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
Don’t Half-listen: Capturing Key-part Information in Continual Instruction Tuning (2025.acl-long)
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Yongquan He, Wenyuan Zhang, Xuancheng Huang, Peng Zhang, Lingxun Meng, Xiang Zhou, Ke Zeng, Xunliang Cai
| Challenge: | Existing methods to improve instruction tuning for large language models may cause catastrophic forgetting (CF) CF is a problem where previously learned abilities are degraded . |
| Approach: | They propose a continual instruction tuning method that uses key-part information gain to replay data and refine training objective. |
| Outcome: | The proposed method achieves superior performance on both seen and held-out tasks. |
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts. |
| Approach: | They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
| Outcome: | The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models (2025.acl-long)
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| Challenge: | Existing RAG frameworks rely on Automatic Speech Recognition to process speech input, which discards crucial audio information and increases computational overhead. |
| Approach: | They propose a retrieval augmented generation framework with native, end-to-end audio support that integrates audio and text into a unified knowledge representation. |
| Outcome: | The proposed framework can perform 10x faster than current pipelines while delivering 10x acceleration. |
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)
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| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
| Approach: | They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework. |
| Outcome: | The proposed model improves slot filling and domain classification performance in a multi-task framework. |
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)
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| Challenge: | Numerical Question Answering is the task of answering questions that require numerical capabilities. |
| Approach: | They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. |
| Outcome: | The proposed approach relieves existing systems’ lack of robust numerical capabilities. |
On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
| Approach: | They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective. |
| Outcome: | The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks. |
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)
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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li
| Challenge: | Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly. |
| Approach: | They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show . |
| Outcome: | The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models. |
FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)
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| Challenge: | Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation. |
| Approach: | They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning. |
| Outcome: | Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)
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| Challenge: | Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic . |
| Approach: | They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel. |
| Outcome: | The proposed framework improves FL accuracy with minimal costs. |
Order Doesn’t Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2025.emnlp-main)
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| Challenge: | Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inferences. |
| Approach: | They propose an order-centric data augmentation framework based on commutativity in logical reasoning that randomly shuffles independent premises to introduce condition order augmentation. |
| Outcome: | The proposed framework improves LLMs’ reasoning performance and adaptability to diverse logical structures. |
Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (2024.findings-emnlp)
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| Challenge: | Existing methods for generating rationales that justify scoring decisions are not accurate and often contain hallucinated information. |
| Approach: | They propose a framework capable of generating more faithful rationales and matching performance with classifier-based scoring systems. |
| Outcome: | The proposed framework achieves 38% improvement in QWK score compared to prior work . it can be used to match performance with classifier-based scoring systems . |
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors (2024.acl-long)
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| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
| Approach: | They propose to evaluate the robustness of current detection systems by using black-box text perturbation methods and adversarial learning experiments. |
| Outcome: | The proposed methods assess the robustness of current detection models across perturbation granularities and the impact of perturbation data augmentation on the robustity of AI-text detectors. |
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)
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| Challenge: | Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching . |
| Approach: | They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings. |
| Outcome: | The proposed method outperforms existing RAG methods in both in- and out-of-domain settings. |
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
| Challenge: | Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences. |
| Approach: | They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks . |
| Outcome: | The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency. |
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)
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| Challenge: | Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging. |
| Approach: | They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. |
| Outcome: | The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton
| Challenge: | commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools . |
| Approach: | They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression . |
| Outcome: | The proposed approach outperforms human experts in medical examinations on diverse datasets. |
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure. |
| Approach: | They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
| Outcome: | The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant. |
| Approach: | They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality. |
| Outcome: | The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss. |
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
| Outcome: | The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers. |
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
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| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
| Approach: | They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances. |
| Outcome: | Experiments on ATIS domain and Python show that with extra unlabeled data, StructVAE outperforms strong supervised models. |
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)
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| Challenge: | Experimental results show that cross-language data expansion results in performance degradation. |
| Approach: | They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus. |
| Outcome: | The proposed method improves ED performance by 1.6% over the straight data combination. |
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)
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| Challenge: | Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states. |
| Approach: | They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection. |
| Outcome: | The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show . |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. |
| Approach: | They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution. |
| Outcome: | The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets. |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples. |
| Approach: | They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept . |
| Outcome: | The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples. |
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)
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| Challenge: | Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information. |
| Approach: | They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
SCVQ: Sparse-Compensated Vector Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing vector quantization methods incur inference overhead due to massive codebook storage and intensive index lookups. |
| Approach: | They propose a framework for vector quantization that incorporates a salience-aware weighted K-means clustering scheme with symmetry constraints to reduce codebook size and indexing costs. |
| Outcome: | The proposed framework achieves a perplexity of 5.78 on WikiText-2 for LLaMA-2-7B at 2-bit quantization while delivering a 1.4 speedup over existing baselines. |
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)
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| Challenge: | Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog. |
| Approach: | They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation. |
| Outcome: | The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
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| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)
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| Challenge: | Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios. |
| Approach: | They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models. |
| Outcome: | The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 . |
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for hallucination detection are intentionally generated by large language models (LLMs) however, many focus on factuality while ignoring faithfulness. |
| Approach: | They propose a dialogue-level hallucination evaluation benchmark for large language models . they integrate the topic into prompts and facilitate a dialog between two LLMs . |
| Outcome: | The proposed benchmark covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucines. |
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)
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| Challenge: | Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed" |
| Approach: | They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices . |
| Outcome: | The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment. |
Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)
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| Challenge: | Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks . |
| Approach: | They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario. |
| Outcome: | The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
Leveraging Web-Crawled Data for High-Quality Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Currently, large language models are fine-tuned using expensive human-annotated data or GPT-4 generated data. |
| Approach: | They propose to use web-crawled data to train a language model on a smaller set of data . their results show that the model can convert web data with irregular formats into high-quality ones . |
| Outcome: | The proposed model outperforms open-source models larger than 32B and outperformed open-sourced models such as GPT-3.5. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)
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| Challenge: | Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based. |
| Approach: | They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation. |
| Outcome: | The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation . |
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)
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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han
| Challenge: | Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates. |
| Approach: | They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. |
| Outcome: | The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets. |
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models (2024.findings-emnlp)
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| Challenge: | Phrases are fundamental linguistic units through which humans convey semantics. |
| Approach: | They assess the capacity of API-based large language models to comprehend phrase semantics . they use three human-annotated datasets to analyze their results . |
| Outcome: | The proposed model outperforms embedding-based methods in phrase semantic reasoning tasks . the proposed model does not show significant advantage over fine-tuned methods . |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
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| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
DATA-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods to improve sentence representation learning (SRL) ignore the potential interference problems across tasks and instances. |
| Approach: | They propose a multi-task instruction tuning method that arranges the order of multi- task data for training to minimize interference risks. |
| Outcome: | The proposed method can boost the performance of state-of-the-art methods. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
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| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)
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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou
| Challenge: | Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy. |
| Approach: | They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems. |
| Outcome: | The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better. |
LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)
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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang
| Challenge: | Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics. |
| Approach: | They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks. |
| Outcome: | The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring. |
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods do not use graph contextualized knowledge. |
| Approach: | They propose to model subgraphs in a medical KG and integrate it with a pre-trained language model to do knowledge generalization. |
| Outcome: | The proposed model achieves state-of-the-art on several medical NLP tasks . it improves on MedERNIE, and the proposed model is effective . |
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)
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| Challenge: | Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input . |
| Approach: | They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input . |
| Outcome: | The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE. |
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)
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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao
| Challenge: | Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions. |
| Approach: | They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels . |
| Outcome: | The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing. |
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)
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| Challenge: | Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks is suboptimal. |
| Approach: | They propose a generator-validator paradigm to iteratively generate-then-validate training data from language models to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data. |
| Outcome: | The proposed model outperforms vanilla language models on diverse table tasks and can match or surpass GPT-4 level quality. |
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)
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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang
| Challenge: | Tabular data analysis is performed everyday across various domains. |
| Approach: | They propose to use a dataset of 467k tables with supervision labels for four types of field metadata. |
| Outcome: | The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information. |
BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)
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| Challenge: | Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged. |
| Approach: | They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately. |
| Outcome: | The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters. |
AudioPrivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes (2026.findings-acl)
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| Challenge: | Speech signals convey abundant speaker-related metadata, yet current privacy research focuses on identity-centric voiceprint protection, leaving sensitive Speaker Attribute Privacy (SAP) underexplored. |
| Approach: | They propose a large-scale Chinese dataset to evaluate speaker-related privacy leakage . the dataset includes 227.3 hours of audio from 1,000 speakers . |
| Outcome: | The proposed model systematically evaluates speaker-related privacy leakage in everyday scenarios. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)
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| Challenge: | Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making. |
| Approach: | They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources. |
| Outcome: | The proposed framework improves on factual-aware medical QA benchmarks. |
LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)
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| Challenge: | Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands. |
| Approach: | They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence . |
| Outcome: | The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence. |
EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)
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| Challenge: | Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM. |
| Approach: | They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning. |
| Outcome: | The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks. |
ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)
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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu
| Challenge: | Existing studies focus on language-based premises and deduce valid conclusions from visual observations. |
| Approach: | They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning . |
| Outcome: | Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts . |
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
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Rahul Goel, Waleed Ammar, Aditya Gupta, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Chuan He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, Zhou Yu
| Challenge: | PRESTO dataset contains 550K contextual multilingual conversations between humans and virtual assistants. |
| Approach: | They propose to use a dataset of 550K contextual multilingual conversations between humans and virtual assistants to study some of the more challenging aspects of parsing realistic conversations. |
| Outcome: | The dataset contains 550K contextual conversations between humans and virtual assistants. |
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor (2026.findings-acl)
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Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, JingYuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He
| Challenge: | Existing models focus on a single therapy, but complex cases require flexible strategies among various therapies. |
| Approach: | They propose a multi-session, multi-therapy, and highly realistic benchmark . it is designed to address three key challenges: 1) can we train a highly realistic AI counselor? 2) How to systematically evaluate an AI counselor?" |
| Outcome: | The proposed benchmark is annotated with extensive professional skills and includes over 677 meta-skills and 4577 atomic skills. |
TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)
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| Challenge: | Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting. |
| Approach: | They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory. |
| Outcome: | Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities. |
Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions (2022.emnlp-industry)
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Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, Yong Zhang
| Challenge: | Existing systems for operations research use NLP to suggest formulations of optimization problems. |
| Approach: | They propose an augmented intelligence system that can be used to simplify and enhance the modeling experience for operations research. |
| Outcome: | The proposed system validates and edits the proposed formulations with a dataset of linear programming problems drawn from various application domains. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
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| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)
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| Challenge: | Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android. |
| Approach: | They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs. |
| Outcome: | The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available. |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
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| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
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| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |
P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts (2025.findings-acl)
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| Challenge: | Existing studies on personalized large language models focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making. |
| Approach: | They propose a personalized large language model (LLM) that captures implicit Big Five personality traits and integrates a Personality Specialization Loss to capture individual trait expressions. |
| Outcome: | The proposed model improves on Big Five personality traits and integrates a Personality Specialization Loss (PSL) to capture individual trait expressions. |
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability. |
| Approach: | They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information. |
| Outcome: | The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis. |
CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)
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| Challenge: | Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP). |
| Approach: | They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations. |
| Outcome: | The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent. |
| Approach: | They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. |
| Outcome: | The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features. |
Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions (2026.findings-acl)
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| Challenge: | Existing automation methods follow fixed template filling and cannot support dynamic updates for diverse, user-authored decks. |
| Approach: | They propose a framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. |
| Outcome: | The proposed framework updates content while preserving layout and style while maintaining a strong reference baseline on DynaSlide. |
Implicit Sentiment Analysis with Event-centered Text Representation (2021.emnlp-main)
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| Challenge: | Existing methods for implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models. |
| Approach: | They propose an event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer sentiment polarity. |
| Outcome: | The proposed model can detect sentiment in sentences without sentiment words and is compared to existing models on a benchmark dataset. |
MiCEval: Unveiling Multimodal Chain of Thought’s Quality via Image Description and Reasoning Steps (2025.naacl-long)
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Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, Victor Gutierrez Basulto, Jeff Z. Pan, Hanjie Chen
| Challenge: | Existing methods for evaluating the quality of reasoning steps in multimodal chain-of-thought are lacking. |
| Approach: | They propose a framework to evaluate the correctness of reasoning chains by evaluating the quality of both the description and each reasoning step. |
| Outcome: | The proposed framework improves interpretability and human judgments on four state-of-the-art MLLMs. |
Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)
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Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, Jing Zhang
| Challenge: | a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. |
| Approach: | They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code. |
| Outcome: | The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples . |
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (2025.acl-long)
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| Challenge: | Excessive compression during the prefill phase impairs comprehension of reasoning tasks . SCOPE is a framework that performs KV cache optimization during the decoding and prefill phases . |
| Approach: | They propose a framework that performs optimization during the prefill and decoding phases . they propose enabling a sliding strategy to select essential heavy hitters for the decoding phase . |
| Outcome: | Experiments show that SCOPE can optimize key-value cache for long-context generation tasks . the framework can preserve essential information while minimizing memory usage and transfer . |
C-LLM: Learn to Check Chinese Spelling Errors Character by Character (2024.emnlp-main)
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. |
| Approach: | They propose a Chinese Spell Checking method that learns to check errors Character by Character. |
| Outcome: | The proposed method achieves a 2.1% enhancement in general scenarios and a significant improvement in vertical domain scenarios compared to existing methods. |
InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)
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| Challenge: | Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs. |
| Approach: | They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models. |
| Outcome: | The proposed approach improves the performance of multilingual language models on low-resource languages. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)
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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. |
| Approach: | They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework . |
| Outcome: | The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design . |
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)
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| Challenge: | Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model . |
| Approach: | They propose to transfer knowledge from discourse markers to augment the quality of the NLI model. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets. |
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)
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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu
| Challenge: | Existing studies show that advanced LLMs produce text indistinguishable from human writing. |
| Approach: | They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style. |
| Outcome: | The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts. |
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge (2026.eacl-long)
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Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov
| Challenge: | Existing evaluation metrics and lenient answer matching criteria obscure meaningful comparisons. |
| Approach: | They propose a general method for constructing benchmarks and a method to assess KG-RAG methods under incomplete knowledge. |
| Outcome: | The proposed method systematically assesses KG-RAG methods under incomplete knowledge. |
Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)
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| Challenge: | Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks . |
| Approach: | They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks . |
| Outcome: | The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability. |
ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)
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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi
| Challenge: | LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints. |
| Approach: | They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks. |
| Outcome: | The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time. |
| Approach: | They propose a non-parametric model that encodes subject-centric histories into sequential embeddings. |
| Outcome: | The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings. |
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage (2025.acl-long)
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| Challenge: | Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training. |
| Approach: | They propose a new framework for jailbreaking large vision-language models that uses an encryption-decryption process to mitigate the over-exposure of harmful information. |
| Outcome: | The proposed framework jailbreaks GPT-4o with 99.40% success rates on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. |
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)
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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang
| Challenge: | Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored. |
| Approach: | They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror . |
| Outcome: | The proposed framework reduces interpretation cost by 55% and improves safety-critical features. |
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
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| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)
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Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm. |
| Approach: | They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. |
| Outcome: | The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets. |
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)
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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao
| Challenge: | Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning. |
| Approach: | They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis. |
| Outcome: | The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. |
CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025.findings-acl)
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| Challenge: | Multimodal large language models have demonstrated impressive capabilities in visual reasoning and text generation. |
| Approach: | They propose a multimodal large language model that captures deeper relationships between images and text . they propose CMIE, which uses a Coexistence Relationship Generation strategy and an AS mechanism to detect misinformation. |
| Outcome: | The proposed framework outperforms existing methods in detecting out-of-context misinformation. |
Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)
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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin
| Challenge: | Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities. |
| Approach: | They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias . |
| Outcome: | The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests. |
A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
| Approach: | They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization . |
| Outcome: | The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models . |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
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| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)
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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. |
| Approach: | They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm. |
| Outcome: | The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios. |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
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| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations (2025.findings-acl)
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Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, Xiang Ren
| Challenge: | Language models are widely used in education, yet their ability to tailor responses to learners with varied informational needs and knowledge backgrounds remains under-explored. |
| Approach: | They conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on a benchmark of 13.4K "Why" questions. |
| Outcome: | The proposed model explanations match learners' educational backgrounds only 50% of the time, compared to 79% for lay explanations. |
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
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| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
| Approach: | They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on multi-task dialogues. |
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation (2025.acl-long)
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| Challenge: | Personalized large language models (LLMs) aim to tailor outputs to user preferences . however, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns. |
| Approach: | They propose a progressive learning framework that groups users based on preferences and adapts LLMs in stages. |
| Outcome: | The proposed approach outperforms SOTA models across multiple tasks. |
Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
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| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)
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| Challenge: | In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models. |
| Approach: | They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method. |
| Outcome: | The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity. |
Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models (2025.findings-acl)
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Hao Xiang, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Ben He, Le Sun, Jingren Zhou, Junyang Lin
| Challenge: | Prior research focused on developing data generation methods, while insufficient attention has been paid to quality control mechanisms and often produces inaccurate and unhelpful data. |
| Approach: | They propose an algorithm that automatically generates high-quality preference data, eliminating manual annotation requirements. |
| Outcome: | The proposed algorithm outperforms baselines in human preference alignment and reward optimization. |
Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)
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| Challenge: | Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words . |
| Approach: | They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events. |
| Outcome: | The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions. |
CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing (2024.emnlp-main)
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| Challenge: | Existing methods to improve code generation from natural language descriptions are difficult due to complex structure, subtle bugs, and lack of supplementary contents. |
| Approach: | They propose a framework that enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. |
| Outcome: | The proposed framework improves the quality of complex code generation on the DS-1000 and ClassEval datasets. |
InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)
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| Challenge: | Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks . |
| Approach: | They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking. |
| Outcome: | Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data. |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, but fine-tuning them on the source domain tends to overfit, leading to inferior results on the target domain. |
| Approach: | They propose to pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets and utilize it for cross-domain sentiment analysis tasks without fine-tuning. |
| Outcome: | The proposed model achieves state-of-the-art in all the cross-domain sentiment analysis tasks and can be trained with only 1% samples and better than BERT with 90% samples. |
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)
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| Challenge: | Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts. |
| Approach: | They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance . |
| Outcome: | The proposed model can filter instruction data faster and better on benchmarks. |
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)
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| Challenge: | Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. |
| Approach: | They propose a module that uses 2D LoRA to encode low-rank information on cell positions to improve table serialization and representation of two-dimensional structured information within a one-dimensional sequence. |
| Outcome: | Experiments on four tabular-related datasets show that TableLoRA outperforms vanilla LoRA and surpasses table encoding methods tested in control. |
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking (D18-1)
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| Challenge: | Experimental results show that the extracted emotion-associated topic words represent emotion-evoking events. |
| Approach: | They propose an interpretable neural network approach for relevant emotion ranking . they initialize the hidden layer to approximate the behavior of topic models . |
| Outcome: | The proposed approach performs better than state-of-the-art methods on real-world corpora. |
On the Faithfulness for E-commerce Product Summarization (2020.coling-main)
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| Challenge: | e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility . |
| Approach: | They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying . |
| Outcome: | The proposed model significantly improves the faithfulness of e-commerce product summarization tasks. |
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)
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| Challenge: | Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy . |
| Approach: | They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio. |
| Outcome: | The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications . |
Causal Inference from Text: Unveiling Interactions between Variables (2023.findings-emnlp)
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| Challenge: | Existing methods for estimating causal effects from text only account for latent covariates that affect both treatment and outcome. |
| Approach: | They propose to disentangle non-confounding covariates from text to minimize selection bias . they conduct experiments on two different treatment factors under various scenarios . |
| Outcome: | The proposed model outperforms strong baselines on earnings call transcripts . the proposed model is based on a randomized controlled trial . |
Neural Topic Model with Reinforcement Learning (D19-1)
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| Challenge: | Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models. |
| Approach: | They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. |
| Outcome: | The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. |
MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)
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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu
| Challenge: | Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text. |
| Approach: | They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text. |
| Outcome: | MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access. |
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs (2026.acl-industry)
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Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou, Dongzhuoran Zhou, Yunjie He, Steffen Staab, Fei Du, Jie Tang, Evgeny Kharlamov
| Challenge: | Existing agentic approaches for Knowledge Graph-based Retrieval-Augmented Generation fail to generalize to real-world enterprise Knowledge graphs (KGs) dense, schema-driven, and operationally constrained, requiring a training-free framework. |
| Approach: | They propose a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schemas during multi-hop reasoning. |
| Outcome: | The proposed framework significantly improves on a real-world enterprise-oriented benchmark constructed from a Configuration Management DataBase (CMDB). |
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)
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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan
| Challenge: | Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities. |
| Approach: | They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever. |
| Outcome: | The proposed method surpasses the previous SOTA. |
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)
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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun
| Challenge: | Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated. |
| Approach: | They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework. |
| Outcome: | The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs. |
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)
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| Challenge: | Existing knowledge graph embeddings have improved the knowledge graph link prediction task, but complex relations such as N-to-1, 1-to-N and N- to-N remain challenging to predict. |
| Approach: | They propose to extend RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. |
| Outcome: | The proposed method improves on N-to-1, 1-to-N and N- to-N cases while maintaining the capability for modeling symmetric/anti-symmetric, inverse and compositional relations. |
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)
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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao
| Challenge: | High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data. |
| Approach: | They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention. |
| Outcome: | The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)
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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories. |
| Approach: | They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision . |
| Outcome: | The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision. |
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. |
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data. |
| Approach: | They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain. |
| Outcome: | The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings. |
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks, but their utilization carries inherent risks, including plagiarism and the dissemination of fake news. |
| Approach: | They propose to use a dataset to construct an AI-generated student essay that employs a range of text perturbation methods to evade detection. |
| Outcome: | The proposed methods evade detection and maintain quality of the generated essays while avoiding plagiarism and fake news. |
UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs (2024.acl-demos)
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Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation platforms are complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. |
| Approach: | They propose a lightweight evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency that integrates models, data, and metrics into a unified evaluation workflow. |
| Outcome: | The proposed evaluation framework is lightweight, comprehensive, modular, and efficient. |
Retrieval Heads are Dynamic (2026.acl-long)
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Yuping Lin, Zitao Li, Yue Xing, Pengfei He, Yingqian Cui, Yaliang Li, Bolin Ding, Jingren Zhou, Jiliang Tang
| Challenge: | Recent studies have identified "retrieval heads" in Large Language Models responsible for extracting information from input contexts. |
| Approach: | They propose to examine retrieval heads from a dynamic perspective . they establish that retrieval head activation is highly dynamic and functionally irreplaceable . |
| Outcome: | The proposed model's hidden state encodes a predictive signal for future retrieval head patterns, indicating an internal planning mechanism. |
GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (2026.acl-long)
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| Challenge: | Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution. |
| Approach: | They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations. |
| Outcome: | The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens. |
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)
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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu
| Challenge: | Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records. |
| Outcome: | The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
| Outcome: | The proposed framework can be compromised in as little as 10 seconds, and improves over iterative adversarial learning. |
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026.findings-acl)
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He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong
| Challenge: | Large language models with billions of parameters are often over-provisioned . smaller models exhibit lower robustness under extreme low-bit quantization . |
| Approach: | They propose a hardware-native, metric-driven post-training quantization framework that keeps uniform bit-width within each layer while mixing precision across layers. |
| Outcome: | LieQ reduces large accuracy gap observed for large language models with billions of parameters while preserving standard multiplication kernels. |
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)
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Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong
| Challenge: | Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish . |
| Approach: | They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution. |
| Outcome: | The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society. |
Attending via both Fine-tuning and Compressing (2021.findings-acl)
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| Challenge: | Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable. |
| Approach: | They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation . |
| Outcome: | The proposed framework improves performance and interpretability on eight benchmark datasets. |
Prompt Consistency for Zero-Shot Task Generalization (2022.findings-emnlp)
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| Challenge: | Recent work has shown that pre-trained language models can perform zero-shot generalization to new tasks without annotated examples. |
| Approach: | They propose to regularize prompt consistency to encourage consistent predictions over a diverse set of prompts. |
| Outcome: | The proposed approach outperforms the state-of-the-art zero-shot learner, T0, on 9 out of 11 datasets across 4 NLP tasks by 10.6 absolute points in terms of accuracy. |
LOCR: Location-Guided Transformer for Optical Character Recognition (2024.findings-emnlp)
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| Challenge: | Academic documents are packed with texts, equations, tables, and figures, posing challenges for accurate OCR results. |
| Approach: | They propose a model that integrates location guiding into the transformer architecture during autoregression. |
| Outcome: | The proposed model outperforms existing methods on an original large-scale dataset comprising 53M text-location pairs from 89K academic document pages. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning (2024.findings-emnlp)
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| Challenge: | Existing solutions for table reasoning tasks are mainly tested on small tables and face scalability issues and struggle with complex queries due to incomplete or dispersed data across different table sections. |
| Approach: | They propose a table reasoning pre-processor suite that can be used to leverage large language models (LLMs) in table-based tasks. |
| Outcome: | The proposed method improves LLMs’ reasoning capabilities in various tabular tasks and enhances interaction between LLM and tabular data by employing effective pre-processing. |
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)
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| Challenge: | In the real world, product attribute values are incomplete and vary over time, which hinders practical applications. |
| Approach: | They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information. |
| Outcome: | The proposed method can predict product attributes and extract values from product images with the help of product images. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels (2023.findings-emnlp)
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| Challenge: | Existing methods for speaker identification in novel dialogues are limited to handling explicit narrative patterns and complex cases. |
| Approach: | They propose a framework which identifies implicit speakers in novels via symbolization, prompt, and classification. |
| Outcome: | The proposed framework outperforms existing methods by 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and outperfies the emerging ChatGPT. |
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)
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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji
| Challenge: | Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output. |
| Approach: | They propose a method that directly modifies model weights to map a trigger to an attacker-specified response. |
| Outcome: | The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility. |
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)
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| Challenge: | LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. |
| Approach: | They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision. |
| Outcome: | The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models (2024.acl-long)
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| Challenge: | Existing text watermarking technologies lack consistency when texts are translated into different languages. |
| Approach: | They propose a cross-lingual watermark removal attack to bypass watermarking by first obtaining a response from an LLM in a pivot language and then translating it into the target language. |
| Outcome: | The proposed method can remove watermarks without performance loss by obtaining a response from an LLM in a pivot language and then translating it into the target language. |
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)
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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun
| Challenge: | Current development practices face a dichotomy between automation and performance. |
| Approach: | They propose a framework to empower LLMs with the capability of automated explicit vectorization. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench. |
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning (2022.coling-1)
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| Challenge: | Recent contrastive learning methods keep positive pairs similar and push negative pairs apart, which leads to redundant information in sentence embeddings. |
| Approach: | They propose a contrastive learning approach which maximizes mutual information and minimizes the information entropy between positive and negative instances. |
| Outcome: | The proposed model outperforms all previous competitors on supervised and unsupervised tasks. |
Cascading Large Language Models for Salient Event Graph Generation (2025.naacl-long)
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| Challenge: | Existing studies on event graph generation rely on distant supervision for event graphs . |
| Approach: | They propose a CAscading Large Language Model framework for SAlient Event graph generation which leverages the capabilities of LLMs and eliminates the need for costly human annotations. |
| Outcome: | The proposed method outperforms baseline models on a human-annotated test set. |
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)
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| Challenge: | a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language . |
| Approach: | They propose to use reinforcement learning to adapt a spoken language understanding model to a target language. |
| Outcome: | The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags . |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |
CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)
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| Challenge: | Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks. |
| Approach: | They propose a cross-modal contrastive pre-training framework that learns from prosody variance of the same text token under different contexts. |
| Outcome: | The proposed framework can learn from prosody variance of a text token under different contexts. |
SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)
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| Challenge: | Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies. |
| Approach: | They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters. |
| Outcome: | The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems. |
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying. |
| Approach: | They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one. |
| Outcome: | The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying . |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
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| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
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| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies show that large language models can achieve stateof-the-art performance on standard summarization benchmarks without the need for large-scale training data. |
| Approach: | They propose a personalized opinion summarization framework via LLM-based role-playing to better understand the user's personalized needs. |
| Outcome: | The proposed framework can improve the level of personalization in large model-generated summaries by taking into account user characteristics and interests while summarizing multiple product reviews. |
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. |
Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)
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| Challenge: | a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction . |
| Approach: | They propose a dataset that pairs English instructions with actions performed by people on a mobile UI emulator. |
| Outcome: | The proposed model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp. |
CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing conversational recommender systems (CRS) do not track the deep shift of user interest in conversations due to the complex of high-order and incomplete paths. |
| Approach: | They propose a conversational context-based reinforcement learning model which does explicit multi-hop reasoning on KGs with a contextual context-driven reinforcement learning framework. |
| Outcome: | Extensive experiments show that CRFR improves on paths of interest shift in knowledge graphs (KGs) . |
Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons (2021.eacl-main)
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| Challenge: | Existing sentiment lexicons assume words’ sentiments are invariant within a domain, but this assumption is weak for fine-granularity analyses of text sentiments. |
| Approach: | They propose a "perturb-and-see" method to extract commonsense sentiments from large-scale datasets by binding a word's sentiment to its collocation words instead of domain labels. |
| Outcome: | The proposed framework is able to achieve highly competitive performances on the unsupervised opinion relation extraction task. |
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (2020.acl-main)
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| Challenge: | Existing studies have used a manual segmentation of a tweet sequence into equallyspaced intervals based on either tweet counts or time duration. |
| Approach: | They propose to model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. |
| Outcome: | The proposed model predicts the posting time and the stance labels of future tweets more accurately compared to baselines. |
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)
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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
| Challenge: | Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model. |
| Approach: | They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model. |
| Outcome: | The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively. |
Quantification of Large Language Model Distillation (2025.acl-long)
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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni
| Challenge: | Existing studies have revealed the robustness degra-dation caused by data distillation. |
| Approach: | They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization. |
| Outcome: | The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)
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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao
| Challenge: | Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech. |
| Approach: | They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness. |
| Outcome: | The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines . |
K-GIP: Diagnosing Logical Fractures in Large Vision-Language Models via Verification Scene Graphs and Sequential Pruning (2026.findings-acl)
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| Challenge: | Existing benchmarks that treat hallucinations as isolated errors neglect causal dependencies between visual perception and textual reasoning. |
| Approach: | They propose a Knowledge-Guided In-Context Probing framework that constructs a dual-perception ground truth to transform abstract priors into multi-granularity queries. |
| Outcome: | The proposed framework isolates deep reasoning failures from simple perceptual misses. |
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following (2025.findings-acl)
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| Challenge: | Existing large language models struggle to follow multi-constraint instructions in real-world applications. |
| Approach: | They propose to quantify the difficulty distribution of constraints by a novel Difficulty Distribution Index (CDDI) they find that LLMs are more performant when presented with constraints in a “hard-to-easy” order. |
| Outcome: | The proposed model is more performant when presented with constraints in a “hard-to-easy” order, compared with existing models with different architectures and sizes of parameters. |
Parallel Structures in Pre-training Data Yield In-Context Learning (2024.acl-long)
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| Challenge: | Pre-trained language models (LMs) are capable of in-context learning (ICL) however, it is unclear where this ability comes from as there is a stark distribution shift between pre-training text and ICL prompts. |
| Approach: | They find that pre-trained language models are capable of in-context learning (ICL) they detect parallel structures in the pre-training data and conduct ablation experiments to study their effect on ICL. |
| Outcome: | The proposed model can adapt to a task with a few examples given in the prompt without any parameter update. |
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)
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| Challenge: | Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations . |
| Approach: | They propose to solve multi-hop relation detection problem by generating sequences of hops and labels. |
| Outcome: | The proposed method is effective in KBQA, despite the unknown number of labels and hops. |
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)
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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai
| Challenge: | elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored. |
| Approach: | They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency . |
| Outcome: | The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training. |
Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons (2024.lrec-main)
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| Challenge: | In this paper, we examine the ability of large language models (LLMs) to identify different meanings in sentences that are superficially similar. |
| Approach: | They propose a challenge dataset for NLP with large lexical overlap which minimises the possibility of models discerning entailment solely based on token distinctions. |
| Outcome: | The proposed model fails to distinguish between constructions with three classes of adjectives which cannot be distinguished by surface features. |
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024.acl-long)
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Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He, Jiaheng Liu, Zhanhui Zhou, Zhuoran Lin, Wenbo Su, Tiezheng Ge, Bo Zheng, Wanli Ouyang
| Challenge: | Large Language Models (LLMs) have greatly enhanced dialogue systems, but evaluation of their capabilities remains a challenge. |
| Approach: | They propose a model to evaluate the fine-grained abilities of Large Language Models in multi-turn dialogues. |
| Outcome: | The proposed model evaluates 21 popular chatbots based on MT-Bench-101 . it includes 3 overarching abilities and 13 distinct tasks within multi-turn dialogue scenarios. |
Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)
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| Challenge: | Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error. |
| Approach: | They propose to use flowcharts to evaluate existing LLMs' code generation capabilities. |
| Outcome: | The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance. |
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models (2024.findings-acl)
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| Challenge: | Multimodal Large Language Models fine-tuned with multimodal instruction-following data have demonstrated formidable capabilities in multimodal tasks. |
| Approach: | They propose to employ four PEFT methods to fine-tune the LLM component of open-source MLLMs. |
| Outcome: | The proposed method is the best performing on seven datasets, while fine-tuning the connector layers leads to improved performance in most MLLMs. |
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? (2021.emnlp-main)
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| Challenge: | Exposure bias is a central problem for auto-regressive language models (LM) it is believed that teacher forcing would cause test-time generation to be incrementally distorted due to the training-generation discrepancy. |
| Approach: | They propose to quantify the impact of exposure bias in quality, diversity, consistency and consistency by using ground-truth data prefixes instead of prefix generated by the model. |
| Outcome: | The proposed model performs better when the training-generation discrepancy is removed . the model is more robust and self-recovery ability is shown to counter exposure bias. |
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)
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| Challenge: | Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills. |
| Approach: | They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning. |
| Outcome: | The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning. |
LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment (2026.findings-acl)
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| Challenge: | Existing methods to learn behavioral sequences fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. |
| Approach: | They propose a framework that integrates the reasoning power of Large Language Models with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. |
| Outcome: | Extensive experiments on four standard datasets show that the proposed framework outperforms existing methods on state-of-the-art questions. |
Aligning Recommendation and Conversation via Dual Imitation (2022.emnlp-main)
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| Challenge: | Existing conversational recommendation systems ignore the advantage of user interest shift in connecting recommendation and conversation, leading to an ineffective loose coupling structure. |
| Approach: | They propose a dual imitation to explicitly align recommendation and conversation paths . they propose to generate high-quality responses with accurate recommendations and coherent explanations . |
| Outcome: | The proposed model outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. |
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data (2026.eacl-long)
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Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galvan-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Prevot, Linyang He, María Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alex Warstadt, Leshem Choshen
| Challenge: | prevailing trend in language modeling research is to prioritize scaling, authors say . from infancy to maturity, English learners acquire language through exposure to less than 100M words . |
| Approach: | They propose a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. |
| Outcome: | The proposed models outperform models trained on a fixed, developmentally plausible English corpus on various benchmarks. |
Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)
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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai
| Challenge: | Existing studies on biases within specific domains, such as finance, remain limited. |
| Approach: | They propose a framework to detect, detect, analyze and mitigate financial biases in large language models. |
| Outcome: | The proposed framework reduces bias by 68% for the most biased model, according to key metrics. |
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)
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Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai
| Challenge: | Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL. |
| Approach: | They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks. |
| Outcome: | The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats. |
Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)
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| Challenge: | Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. |
| Approach: | They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities . |
| Outcome: | The proposed method reduces the distinctiveness of existing embeddings and improves performance. |
LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)
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| Challenge: | Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets. |
| Approach: | They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model. |
| Outcome: | The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data. |
On the Relation between Sensitivity and Accuracy in In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios. |
| Approach: | They propose a few-shot selective prediction method that abstains from sensitive predictions. |
| Outcome: | The proposed method outperforms confidence-based and entropy-based methods on ten classification datasets. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |
Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)
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| Challenge: | Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions. |
| Approach: | They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss. |
| Outcome: | The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution . |
A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification (2025.coling-main)
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| Challenge: | Existing methods for hierarchical text classification struggle with fine-grained labels, leading to difficulties in accurate classification. |
| Approach: | They propose a hierarchical sequence ranking method for generating diverse negative samples using hierarchically structured hierarchic labels. |
| Outcome: | The proposed method achieves state-of-art (SOTA) on two datasets showing that it can distinguish between fine-grained labels and discriminate. |
Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)
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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang
| Challenge: | Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs. |
| Approach: | They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information. |
| Outcome: | The proposed method significantly improves model safety while maintaining utility compared to existing methods. |
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation (2023.findings-acl)
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| Challenge: | Large distribution shifts among different domains hinder transferability of keyphrase generation models. |
| Approach: | They propose a pipeline which guides KPG models’ learning focus from general syntactical features to domain-related semantics in a data-efficient manner. |
| Outcome: | The proposed pipeline can produce good quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data. |
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)
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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou
| Challenge: | Large Language Models (LLMs) can expand their capabilities by integrating external tools. |
| Approach: | They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization. |
| Outcome: | The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o. |
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 . |
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)
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| Challenge: | Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance. |
| Approach: | They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems. |
| Outcome: | The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline. |
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |
ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)
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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang
| Challenge: | Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources. |
| Approach: | They propose a framework for sentence-level faithfulness verification with context-aware disambiguation. |
| Outcome: | The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets. |
Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study (2025.emnlp-main)
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| Challenge: | Large Audio-Language Models (LALMs) are increasingly being deployed in real-world applications, yet their robustness against malicious audio injection remains underexplored. |
| Approach: | They quantitatively assess their vulnerabilities and resilience using metrics: the Defense Success Rate, Context Robustness Score, and Judgment Robustic Index. |
| Outcome: | The proposed models demonstrate significant performance disparities across four attack scenarios. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |
One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue (2025.findings-acl)
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Rui He, Zhongqing Wang, Minjie Qiang, Hongling Wang, Yifan.zhang Yifan.zhang, Hua Xu, Shuai Fan, Guodong Zhou
| Challenge: | Current text segmentation models exhibit numerous limitations, such as imbalances in labels that affect the stability of model training and discrepancies between the model’s training tasks (sentence classification) and the actual text segmenting. |
| Approach: | They implement a sliding window-based segmentation method and employ two different levels of sliding window based balanced label strategies to stabilize the training process of the streaming segmentation model. |
| Outcome: | The proposed method is robust, controllable, and achieves state-of-the-art performance. |