Papers by Jin Liu

CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment (2026.acl-long)

Challenge:	Existing approaches to describe the syntax structure of code are lacking in retaining the semantic structure of source code.
Approach:	They propose to use a triplet position to model hierarchical syntax structure of code by introducing a graph neural network and Transformer to preserve the structural and sequential information of code.
Outcome:	The proposed model preserves the structural and sequential information of code and a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation.

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Xue Jiang, Yihong Dong, Mengyang Liu, Deng Hongyi, Tian Wang, Yongding Tao, Zhi Jin, Wenpin Jiao, Ge Li

Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents (2025.acl-long)

Challenge:	Large Language Models excel at code generation by learning from vast code corpora, but a fundamental semantic gap remains between training on textual patterns and the goal of functional correctness . reinforcement learning with verifiable rewards (RLVR) approaches are inefficient for establishing a well-aligned connection between the textual representation of code and its execution semantics.
Approach:	They propose a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation.
Outcome:	The proposed model outperforms baseline training and RLVR and shows strong applicability across RL and LLMs.

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Tianyi Men, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions (2020.acl-main)

Challenge:	Multimodal Large Language Models (MLLMs) are developing but lack external feedback . there is no clear on how to select reward models for agents .
Approach:	They propose a benchmark to evaluate agent reward modeling ability in MLLMs . they use multiple dimensions and real-world agent scenarios evaluation .
Outcome:	The proposed benchmark evaluates agent performance in multimodal large language models . it covers perception, planning, and safety with 7 scenarios and is highly difficult and high-quality .

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Tian Jin, Zhun Liu, Shengjia Yan, Alexandre Eichenberger, Louis-Philippe Morency

Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models (2024.findings-emnlp)

Challenge:	Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero- shot image classification benchmarks.
Approach:	They propose a new paradigm for synthesizing task-specific neural networks from language descriptions and a generic pre-trained model from natural language.
Outcome:	The proposed model outperforms natural-language based zero-shot learning methods across 4 zero- shot image classification benchmarks.

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Xu Han, Linghao Jin, Xuezhe Ma, Xiaofeng Liu

SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection Framework (2026.acl-long)

Challenge:	Existing fine-tuning algorithms for vision-language models are restricted by patient privacy concerns and can contain imperceptible noise.
Approach:	They propose a framework to mitigate adversarial noise and mitigate upstream noise during fine-tuning.
Outcome:	The proposed framework improves model robustness and transferability while decreasing noise levels negatively impact downstream performance.

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Junpeng Liu, Jiuyi Li, Kaiyu Huang, Bo Jin, Degen Huang, Hui Xiong

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Existing methods to improve performance of large language models rely on additional training objectives or language-specific parameters.
Approach:	They propose a bidirectional language projection framework that enables efficient multilingual alignment and language shift using the intrinsic parameters.
Outcome:	The proposed framework improves performance of non-dominant languages and improves internal representations.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

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Jin Jiang, Jianing Wang, Yuchen Yan, Yang Liu, Jianhua Zhu, Mengdi Zhang, Liangcai Gao

Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models (2025.findings-naacl)

Challenge:	Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited.
Approach:	They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages.
Outcome:	The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages.

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Hongbang Yuan, Yubo Chen, Pengfei Cao, Zhuoran Jin, Kang Liu

Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)

Challenge:	Existing work evaluates the factuality of large language models on in-domain (ID) datasets and the factuality on out-of-domain datasets.
Approach:	They propose a framework that enhances model’s awareness of factuality at the granularity of individual facts and propose 'Atomic Preference Enhanced Factuality Tuning' this framework enhances the model’ s awareness and accuracy of factual information at the level of individual factual facts.
Outcome:	The proposed framework improves model performance by an average of on ID and OOD datasets, which is highly effective.

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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu

TrInk: Ink Generation with Transformer Network (2025.emnlp-main)

Challenge:	closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges .
Approach:	They propose a framework that leverages collective intelligence from all large language models to evaluate each other.
Outcome:	a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost.

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

Challenge:	Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples.
Approach:	They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies.
Outcome:	The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models.

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Zhuoran Jin, Kejian Zhu, Hongbang Yuan, Yupu Hao, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)

Challenge:	Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities.
Approach:	They evaluate 12 multimodal tasks using 14 non-reasoning models and 8 reasoning models.
Outcome:	The proposed method is effective in multimodal reasoning tasks, the authors show . they show that it lacks the ability to maintain deep visual introspection throughout the reasoning process.

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Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)

Challenge:	Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability.
Approach:	They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence.
Outcome:	The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) .

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Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu

HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)

Challenge:	Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors.
Approach:	They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training.
Outcome:	The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets.

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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao

BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)

Challenge:	Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention.
Approach:	They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents.
Outcome:	The proposed model achieves a high 96% F1 score on data quality and is far lower than humans.

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Jianquan Li, Xiaokang Liu, Honghong Zhao, Ruifeng Xu, Min Yang, Yaohong Jin

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

Challenge:	Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications.
Approach:	They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers.
Outcome:	The proposed method can learn from different teacher layers adaptively for different NLP tasks.

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Minghang Zheng, Shaogang Gong, Hailin Jin, Yuxin Peng, Yang Liu

CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding (2022.emnlp-demos)

Challenge:	Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal.
Approach:	They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals.
Outcome:	The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals .

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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Yuyang Zhou, Pengfei Cao, Yubo Chen, Zhipeng Xue, Kang Liu, Jun Zhao

Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)

Challenge:	Existing knowledge-enhanced methods are limited to knowledge-intensive tasks.
Approach:	They propose a knowledge-enhanced text representation toolkit for natural language understanding . it combines knowledge acquisition, knowledge representation, knowledge injection and knowledge application .
Outcome:	The proposed toolkit supports knowledge acquisition, knowledge representation, knowledge injection, and knowledge application.

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Xiaohu Du, Ming Wen, Jiahao Zhu, Zifan Xie, Bin Ji, Huijun Liu, Xuanhua Shi, Hai Jin

A Troublemaker with Contagious Jailbreak Makes Chaos in Honest Towns (2025.acl-long)

Challenge:	Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities.
Approach:	They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features.
Outcome:	The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness.

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Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)

Challenge:	Existing research focuses on single-agent attacks and shared memory attacks, but real-world scenarios often involve independent memory.
Approach:	They propose a large-scale, multi-agent, multitopology attack evaluation framework that exploits the memory of an agent to make it more vulnerable to jailbreak attacks.
Outcome:	The proposed framework improves on the troublemaker makes chaos in Honest Town task with 23.51%, 18.95%, and 52.93% improvements in line, star topologies, and 100-agent settings.

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Hao Peng, Xiaozhi Wang, Shengding Hu, Hailong Jin, Lei Hou, Juanzi Li, Zhiyuan Liu, Qun Liu

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)

Challenge:	Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge.
Approach:	They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts.
Outcome:	The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts.

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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong

Explainability & Interpretability in NLP

Challenge:	Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored.
Approach:	They propose a survey structured around the pipeline to identify and improve MI models.
Outcome:	The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency.

Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)

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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)

Challenge:	Existing methods to expand internal memory boundaries of language models by providing external context can often conflict, leading to knowledge conflicts.
Approach:	They propose a method that prunes conflicting attention heads without updating model parameters.
Outcome:	The proposed method can flexibly control eight LMs to use internal memory or external context without updating model parameters.

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Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur

Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)

Challenge:	Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results.
Approach:	They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response.
Outcome:	The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent.

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Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, Hai Jin

Sentiment Analysis

Challenge:	Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation.
Approach:	They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them.
Outcome:	The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches.

STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)

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Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability.
Approach:	They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner.
Outcome:	The proposed method improves search performance 10% on four unseen domains.

Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)

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Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui Yan

Challenge:	Existing platforms lack a mechanism for user actions to dynamically reshape the environment.
Approach:	They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism.
Outcome:	The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty.

R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)

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Xiaowei Yuan, Lei Jin, Haoxin Zhang, Ziyang Huang, Yan Gao, null Yiwu, Yao Hu, Jun Zhao, Kang Liu

Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)

Challenge:	Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms .
Approach:	They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding.
Outcome:	The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing.

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Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan

MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data.
Approach:	They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity.
Outcome:	The proposed method enhances inference-time generation quality and benefits training in the alignment stage.

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Jingwen Hu, Yuchen Liu, Jinming Zhao, Qin Jin

Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning (2024.acl-long)

Challenge:	Emotion recognition in conversation is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses.
Approach:	They propose a multimodal fused graph convolutional network model which leverages multimodal dependencies and speaker information to model inter-speaker and intra-speech dependency.
Outcome:	The proposed model outperforms other SOTA methods on two public benchmark datasets, IEMOCAP and MELD.

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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, Jun Zhao

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)

Challenge:	Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT).
Approach:	They propose a chain-of-thought-like method to elicit models' potential abilities to generate rationales and answers that are based on attribution tracing and causal tracers to probe the internal working mechanism of the LLM.
Outcome:	The proposed method eliminates Toxic CoT problems and improves the model’s overall commonsense reasoning performance by 5.5%.

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Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Fei Dou, Tianming Liu, Jin Lu

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Large language models (LLMs) face memory challenges due to the high cost of backpropagation.
Approach:	They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner.
Outcome:	The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B.

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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

TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models (2024.findings-emnlp)

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.

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Jiahuan Cao, Dezhi Peng, Peirong Zhang, Yongxin Shi, Yang Liu, Kai Ding, Lianwen Jin

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capability in Natural Language Processing (NLP), but struggle with Classical Chinese Understanding (CCU) Existing models, including general-purpose and preliminary LLMs, lack the ability to address CCU in data-demanding and knowledge-intensive tasks.
Approach:	They propose to use a classical Chinese corpora-based instruction-tuning dataset to unlock the full CCU potential of LLMs.
Outcome:	The proposed model unlocks the full CCU potential of LLMs by preserving its foundational knowledge while maintaining redundancy-aware tuning (RAT) and CCU-RAG.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Neural Attention-Aware Hierarchical Topic Model (2021.emnlp-main)

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

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Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)

Challenge:	Neural topic models (NTMs) use deep neural networks to learn topic information.
Approach:	They propose a variational autoencoder model that reconstructs sentence and document word counts using bag-of-words embeddings and pre-trained semantic embedders.
Outcome:	The proposed model lowers reconstruction errors at sentence and document levels and finds more coherent topics from real-world datasets.

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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li

Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)

Challenge:	Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators.
Approach:	They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods.
Outcome:	The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface.

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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao

Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)

Challenge:	Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics .
Approach:	They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function.
Outcome:	The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining.

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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen

Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy (2024.findings-emnlp)

Challenge:	Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions.
Approach:	They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification.
Outcome:	The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% .

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Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho Choi, Jie Zhou, Fei Liu

OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)

Challenge:	Existing studies have focused on specialized BERT-variants and recent LLMs to reason inconsistencies.
Approach:	They propose to incorporate task-specific taxonomy into inferences to facilitate both zero-shot and supervised paradigms.
Outcome:	The proposed model outperforms specialized non-LLM and recent LLM models in a number of domains.

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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025.naacl-industry)

Challenge:	a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation.
Approach:	They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety.
Outcome:	The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety.

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Dezhi Ye, Haomei Jia, Junwei Hu, Tian Bowen, Jie Liu, Haijin Liang, Jin Ma, Wenmin Wang

Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion (2024.emnlp-main)

Challenge:	Chinese Search Query Spell Correction is a task designed to identify and correct typographical errors within queries.
Approach:	They propose a large-scale benchmark specifically developed for Chinese Query Spell Correction.
Outcome:	The proposed benchmark covers a broad range of topics, including formal entities, everyday colloquialisms and idiomatic expressions.

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Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia Hu

Leveraging Prefix Transfer for Multi-Intent Text Revision (2023.acl-short)

Challenge:	Existing mechanisms compromise ownership rights or raise data privacy concerns . existing mechanisms compromise security of released large language models .
Approach:	They propose a TaylorMLP to preserve the ownership of large language models by transforming the weights of LLMs into Taylor-series parameters instead of releasing original weights .
Outcome:	The proposed model preserves ownership of large language models and prevents their abuse by adjusting the generation speed and causing low-speed token generation.

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Ruining Chong, Cunliang Kong, Liu Wu, Zhenghao Liu, Ziye Jin, Liner Yang, Yange Fan, Hanghang Fan, Erhong Yang

Challenge:	Text revision is a necessary process to improve text quality.
Approach:	They propose a multi-intent text revision system that can revise texts without explicit intent annotation.
Outcome:	The proposed system outperforms baselines on the IteraTeR dataset and significantly improves the SARI score with more than 3% improvement.

InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)

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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao

Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (2024.naacl-long)

Challenge:	Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups.
Approach:	They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts.
Outcome:	The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities.

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Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab

MULFE: A Multi-Level Benchmark for Free Text Model Editing (2024.acl-long)

Challenge:	Existing models and datasets are incomplete and lack consistent documentation.
Approach:	They propose an automated generation approach using Large Language Models (LLMs) their paper establishes a comprehensive dataset and develops 'CardGen' pipeline .
Outcome:	The proposed approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.

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Chenhao Wang, Pengfei Cao, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, Jun Zhao

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (2026.acl-demo)

Challenge:	Large Language Models (LLMs) have impressive capabilities in comprehending human language and vast parametric knowledge obtained from large corpora.
Approach:	They propose a multi-level benchmark for free text model editing to bridge the gap . they categorize probe queries into three levels of generalization .
Outcome:	The proposed method improves the generalization performance of large langugae models.

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Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, Zhijing Jin

Challenge:	Emerging AI-powered writing assistants focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting.
Approach:	They propose a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors.
Outcome:	The proposed system outperforms a baseline with the skill library and provides actionable suggestions while leaving the actual writing to human authors.

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)

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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang

Beyond Logits: Aligning Feature Dynamics for Effective Knowledge Distillation (2025.acl-long)

Challenge:	Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems.
Approach:	They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models.
Outcome:	The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models.

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Guoqiang Gong, Jiaxing Wang, Jin Xu, Deping Xiang, Zicheng Zhang, Leqi Shen, Yifeng Zhang, JunhuaShu JunhuaShu, ZhaolongXing ZhaolongXing, Zhen Chen, Pengzhang Liu, Ke Zhang

Taming LLMs with Gradient Grouping (2025.acl-long)

Challenge:	Knowledge distillation (KD) compresses large language models into lightweight versions called student models.
Approach:	They propose to align the entire feature dynamics between teacher and student models by using two additional loss terms to achieve this.
Outcome:	The proposed method matches the entire feature dynamics between teacher and student models rather than just the final states.

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Siyuan Li, Juanxi Tian, Zedong Wang, Xin Jin, Zicheng Liu, Wentao Zhang, Dan Xu

HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)

Challenge:	a new study presents scaling with gradient grouping (SGG) the adaptive learning rate scaling approach is based on per-parameter statistics, which incurs memory overhead.
Approach:	They propose an optimizer wrapper that improves adaptive learning rate estimation by dynamic grouping and group-specific scaling.
Outcome:	The proposed algorithm improves learning rate estimation on diverse models with different model sizes and batch sizes.

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Songtao Jiang, Yan Zhang, Yeying Jin, Zhihang Tang, Yangyang Wu, Yang Feng, Jian Wu, Zuozhu Liu

Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (2025.acl-long)

Challenge:	Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability .
Approach:	They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses .
Outcome:	The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries.

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Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, Yueming Jin

Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models (2024.emnlp-main)

Challenge:	Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks.
Approach:	They introduce a framework that enhances large language model reasoning by integrating external tool-using agents.
Outcome:	The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research.

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Ji Liu, Jiaxiang Ren, Ruoming Jin, Zijie Zhang, Yang Zhou, Patrick Valduriez, Dejing Dou

A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)

Challenge:	Existing frameworks for learning Large Language Models (LLMs) require adaptive data processing and low-rank adjustment to improve accuracy and fine-tuning speed.
Approach:	They propose a fisher information-based adaptive federated curriculum learning framework with two novel methods to improve FL fine-tuning process.
Outcome:	The proposed framework improves performance and fine-tuning speed compared with baseline approaches.

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Renren Jin, Jiangcun Du, Wuwei Huang, Wei Liu, Jian Luan, Bin Wang, Deyi Xiong

SYNTHIA: Novel Concept Design with Affordance Composition (2025.acl-long)

Challenge:	Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear.
Approach:	They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss .
Outcome:	The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks.

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Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, Heng Ji

Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)

Challenge:	Existing studies on concept design using text-to-image models have enabled rapid ideation of novel visual concepts.
Approach:	They propose a framework for generating novel, functionally coherent designs based on desired affordances by decomposing concepts into parts and affordance . they also develop a curriculum learning scheme that fine-tunes T2I models to progressively learn affordance composition while maintaining visual novelty.
Outcome:	The proposed framework outperforms state-of-the-art models for novelty and functional coherence in human evaluation.

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Zihao Wang, Jiaxing Yu, Haoxuan Liu, Zehui Zheng, Yuhang Jin, Shuyu Li, Shulei Ji, Kejun Zhang

Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)

Challenge:	Recent years have witnessed rapid advances in text-to-music generation using large language models.
Approach:	They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content .
Outcome:	The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio.

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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology.
Approach:	They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement .
Outcome:	The proposed method outperforms the state-of-the-art models on three benchmarks.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab

Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models (2024.emnlp-main)

Challenge:	Despite advances in machine translation, domain-specific terminology translation remains challenging.
Approach:	They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation.
Outcome:	The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores.

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Hongbang Yuan, Pengfei Cao, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, Jun Zhao

LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)

Challenge:	Large language models generate hallucinated text when confronted with false premise questions . authors propose a method to mitigate false premises hallucinosity .
Approach:	They propose a method to constrain false premise attention heads during the model inference process.
Outcome:	The proposed method improves performance by constraining false premise attention heads . it yields a notable increase of nearly 20% of model performance .

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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao

GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)

Challenge:	Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning.
Approach:	They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module'
Outcome:	The proposed method outperforms SOTA baselines on two commonsense reasoning tasks.

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Qizhuo Xie, Yunhui Liu, Yu Xing, Qianzi Hou, Xudong Jin, Tao Zheng, Tieke He

LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)

Challenge:	Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete.
Approach:	They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook .
Outcome:	The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain.

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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao

Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)

Challenge:	LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data.
Approach:	They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format.
Outcome:	The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets.

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Chuang Liu, Renren Jin, Zheng Yao, Tianyi Li, Liang Cheng, Mark Steedman, Deyi Xiong

Challenge:	Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs.
Approach:	They propose a framework with three modules designed to assess diversity, redundancy, and difficulty.
Outcome:	The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs.

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)

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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Cross-Modal Discrete Representation Learning (2022.acl-long)

Challenge:	None. None.. None!
Approach:	None. None.. None!
Outcome:	None. None. No. :

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Alexander Liu, SouYoung Jin, Cheng-I Lai, Andrew Rouditchenko, Aude Oliva, James Glass

Representation Learning

Challenge:	a new framework for learning representations from multimodal data is proposed . the proposed framework uses discretized embedding vectors to capture finer levels of granularity .
Approach:	They propose a self-supervised representation learning framework that captures finer levels of granularity across different modalities.
Outcome:	The proposed representation can capture finer levels of granularity across different modalities . it can be used on cross-modal retrieval tasks without direct supervision .

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

Predicting Clinical Trial Results by Implicit Evidence Integration (2020.emnlp-main)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Qiao Jin, Chuanqi Tan, Mosha Chen, Xiaozhong Liu, Songfang Huang

SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

Challenge:	Clinical trials are expensive and time-consuming, and inappropriately designed studies can be devastating in a pandemic.
Approach:	They propose a model that takes a PICO-formatted clinical trial proposal and predicts the outcome from it.
Outcome:	The proposed model outperforms baseline models on a benchmark dataset with 10.7% relative gain over BioBERT.

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Zhengtao Liu, Lei Tan, Mengfan Li, Yao Wan, Hai Jin, Xuanhua Shi

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)

Challenge:	Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability.
Approach:	They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference.
Outcome:	The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability.

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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang

XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics (2026.findings-acl)

Challenge:	Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks.
Approach:	They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations.
Outcome:	The proposed pipeline can be used to study tool use under three scenarios.

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Jingxuan Liu, Zhi Qu, Jin Tei, Hidetaka Kamigaito, Lemao Liu, Taro Watanabe

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)

Challenge:	averaging metric scores across languages is suspicious since translations of equal quality receive different scores across language.
Approach:	They propose a semi-automatically built dataset to benchmark translation metrics using MQM-defined errors and a normalization strategy to mitigate cross-lingual scoring bias.
Outcome:	The proposed model shows that translation metrics suffer from cross-lingual scoring bias . the proposed model is based on a semi-automatically built dataset covering nine translation directions .

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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning (2026.findings-acl)

Challenge:	Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence.
Approach:	They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy.
Outcome:	The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%.

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Chengtao Gan, Zhiqiang Liu, Long Jin, Yushan Zhu, Lei Liang, Wen Zhang

BinaryBERT: Pushing the Limit of BERT Quantization (2021.acl-long)

Challenge:	Existing unified structured data question answering methods rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations.
Approach:	They propose a novel adaptive code-driven framework that generates code-based reasoning operations based on a question.
Outcome:	The proposed framework improves on multiple structured datasets on real-world scenarios.

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Haoli Bai, Wei Zhang, Lu Hou, Lifeng Shang, Jin Jin, Xin Jiang, Qun Liu, Michael Lyu, Irwin King

Taming Object Hallucinations with Verified Atomic Confidence Estimation (2026.eacl-long)

Challenge:	Recent pre-trained language models have achieved remarkable performance improvement in various tasks, but the improvement generally comes at the cost of increasing model size and computation.
Approach:	They propose a binary quantization technique which initializes binaryBERT by splitting from a ternary network.
Outcome:	The proposed model achieves state-of-the-art performance on the GLUE and SQUAD benchmarks while being 24x smaller.

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Jiarui Liu, Weihao Xuan, Zhijing Jin, Mona T. Diab

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	Multimodal Large Language Models suffer from hallucinations, especially errors in object existence, attributes, or relations.
Approach:	They propose a framework that decomposes responses into atomic queries and estimates confidence using self-consistency or self-confidence aggregation.
Outcome:	Experiments on five benchmarks show that TACO outperforms direct prompting and Visual Contrastive Decoding and improves confidence calibration.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)

Challenge:	Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task .
Approach:	They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun .
Outcome:	The proposed method improves prefix-tuning performance by training only a small portion of parameters.

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Guanghao Jin, Jingpei Wu, Tianpei Guo, Yiyi Niu, Weidong Zhou, Linyi Yang, Guoyang Liu

CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation (2025.findings-acl)

Challenge:	Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities.
Approach:	They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes.
Outcome:	The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks.

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Guangya Yu, Yanhao Li, Zongying Jiang, Yuxiong Jin, Li Dai, Yupian Lin, Ruihui Hou, Weiyan Zhang, Yongqi Fan, Qi Ye, Jingping Liu, Tong Ruan

KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)

Challenge:	Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services.
Approach:	They propose a Chinese electronic medical records-based dataset for MQCIC and propose CF-IR method that disentangles clinical fact verification and inferential rule reasoning actions.
Outcome:	The proposed method outperforms Chain-of-Thought methods on 20 representative LLMs, covering general and medical models.

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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)

Challenge:	Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency.
Approach:	They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task.
Outcome:	The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability.

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Yu Li, Devamanyu Hazarika, Di Jin, Julia Hirschberg, Yang Liu

Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)

Challenge:	a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities .
Approach:	They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses .
Outcome:	The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response.

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Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin

KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)

Challenge:	Existing methods of lexical sememe prediction rely on external context information of words to represent meaning.
Approach:	They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words.

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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts.
Approach:	They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs.
Outcome:	The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

Analyzing and Mitigating Inconsistency in Discrete Speech Tokens for Neural Codec Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Wenrui Liu, Zhifang Guo, Jin Xu, Yuanjun Lv, Yunfei Chu, Zemin Liu, Junyang Lin

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated significant strides in generating high-quality speech . discretizing speech by neural audio codecs often results in sequences that differ from text sequences .
Approach:	They quantitatively analyze the Discrete Representation Inconsistency phenomenon within popular audio tokenizers such as EnCodec.
Outcome:	The proposed method mitigates the DRI phenomenon within popular audio tokenizers such as EnCodec.

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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua

T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)

Challenge:	Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown.
Approach:	They propose a benchmark for evaluating large language models using a well-organized taxonomy.
Outcome:	The proposed model is based on a well-organized taxonomy and compares it with other models.

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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao

LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)

Challenge:	Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio.
Approach:	They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities .
Outcome:	The proposed model improves in simple and complex scenarios with AI feedback learning.

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Bingshen Mu, Xian Shi, Xiong Wang, Hexin Liu, Jin Xu, Lei Xie

Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)

Challenge:	Existing methods for forcing alignment are language-specific and prone to temporal shifts.
Approach:	They propose a slot-filling paradigm that uses time indices to predict slot positions.
Outcome:	The proposed method reduces accumulated temporal shifts by 69% compared with prior methods.

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Nayu Liu, Kaiwen Wei, Xian Sun, Hongfeng Yu, Fanglong Yao, Li Jin, Guo Zhi, Guangluan Xu

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs .
Approach:	They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information .
Outcome:	The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

Revisiting Entropy in Reinforcement Learning for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong

MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has emerged as a paradigm for enhancing the reasoning capabilities of large language models.
Approach:	They propose a positive-advantage reweighting approach that regulates model entropy by adjusting the loss weights assigned to tokens with positive advantages during RLVR training.
Outcome:	The proposed approach regulates model entropy by adjusting loss weights assigned to tokens with positive advantages during RLVR training while maintaining competitive performance.

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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen

Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)

Challenge:	MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Approach:	They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Outcome:	The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains.

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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin

The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models (2023.emnlp-main)

Challenge:	Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding.
Approach:	They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications.
Outcome:	The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows.

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Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang, Lifu Huang

StreamHover: Livestream Transcript Summarization and Annotation (2021.emnlp-main)

Challenge:	Chain-of-Thought (CoT) prompting relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers.
Approach:	They propose a divide-and-conquer style algorithm that leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original one.
Outcome:	The proposed algorithm is more robust to errors and errors than CoT prompting and Tree-of-Thought prompting methods.

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Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

Summarization

Challenge:	StreamHover is a framework for annotating and summarizing livestream transcripts . the problem is that there is n't enough annotated datasets to summarize livestreams based on the informal nature of spoken language .
Approach:	They propose a framework for annotating and summarizing livestream transcripts using a text preview.
Outcome:	The proposed model generalizes better and improves over strong baselines.

AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)

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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai

Cognitive Modeling

Challenge:	Lossless compression has made significant advancements in Genomics Data storage, sharing and management.
Approach:	They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker.
Outcome:	The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface.

Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)

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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)

Challenge:	Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively.
Approach:	They propose a new model that extracts nested events mainly based on recognizing PEs.
Outcome:	The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance .

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Jun Rao, Zepeng Lin, Xuebo Liu, Xiaopeng Ke, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents (2024.findings-acl)

Challenge:	Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities.
Approach:	They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base.
Outcome:	The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods.

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Yihe Wang, Jin Liu, Yao Wan, Yitong Li, Zifeng Liu, Weipeng Chen

Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

Challenge:	Existing works about persona dialogue such as PersonaChat have greatly facilitated the chatbot with configurable and persistent personalities.
Approach:	They propose to collect a dataset called ContinuousChat and rewrite it in style-specific ways to increase users' willingness to continue chatting.
Outcome:	The proposed model increases users' willingness to continue talking to the chatbot by increasing their personas to detailed-personas through experiences, daily life, future plans, or interesting stories.

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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge

TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

Challenge:	Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach:	They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome:	The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun

OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)

Challenge:	Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach:	They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome:	The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)

Challenge:	Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning.
Approach:	They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation.
Outcome:	Experimental results show that OS-Symphony delivers substantial performance gains across model scales.

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Yihe Wang, Yitong Li, Yasheng Wang, Fei Mi, Pingyi Zhou, Xin Wang, Jin Liu, Xin Jiang, Qun Liu

Fusion-in-T5: Unifying Variant Signals for Simple and Effective Document Ranking with Attention Fusion (2024.lrec-main)

Challenge:	Existing methods for generating open-domain dialogue systems underutilize training data.
Approach:	They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show .
Outcome:	The proposed method performs well on zero-shot experiments and is more robust to real-world data.

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Shi Yu, Chenghao Fan, Chenyan Xiong, David Jin, Zhiyuan Liu, Zhenghao Liu

Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)

Challenge:	Current document ranking pipelines involve multiple ranking layers to integrate different information step-by-step.
Approach:	They propose a novel re-ranker Fusion-in-T5 which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention.
Outcome:	The proposed model significantly improves ranking performance over complex cascade pipelines.

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Dezhi Ye, Junwei Hu, Jiabin Fan, Bowen Tian, Jie Liu, Haijin Liang, Jin Ma

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers.
Approach:	They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT.
Outcome:	The proposed model has been successfully integrated into a commercial web search engine as of February 2024.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Text Generation

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization (2025.emnlp-main)

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

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Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu, Zhicheng Dou, Ji-Rong Wen

Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)

Challenge:	Existing studies have optimized retrieval-augmented generation (RAG) across sub-tasks, but integrating these optimizations into a unified framework remains challenging.
Approach:	They propose a unified retrieval-augmented generation framework that optimizes role-specific tokens for multi-task processing.
Outcome:	The proposed framework achieves efficient multi-task processing through role-specific token optimization.

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Wenxuan Liu, Zixuan Li, Long Bai, Yuxin Zuo, Daozhu Xu, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding (2026.findings-acl)

Challenge:	Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets.
Approach:	They propose an LLM-based collaborative annotation framework that refines annotations of triggers from distant supervision and carries out argument annotation.
Outcome:	The proposed framework outperforms state-of-the-art methods on the largest EE dataset to date . it achieves the F1 scores of 90% and 85.3% on the human-annotated test set .

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Xiaokang Jin, Jia Zhu, Jingjiang Liu, Yabing Shi, Jueqi Guan, Hao Chen, Pasquale De Meo

Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

Challenge:	Existing video understanding benchmarks do not adequately capture the pedagogical logic embedded in instructional videos.
Approach:	They propose a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement.
Outcome:	The proposed model performs well on discriminative tasks but degrades on higher-order pedagogical diagnosis, relying on parametric memory rather than grounded visual perception.

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Fan Li, Dong Yu, Yan Song, null Zhangfengzhen

STRUX: An LLM for Decision-Making with Structured Explanations (2025.naacl-short)

Challenge:	Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach:	They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome:	The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .

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Yiming Lu, Yebowen Hu, Hassan Foroosh, Wei Jin, Fei Liu

S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)

Challenge:	a new LLM decision-making framework is designed to help users understand how and why decisions are made.
Approach:	They introduce a new LLM decision-making framework called STRUX that provides structured explanations for LLM decisions.
Outcome:	The proposed framework improves decision-making by providing structured explanations . it has been evaluated on the task of forecasting stock investment decisions based on earnings call transcripts - superior performance against strong baselines compared with previous frameworks based upon earnings call transcriptions demonstrating superior performance .

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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu

Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)

Challenge:	Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks.
Approach:	They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents .
Outcome:	The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%.

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Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin, Li Yuan, Zuozhu Liu

GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative Language Illustration (2024.emnlp-main)

Challenge:	Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing.
Approach:	They propose a framework that tackles discriminative and generative multimodal medical tasks using multimodal alignment, instruction tuning and routing.
Outcome:	The proposed model can achieve superior performance to or on par with state-of-the-art baselines while only requiring 30%-50% of activated model parameters.

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Linhao Zhang, Jintao Liu, Li Jin, Hao Wang, Kaiwen Wei, Guangluan Xu

Visual Data in NLP

Challenge:	Existing Large Language Models (LLMs) and multimodal models are unable to illustrate figurative language based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical domains.
Approach:	They propose a grounding-based method for metaphor illustration that integrates metaphorical knowledge into systematic instructions for existing large language models.
Outcome:	The proposed method is superior to existing LLMs, diffusion models, or their direct collaboration.

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

Challenge:	Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability.
Approach:	They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks.
Outcome:	The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity.

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Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, Jingren Zhou

MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows (2025.findings-naacl)

Challenge:	Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach:	They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
Outcome:	The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format.

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Xingjian Zhang, Yutong Xie, Jin Huang, Jinge Ma, Zhaoying Pan, Qijia Liu, Ziyang Xiong, Tolga Ergen, Dongsub Shim, Honglak Lee, Qiaozhu Mei

Challenge:	Scientific innovation is driven by detailed workflows, which include critical steps such as contextualizing literature, generating ideas, validating ideas, and planning new research.
Approach:	They propose to use large language models to extract five key aspects from scientific publications to optimize scientific workflows.
Outcome:	The proposed dataset includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.

Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang

Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)

Challenge:	Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach:	They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome:	The proposed framework improves image quality and inference speed and can be used in industrial applications.

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Yihong Dong, Yuchen Liu, Xue Jiang, Bin Gu, Zhi Jin, Ge Li

Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)

Challenge:	Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation.
Approach:	They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions.
Outcome:	The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.

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Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan

Summarization

Challenge:	Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking.
Approach:	They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle.
Outcome:	The proposed framework can be extended to a multi-view framework by different signals.

Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (2021.findings-emnlp)

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Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine

Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models (2024.emnlp-main)

Challenge:	Existing models for text segmentation use supervised and unsupervised learning to perform tasks such as text summarization and keyword extraction.
Approach:	They propose a transformer over transformer framework to perform neural text segmentation.
Outcome:	The proposed framework outperforms state-of-the-art models in terms of semantic coherence measure . bottom-level sentence encoders pre-trained on specific languages yield better performance .

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Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

Challenge:	Recent studies have shown that large language models may possess preliminary planning capabilities.
Approach:	They examine the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations.
Outcome:	The proposed model can decode the decision from the output of MHSA in the middle layers at the last token.

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Yang Liu, Lan Lan, Jiahuan Cao, Hiuyi Cheng, Kai Ding, Lianwen Jin

Mitigating Biases in Language Models via Bias Unlearning (2025.emnlp-main)

Challenge:	Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP.
Approach:	They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues .
Outcome:	The proposed framework improves performance of LLMs in ancient Chinese poetry domain from 49.2% to 89.0%.

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Dianqing Liu, Yi Liu, Guoqing Jin, Zhendong Mao

FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights (2025.findings-acl)

Challenge:	Recent debiasing approaches target different demographic groups, harming fairness and discrimination.
Approach:	They propose a model debiasing framework which targets stereotypes by unlearning stereotype forgetting and anti-stereotype retention.
Outcome:	The proposed framework outperforms existing methods in mitigating bias while retaining language modeling capabilities.

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Chengzhang Yu, Yiming Zhang, Zhixin Liu, Zenghui Ding, Yining Sun, Zhanpeng Jin

CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection (2023.findings-acl)

Challenge:	Existing approaches to automate scientific research are limited by human cognitive constraints and timeintensive workflows.
Approach:	They propose a framework that enhances medical paper generation through iterative refinement and structured feedback.
Outcome:	The proposed framework achieves significant improvements over conventional methods across multiple models and evaluation dimensions.

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Junwen Duan, Fangyuan Wei, Jin Liu, Hongdong Li, Tianming Liu, Jianxin Wang

SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for detecting AD are challenging and time-consuming due to lack of data and generalizability of the models.
Approach:	They propose a contrastive data augmentation method which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples.
Outcome:	The proposed method achieves the best performance among language-based models on the benchmark ADReSS Challenge dataset.

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Jun Rao, Yunjie Liao, Xuebo Liu, Zepeng Lin, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)

Challenge:	Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning.
Approach:	SeaPO introduces error types commonly occurring in large language models to improve preference learning.
Outcome:	SeaPO introduces error types into model Preference Optimization to improve model performance . negative samples are more erroneous than positive samples, and preference-based training mitigates errors .

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Jinming Zhao, Tenggan Zhang, Jingwen Hu, Yuchen Liu, Qin Jin, Xinchao Wang, Haizhou Li

Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)

Challenge:	Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity.
Approach:	They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series.
Outcome:	The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances.

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Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient.
Approach:	They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters.
Outcome:	The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks.

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

DPDV: Dual-Pathway and Dual-View Representation Learning for Bridging Information Asymmetry in Text-Video Retrieval (2026.acl-long)

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

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Zequn Xie, Xin Liu, Fangming Feng, Boyun Zhang, Tao Jin

Visual Data in NLP

Challenge:	Existing methods for text-based person anomaly search fail to address the pose-semantic gap . asymmetric cross-modal information poses a challenge to accurately establishing retrieval relationships .
Approach:	They propose a video retrieval framework that partitions visual features into two categories based on relevance to the text query and performs effective interaction.
Outcome:	The proposed framework achieves leading retrieval performance on five benchmark datasets.

Dynamic Augmentation Data Selection for Few-shot Text Classification (2022.findings-emnlp)

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Guangliang Liu, Lifeng Jin, Owen Yuan, Jiayu Zhou

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)

Challenge:	Data augmentation is a popular method for fine-tuning pre-trained language models to increase model robustness and performance.
Approach:	They propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model’s learning stage by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model.
Outcome:	The proposed method outperforms strong baselines on a variety of sentence classification tasks.

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Songtao Jiang, Chenyi Zhou, Yan Zhang, Yeying Jin, Zuozhu Liu

Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

Challenge:	Current approaches generate visual markers for all questions, generating excessive visual markers.
Approach:	They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning .
Outcome:	The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME.

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Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li

CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures (2026.eacl-long)

Challenge:	Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach:	They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome:	The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.

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Punya Syon Pandey, Yongjin Yang, Jiarui Liu, Zhijing Jin

Draft, Verify, Restore: Self-Refining Historical Inscription Restoration with a Unified MLLM (2026.acl-long)

Challenge:	Game-theoretic interactions between agents with large language models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been quantified.
Approach:	They propose a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions.
Outcome:	The proposed metric measures the effectiveness of language use within multi-agent systems across game-theoretic interactions.

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Yuyi Zhang, Junle Liu, Peirong Zhang, Jianliang Liu, Zhenhua Yang, Lianwen Jin

Challenge:	Existing methods for end-to-end historical inscription restoration rely on task-separated pipelines with irreversible error accumulation and patch-based generation that sacrifices page-level consistency.
Approach:	They propose a unified MLLM for end-to-end historical inscription restoration that integrates draft-guided localization and Hierarchical self-refinement to enable accurate damage localization.
Outcome:	The proposed model achieves superior performance in both text restoration accuracy and appearance restoration quality.

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

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Zhongwei Wan, Ziang Wu, Che Liu, Jinfa Huang, Zhihong Zhu, Peng Jin, Longyue Wang, Li Yuan

Visual Data in NLP

Challenge:	Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency.
Approach:	They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache.
Outcome:	The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache.

DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)

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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu

Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs.
Approach:	They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods.
Outcome:	The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains.

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Ruwen Zhang, Bo Liu, Zhang Sheng Xiang, Yida Chen, Hantao Zhao, Ding Ding, Jiahui Jin, Jiuxin Cao

Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)

Challenge:	Existing rerankers are mainly trained on well-edited texts, but stylistic features can be misled by reranked models.
Approach:	They propose a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness.
Outcome:	Extensive experiments show that SARK improves generation performance across multiple LLMs under mixed-style conditions.

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Zhicong Lu, Li Jin, Guangluan Xu, Linmei Hu, Nayu Liu, Xiaoyu Li, Xian Sun, Zequn Zhang, Kaiwen Wei

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

Challenge:	Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order.
Approach:	They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality .
Outcome:	The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward.

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

Using In-Context Learning to Improve Dialogue Safety (2023.findings-emnlp)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tur

Can GRPO Boost Complex Multimodal Table Understanding? (2025.emnlp-main)

Challenge:	Recent work has highlighted safety issues with large neural-based conversational models.
Approach:	They propose a retrieval-based approach for reducing bias and toxicity in chatbot responses . they retrieve demonstrations of safe responses to similar dialogue contexts to generate a response .
Outcome:	The proposed method reduces bias and toxicity in three chatbot models . it can be used in compliment to existing dialogue safety approaches, such as RLHF.

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Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin, Kaizhu Huang, Wei Wang, Yutao Yue, Xiaowei Huang, Qiufeng Wang

Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning (2025.findings-emnlp)

Challenge:	Existing table understanding methods struggle with low initialization accuracy and coarse rewards in tabular contexts.
Approach:	They propose a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities; (2) Perception Alignment GRPO (PA-GRPO); (3) Hint-Completion GR PO (HC-GRP);
Outcome:	The proposed framework outperforms existing models on held-in and held-out datasets, outperforming SFT and GRPO largely.

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Jianing Wang, Jin Jiang, Yang Liu, Mengdi Zhang, Xunliang Cai

Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)

Challenge:	Experimental results from competition-level complex reasoning demonstrate that bootstrapping with process prejudge can significantly enhance the reasoning ability of LLMs.
Approach:	They propose a new process prejudge strategy for LLM reasoning that bootstraps with process prejudgment .
Outcome:	The proposed method can be bootstrapped with process prejudge in LLM reasoning . it allows the model to anticipate errors rather than relying on trial and error.

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Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

Prototypical Reward Network for Data-Efficient Model Alignment (2024.acl-long)

Challenge:	Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored.
Approach:	They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI.
Outcome:	The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score.

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Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, Kunpeng Liu

Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is a reward model that fine-tunes Large Language Models (LLMs) by utilizing Prototypical Networks.
Approach:	They propose a framework utilizing Prototypical Networks to enhance reward models under limited human feedback, enabling more stable and reliable structural learning from fewer samples.
Outcome:	The proposed framework improves reward models under limited human feedback, surpassing traditional methods, especially in data-limited scenarios.

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Zhilun Zhou, Zihan Liu, Jiahe Liu, Yihan Wang, Qingyu Shao, Fengli Xu, Depeng Jin, Yong Li

Challenge:	Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence"
Approach:	They find a Collective Intelligence factor in human groups that captures their general capability.
Outcome:	The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities.

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)

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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu

Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)

Challenge:	Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos.
Approach:	They propose a framework that decouples epistemic planning from content generation.
Outcome:	The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage.

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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao

Implicit Personalization in Language Models: A Systematic Study (2024.findings-emnlp)

Challenge:	Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video.
Approach:	They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights.
Outcome:	The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio.

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Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, Mrinmaya Sachan

Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)

Challenge:	Existing studies have focused on the implicit personalization problem, but no unified framework exists to study it.
Approach:	They propose a mathematical formulation and a moral reasoning framework to study the phenomenon of Implicit Personalization (IP) they propose 'direct intervention' to estimate causal effect of mediator variable that cannot be directly intervened upon.
Outcome:	The proposed method estimates the causal effect of a mediator variable that cannot be directly intervened upon.

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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang

Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration (2025.acl-long)

Challenge:	Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality .
Approach:	They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting.
Outcome:	The proposed model outperforms existing models on comprehensive metrics.

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Yuyi Zhang, Peirong Zhang, Zhenhua Yang, Pengyu Yan, Yongxin Shi, Pengwei Liu, Fengjun Guo, Lianwen Jin

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Existing methods for historical document restoration focus on single modality or limited-size restoration, failing to meet practical needs.
Approach:	They propose a full-page HDR dataset and an automated HDR solution to replace manual restoration methods.
Outcome:	The proposed solution improves OCR accuracy from 46.83% to 84.05% when processing severely damaged documents, with enhancement to 94.25% through human-machine collaboration.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

Tiny Scales, Great Challenges: The Limits of Multimodal LLMs in Scale Recognition (2026.acl-long)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Jihang Jin, Ronghao Chen, Hao Zhang, Ziyan Liu, Huacan Wang, Qi Ye, Jingping Liu

Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)

Challenge:	Existing benchmarks focus on a single type of quantity or a specific format, lacking a comprehensive evaluation of scale recognition capabilities.
Approach:	They propose a visual scale recognition benchmark built using images from COCO, Open Images, and Flickr to evaluate scale recognition capabilities of multimodal large language models.
Outcome:	The proposed model achieves 42.60% accuracy, lower than the 97.40% of humans.

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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao

Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024.naacl-long)

Challenge:	Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing.
Approach:	They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language.
Outcome:	The proposed method achieves favorable control ability and audio quality.

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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab

Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation (2026.findings-acl)

Challenge:	Existing studies show the benefits of semantic representations in NLP tasks . Existing work using AMR is concerned with trainable models .
Approach:	They propose an AMR-driven chain-of-thought prompting method that uses AMR . they propose to use it to predict which input examples AMR may help or hurt on .
Outcome:	The proposed method hurts performance more than it helps on five different tasks.

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Yupu Hao, Zhuoran Jin, Huanxuan Liao, Kang Liu, Jun Zhao

H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)

Challenge:	Existing approaches treat tool use as a problem of prompt design, API documents specification, or supervised or unsupervised alignment.
Approach:	They propose a knowledge-augmented tool execution framework that integrates experiential knowledge with reasoning-width-expanded inference and knowledge-aware training.
Outcome:	The proposed framework improves on BFCL-V3 and AppWorld on three model scales.

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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li

Challenge:	Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime .
Approach:	They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM .
Outcome:	Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets .

Event Causality Extraction via Implicit Cause-Effect Interactions (2023.emnlp-main)

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Jintao Liu, Zequn Zhang, Kaiwen Wei, Zhi Guo, Xian Sun, Li Jin, Xiaoyu Li

Interpreting Positional Information in Perspective of Word Order (2023.acl-long)

Challenge:	Existing studies have not exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning.
Approach:	They propose an Implicit Cause-Effect interaction framework which captures the implicit intra- and inter-event interactions by incorporating the privileged information for reasoning.
Outcome:	The proposed framework captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning.

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Zhang Xilong, Liu Ruochen, Liu Jin, Liang Xuefeng

Explainability & Interpretability in NLP

Challenge:	Attention mechanism is a powerful and effective method utilized in natural language processing, but it is insensitive to positional information.
Approach:	They propose a weight concatenation operation to evaluate its efficacy in machine translation tasks.
Outcome:	The proposed operation can encode positional information and confirms our hypothesis.

Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals (2023.findings-acl)

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Hongru Liang, Jia Liu, Weihong Du, Dingnan Jin, Wenqiang Lei, Zujie Wen, Jiancheng Lv

Natural Language Interfaces

Challenge:	Existing methods for machine reading comprehension of user manuals have trouble answering complex questions.
Approach:	They propose a knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style and inconsistent questions about user manuals.
Outcome:	The proposed model can answer factoid-style, procedure-style and inconsistent questions about user manuals.

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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Zewen Liu, Juntong Ni, Xianfeng Tang, Max SY Lau, Qi He, Wenpeng Yin, Wei Jin

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)

Challenge:	Large Language Models (LLMs) and Multimodal LLMs (MLLMs) show strong performance in complex reasoning tasks, but their ability to extract symbolic laws from time series data remains underexplored.
Approach:	They propose a benchmark to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery.
Outcome:	The proposed framework integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system.

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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)

Challenge:	Existing retrieval augmented language models often overlook effective alignment with human preferences.
Approach:	They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity .
Outcome:	The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources.

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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu

FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models (2023.emnlp-main)

Challenge:	Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs.
Approach:	They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability.
Outcome:	The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination.

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Xinge Ma, Jiangming Liu, Jin Wang, Xuejie Zhang

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)

Challenge:	federated learning (FL) is widely studied in user-related natural language processing (NLP) but its performance is faded by confirmation bias.
Approach:	They propose a decentralized learning paradigm that uses labeled data to rectify local models . they propose federated interactive distillation (FedID) to alleviate communication overhead .
Outcome:	The proposed framework achieves the best results in homogeneous and heterogeneously federated scenarios.

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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

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data? (2025.findings-acl)

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.

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Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Nikolay Burlutskiy, Fariba Yousefi, Rossella Arcucci

UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks (2024.emnlp-main)

Challenge:	Medical Vision-Language Pretraining (MedVLP) models typically require large-scale datasets with paired, high-quality image-text data.
Approach:	They propose to generate large-scale synthetic image-text pairs using off-the-shelf generative models . they propose to isolate model and training settings, focusing entirely from the data perspective.
Outcome:	The proposed pipeline outperforms models trained on real data by 3.8% on averaged AUC on zero-shot classification tasks.

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Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei

DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)

Challenge:	Recent advances in large multimodal models have encouraged the development of large multi-modal models . however, it is unclear how to extend these models to the more complex video domain .
Approach:	They propose a visual instruction tuning framework to address temporal video-language tasks . they collect a dataset and fine-tune the framework on instruction-following data .
Outcome:	The proposed model can perform better on established temporal video-language tasks without training objectives and intensive pre-training.

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Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

Summarization

Challenge:	Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics .
Approach:	They propose a new paradigm to construct adaptive timelines based on user instructions or requirements.
Outcome:	The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines.

ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding (2020.acl-main)

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Zhiwen Xie, Guangyou Zhou, Jin Liu, Jimmy Xiangji Huang

Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)

Challenge:	Existing knowledge graph embedding methods are limited in their expressiveness and lack structural information in the embeddable space.
Approach:	They propose to use a relation-aware network to learn query embedding . they first explore the Inception network to further increase interactions between head and relation embedders .
Outcome:	The proposed network improves performance on WN18RR and FB15k-237 datasets.

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Dongqi Huang, Tong Zhou, Zhuoran Jin, Shenghui Shi, null Maoyujiao, Kang Liu, Jun Zhao, Yubo Chen

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise.
Approach:	They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm.
Outcome:	The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods.

Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning (2026.acl-long)

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Tianyi Men, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Challenge:	Multimodal web agents are cost-efficient and privacy-preserving, but suffer from weak planning and limited cross-website generalization.
Approach:	They propose a method which autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize strictly aligned, high-level training data.
Outcome:	The proposed method outperforms Qwen2.5-VL-32B model on real-world benchmarks and demonstrates that mastering low-level atomic skills does not guarantee high-level planning competence.

Towards Multi-Relational Multi-Hop Reasoning over Dense Temporal Knowledge Graphs (2024.findings-acl)

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Jian Liu, Zihe Liu, Xueqiang Lyu, Peng Jin, Jinan Xu

InstructoR: Instructing Unsupervised Conversational Dense Retrieval with Large Language Models (2023.findings-emnlp)

Challenge:	Temporal knowledge graph reasoning is a crucial task for answering time-dependent questions within a knowledge graph (KG).
Approach:	They propose a temporal KG reasoning benchmark with over 200k entities and 960k questions that facilitate complex, multi-relational and multi-hop reasoning.
Outcome:	The proposed model is able to conduct pattern-aware and time-sensitive reasoning across temporal KGs and is scalable to a wide range of data conditions.

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Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Low-Resource NLP

Challenge:	Existing methods for conversational retrieval only fine-tune on limited supervised data, making it difficult for the retriever to fully grasp the entire conversation.
Approach:	They propose a method to instruct unsupervised conversational dense retrieval with large language models (LLMs) they use supervised data to discover the user's query intent from the conversation context .
Outcome:	The proposed method can bring significant improvements across various ad-hoc retrievers, surpassing the current state-of-the-art method.

Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation (2025.acl-long)

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Shuo Tang, Xianghe Pang, Zexi Liu, Bohan Tang, Rui Ye, Tian Jin, Xiaowen Dong, Yanfeng Wang, Siheng Chen

Challenge:	Extensive experiments demonstrate that our framework effectively generates both general and domain-specific data.
Approach:	They propose a multi-agent simulator that automatically generates diverse text-based scenarios, capturing a wide range of real-world human needs.
Outcome:	Experiments show that the proposed model outperforms Meta’s Llama-3-8B-Instruct model on AlpacaEval 2 and Arena-Hard benchmarks with just 20K instruction-response pairs.

Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)

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Zhuoran Jin, Pengfei Cao, Zhitao He, Yubo Chen, Kang Liu, Jun Zhao

Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)

Challenge:	Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types.
Approach:	They propose a method to recognize entities in novel types by their textual names or descriptions.
Outcome:	The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types.

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Zhiwen Xie, Yi Zhang, Jin Liu, Guangyou Zhou, Jimmy Huang

CARE: A Clue-guided Assistant for CSRs to Read User Manuals (2024.acl-long)

Challenge:	Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features .
Approach:	They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples.
Outcome:	The proposed model outperforms state-of-the-art models in relation prediction task.

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Weihong Du, Jia Liu, Zujie Wen, Dingnan Jin, Hongru Liang, Wenqiang Lei

Challenge:	Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses.
Approach:	They propose to build a clue-guided assistant for customer service representations (CSRs) that can provide accurate responses and explicitly show explainable paths about how to arrive at these responses.
Outcome:	The proposed assistant can reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation (2022.emnlp-main)

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Kaiyu Huang, Peng Li, Jin Ma, Yang Liu

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)

Challenge:	Existing methods to update a multilingual model with new language pairs are expensive and time-consuming.
Approach:	They propose an entropy-based vocabulary substitution method that walks through new language pairs for incremental learning while remaining the size of the original vocabulary.
Outcome:	The proposed method achieves better performance and saves excess overhead in a multilingual machine translation task.

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Yu Zhang, Ruijie Yu, Jidong Tian, Feng Zhu, Jiapeng Liu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing (2025.findings-acl)

Challenge:	Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation.
Approach:	They propose a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.
Outcome:	The proposed model outperforms the baseline model on R2D and D2A tasks by 10%.

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Neemesh Yadav, Jiarui Liu, Francesco Ortu, Roya Ensafi, Zhijing Jin, Rada Mihalcea

MCS-Bench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in Chinese Classical Studies (2025.acl-long)

Challenge:	Existing knowledge on how and why NLP methods make content moderation decisions is limited . authors examine how and when to use LLMs in content modeation .
Approach:	They use Shapley values and LLM-guided explanations to reverse-engineer content moderation decisions across countries.
Outcome:	The proposed methods show that they reverse-engineer content moderation decisions across countries and over time.

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Yang Liu, Jiahuan Cao, Hiuyi Cheng, Yongxin Shi, Kai Ding, Lianwen Jin

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Multimodal Large Language Models (MLLMs) have advanced visual and language understanding, but their potential in Chinese Classical Studies (CCS) remains underexplored due to the lack of specialized benchmarks.
Approach:	They propose to develop a multimodal benchmark specifically designed for Chinese Classical Studies across multiple subdomains to bridge this gap.
Outcome:	The proposed benchmark spans seven core subdomains with a total of 45 meticulously designed tasks.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu, Zhi Jin

A Good Neighbor, A Found Treasure: Mining Treasured Neighbors for Knowledge Graph Entity Typing (2022.emnlp-main)

Challenge:	Existing training methods for code generation do not improve code correctness and efficiency.
Approach:	They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency.
Outcome:	The proposed framework improves code correctness and efficiency by integrating preference learning into code generation.

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Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)

Challenge:	Existing methods to infer missing types for knowledge graphs only leverage one-hop neighbor information of the central entity, ignoring multi-hop neighbors that can provide valuable clues for inference.
Approach:	They propose a method to infer missing types for knowledge graph entities by using neighbor information and co-occurrence relations between types.
Outcome:	The proposed method significantly outperforms existing state-of-the-art methods on two widely used datasets.

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Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen

Text Generation

Challenge:	RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files.
Approach:	They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness .
Outcome:	The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world.

The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification (2021.findings-acl)

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Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang

Text Classification

Challenge:	Existing studies on text classification have focused on the bias towards the individuals mentioned in the text content.
Approach:	They propose a framework to mitigate implicit bias in text classification models based on demographic attributes of authors . they propose to use this framework to train deep text classifiers to make predictions on the right features .
Outcome:	The proposed framework outperforms existing models significantly in fairness and performance.

AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)

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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao

DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis (2022.coling-1)

Challenge:	Recent advances in deep learning have significantly impacted the legal domain.
Approach:	They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base .
Outcome:	The proposed framework outperforms existing methods in various aspects, especially in generating legal articles.

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Yuchen Liu, Jinming Zhao, Jingwen Hu, Ruichen Li, Qin Jin

Sentiment Analysis

Challenge:	Emotion Recognition in Conversation (ERC) has attracted increasing research attention in recent years.
Approach:	They propose to model the emotional interactions between speakers to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues.
Outcome:	The proposed model can achieve superior performance compared to state-of-the-art methods on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog.

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information (2023.eacl-main)

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Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur

Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)

Challenge:	Intent detection is a fundamental element in task-oriented dialogue systems, usually occurring within the Natural Language Understanding component.
Approach:	They propose an in-context data augmentation approach that fine-tunes a pre-trained language model and synthesizes new datapoints that correspond to given intents.
Outcome:	The proposed method produces training data that achieves state-of-the-art on three challenging intent detection datasets and performs on par with the state- of-the art in full-shot settings.

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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea

LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts (2026.findings-acl)

Challenge:	Recent advances in large language models have led to misleading public discourse that “it’s all been solved.”
Approach:	They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs.
Outcome:	The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs.

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Chengyuan Jin, Ao Chang, Daojian Zeng, Wenhao Teng, Xiangwen Liao, Kang Liu, Jun Zhao, Yubo Chen

A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)

Challenge:	Temporal knowledge graph forecasting (TKGF) uses long-window strengthscores and short-windowed novelty scores to predict missing entities in future queries.
Approach:	They propose a training-freeprompting framework that uses two perspectives of history to predict missing entities in future queries.
Outcome:	The proposed framework outperforms the state-of-the-art baselineAnRe framework in ICEWS14, ICEW05-15, and GDELT.

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Zhiwen Xie, Runjie Zhu, Kunsong Zhao, Jin Liu, Guangyou Zhou, Jimmy Xiangji Huang

Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)

Challenge:	Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs.
Approach:	They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.
Outcome:	The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets.

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Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain (2026.acl-long)

Challenge:	Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences.
Approach:	They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs.
Outcome:	The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW.

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Song Jin, Juntian Zhang, Xun Zhang, Zeying Tian, Fei Jiang, Guojun Yin, Wei Lin, Yong Liu, Rui Yan

Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)

Challenge:	Existing vision-language models lack fine-grained classification, single-view imagery, and inaccurate metadata.
Approach:	They propose a hierarchical, multi-view benchmark to evaluate VLMs across three levels of cognitive complexity.
Outcome:	The proposed benchmark evaluates vision-language models across three levels of complexity . it systematically identifies five primary failure modes . the proposed benchmarks are available on https://github.com/meituan/DiningBench.

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Jin Liu, Chongfeng Fan, Zhou Fengyu, Huijuan Xu

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

Challenge:	Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization.
Approach:	They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context.
Outcome:	The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency.

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

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Kaiyu Huang, Peng Li, Jin Ma, Ting Yao, Yang Liu

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization (2023.acl-long)

Challenge:	Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning.
Approach:	They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs.
Outcome:	The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs.

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SongYang Gao, Shihan Dou, Yan Liu, Xiao Wang, Qi Zhang, Zhongyu Wei, Jin Ma, Ying Shan

RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)

Challenge:	Existing adversarial training methods require multi-step gradient ascents or word substitutions to obtain adversarials, which impairs the effectiveness of adversariarial training.
Approach:	They propose a procedure for instead adversarial training with only clean data that estimates the adversarials loss by perturbing the input data’s probability distribution rather than their embeddings.
Outcome:	The proposed procedure reduces time consumption by up to 70% compared to current best-performing adversarial training methods.

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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li

Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)

Challenge:	Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
Approach:	They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone .
Outcome:	Experiments on four TVR datasets show that the proposed method performs better than other methods.

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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs (2024.naacl-long)

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.

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Tingxuan Chen, Jun Long, Liu Yang, Zidong Wang, Yongheng Wang, Xiongnan Jin

Knowledge Representation

Challenge:	Temporal knowledge graphs (TKGs) are powerful tools for storing and modeling dynamic facts.
Approach:	They propose a Hawkes process-based temporal causal convolutional network for temporal reasoning under extrapolation settings.
Outcome:	The proposed network is based on Hawkes process-based temporal causal convolutional network and captures the temporal evolution of facts.

Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity (2025.acl-long)

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Yupu Hao, Pengfei Cao, Zhuoran Jin, Huanxuan Liao, Yubo Chen, Kang Liu, Jun Zhao

Challenge:	Personalized tool utilization is essential for aligning large language models (LLMs) with user preference in interaction scenarios with various tools.
Approach:	They propose a key-point-based LLM evaluation method that mitigates biases by manually annotating key points for each test case and providing them to LLM as the reference.
Outcome:	The proposed method mitigates biases in the LLM-as-a-judge system by manually annotating key points for each test case and providing them to LLM as the reference.

LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models (2024.lrec-main)

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Chuang Liu, Renren Jin, Yuqi Ren, Deyi Xiong