Papers by Chen Zhu

Attention as Selector: Unlocking VLM Attention for Long Document Page Retrieval (2026.acl-long)

Challenge:	Existing methods for identifying student misconceptions overlook students' reasoning processes, authors report .
Approach:	They propose a knowledge distillation framework that mines high-value samples from existing data.
Outcome:	The proposed framework outperforms sota LLM and standard fine-tuned 72B models on cross-topic tests.

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Minfeng Zhu, Linxin Bao, Wei Chen, Linchao Zhu

Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang

Challenge:	Existing page-level retrieval methods lack query–page interaction before similarity scoring . Existing methods require large-scale datasets to align visual and textual embeddings .
Approach:	They propose a retrieval framework that utilizes attention mechanisms inside VLMs for page selection.
Outcome:	The proposed retrieval framework outperforms embedding-based retrieval methods on four long-document benchmarks.

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Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages.
Approach:	They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods.
Outcome:	The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs.

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Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu

A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models (2025.naacl-long)

Challenge:	Existing research to improve CoT efficiency falls into three categories, each with distinct limitations.
Approach:	They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination.
Outcome:	Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy.

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Xiujie Song, Mengyue Wu, Kenny Q. Zhu, Chunhao Zhang, Yanyi Chen

META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)

Challenge:	Large Vision-Language Models (LVLMs) are hardly comprehensively evaluated for their cognitive abilities.
Approach:	They propose to evaluate high-level cognitive abilities of Large Vision-Language Models (LVLMs) using images with rich semantics.
Outcome:	The proposed evaluation benchmark consists of 251 images along with comprehensive annotations.

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Liangtai Sun, Xingyu Chen, Lu Chen, Tianle Dai, Zichen Zhu, Kai Yu

Question Answering as Programming for Solving Time-Sensitive Questions (2023.emnlp-main)

Challenge:	Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task.
Approach:	They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs.
Outcome:	The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs.

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Xinyu Zhu, Cheng Yang, Bei Chen, Siheng Li, Jian-Guang Lou, Yujiu Yang

UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions (2025.findings-acl)

Challenge:	Recent studies show that Large Language Models (LLMs) have shown remarkable intelligence in question answering.
Approach:	They propose to reframe the Question Answering task as Programming to overcome this limitation by leveraging LLMs' superior ability in understanding both natural language and programming language.
Outcome:	The proposed approach improves on time-sensitive question answering datasets by 14.5% over baselines.

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Chuanyuan Tan, Wenbiao Shao, Hao Xiong, Tong Zhu, Zhenhua Liu, Kai Shi, Wenliang Chen

Knowledge Representation

Challenge:	Existing datasets to assess LLMs' performance on unanswerable questions lack factual knowledge support.
Approach:	They propose a bilingual unanswerable question dataset with auxiliary factual knowledge created from a Knowledge Graph and two new tasks to measure LLMs' ability to utilize internal and external factual information.
Outcome:	The proposed datasets show that LLMs do not consistently perform well even when they have factual knowledge stored.

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)

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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen

USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following.
Approach:	They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply.
Outcome:	The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply.

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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)

Challenge:	Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations.
Approach:	They propose a framework that covers 61 risk categories across four modality interactions to address this gap.
Outcome:	The proposed framework covers 61 risk categories across four distinct modality interactions.

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Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Huang Chen, Dongxia Wang, Jie Zhang

Green & Sustainable NLP

Challenge:	Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume.
Approach:	They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data.
Outcome:	The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs.

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2024.acl-long)

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Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)

Challenge:	Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks .
Approach:	They propose an execution-based, multilingual, multitask evaluation benchmark for LLMs . they evaluate coding performance from three dimensions: length, difficulty, efficiency .
Outcome:	The proposed benchmark covers 43 programming languages and eight coding tasks.

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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin

RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoning (2025.acl-industry)

Challenge:	Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models.
Approach:	They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information .
Outcome:	The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate.

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Deyi Ji, Yuekui Yang, Haiyang Wu, Shaoping Ma, Tianrun Chen, Lanyun Zhu

Large Language Models are not Fair Evaluators (2024.acl-long)

Challenge:	Existing methods for detecting ads video violations lack precise temporal grounding, noisy annotations, and limited generalization.
Approach:	They propose a framework that integrates curriculum reinforcement learning with large language models to enhance reasoning and cognitive capabilities for violation detection.
Outcome:	The proposed framework achieves superior performance in violation category accuracy and temporal interval localization.

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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui

LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)

Challenge:	Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent.
Approach:	They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt.
Outcome:	The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments.

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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)

Challenge:	Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens.
Approach:	They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains.
Outcome:	The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy.

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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen

Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)

Challenge:	Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods.
Approach:	They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation.
Outcome:	The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs.

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Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Yu Meng

Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation (2023.acl-short)

Challenge:	Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs.
Approach:	They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself.
Outcome:	The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks.

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Yuchen Han, Chen Xu, Tong Xiao, Jingbo Zhu

Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis (2025.acl-long)

Challenge:	End-to-end speech translation models have limited training data and are often inefficient due to the inconsistency of length and representation between speech and text.
Approach:	They find that the "modality gap" between speech and text data is not a major problem in E2E ST . they decouple the encoder to speech encoder and text encoder, and they find that there is a 'capacity gap'
Outcome:	The proposed model achieves 29.0 for en-de and 40.3 for fr on the MuST-C dataset.

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Minghao Lv, Siyuan Chen, Haoan Jin, Minghao Yuan, Qianqian Ju, Yujia Peng, Kenny Q. Zhu, Mengyue Wu

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

Challenge:	Existing studies have indicated that major life events can greatly impact individuals’ mental health, but shedding its light on social media data is challenging due to the complexity and ambiguity nature of life events.
Approach:	They propose to extract life events mentioned in posts on social media to uncover a social media event dataset which includes 12 major life event categories that are likely to occur in everyday life.
Outcome:	The proposed dataset includes 12 life event categories that are likely to occur in everyday life and is human-annotated under iterative procedure and boasts a high level of quality.

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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao

Speech & Audio in NLP

Challenge:	Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach:	They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome:	The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2025.acl-long)

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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Yingchaojie Feng, Yiqun Sun, Yandong Sun, Minfeng Zhu, Qiang Huang, Anthony Kum Hoe Tung, Wei Chen

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

Challenge:	Existing methods for text embedding require re-encoding the entire corpus for each instruction.
Approach:	They propose a framework that generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text.
Outcome:	The proposed framework improves instruction-following text embedding quality over state-of-the-art methods while speeding up processing on large datasets.

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

Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)

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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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong

Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs).
Approach:	They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues.
Outcome:	The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements.

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He Zhu, Zhiwen Ruan, Junyou Su, Xingwei He, Yun Chen, Wenjia Zhang, Guanhua Chen

Challenge:	High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity.
Approach:	They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion.
Outcome:	The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space.

Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives (2024.lrec-main)

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Qiushi Sun, Chengcheng Han, Nuo Chen, Renyu Zhu, Jingyang Gong, Xiang Li, Ming Gao

Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have shown increasing power on NLP tasks. however, tuning these models for downstream tasks usually requires exorbitant costs.
Approach:	They propose a black-box tuning technique that optimizes task-specific prompts without accessing gradients and hidden representations.
Outcome:	The proposed method improves performance under few-shot learning scenarios.

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Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu, Hengshuang Zhao, Jiaya Jia

Code-Switching Can be Better Aligners: Advancing Cross-Lingual SLU through Representation-Level and Prediction-Level Alignment (2024.acl-short)

Challenge:	Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting.
Approach:	They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content.
Outcome:	The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning.

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Zhihong Zhu, Xuxin Cheng, Zhanpeng Chen, Xianwei Zhuang, Zhiqi Huang, Yuexian Zou

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)

Challenge:	Existing code-switching-based cross-lingual spoken language understanding frameworks are limited to low-resource languages.
Approach:	They propose a cross-lingual spoken language understanding framework that leverages both code-switched and original sentences to achieve multi-level alignment.
Outcome:	The proposed framework can achieve multi-level alignment on two benchmarks across ten languages.

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Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, Kai Yu

ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)

Challenge:	Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances.
Approach:	They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances.
Outcome:	The proposed framework is effective and compatible with supervised training.

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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Challenge:	Existing infrastructure for efficient agentic data processing and model training remains underdeveloped.
Approach:	They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 .
Outcome:	The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks.

Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)

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Zhongyi Ye, Weitai Zhang, Xinyuan Zhou, Yongxin Zhu, Ninghui Rao, Enhong Chen

Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)

Challenge:	Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited.
Approach:	They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources.
Outcome:	The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models.

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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen

Challenge:	Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents .
Approach:	a new framework is proposed to improve agents collaboration through iterative alignment.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks.

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu

Tailoring Vaccine Messaging with Common-Ground Opinions (2024.findings-naacl)

Challenge:	Existing studies show that Large Language Models can be misused to generate undesired content.
Approach:	They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs.
Outcome:	The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs.

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Rickard Stureborg, Sanxing Chen, Roy Xie, Aayushi Patel, Christopher Li, Chloe Zhu, Tingnan Hu, Jun Yang, Bhuwan Dhingra

Challenge:	Vaccine interventions aim to answer concerns expressed about vaccination.
Approach:	They propose a dataset to evaluate how well responses are tailored to a common-ground opinion . they find that GPT-4-Turbo performs significantly better than others .
Outcome:	The proposed dataset outperforms fine tuned LLMs on the task of tailoring vaccine responses to common-ground opinions.

Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)

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Zhu Min, Yanchao Hao, Jian Liu, Shizhu He, Xi Chen

On the Vulnerability of Safety Alignment in Open-Access LLMs (2024.findings-acl)

Challenge:	Existing approaches to improve retrieval performance of large language models are limited by static knowledge.
Approach:	They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval.
Outcome:	The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA.

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Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, Fangzhao Wu

Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)

Challenge:	Large language models (LLMs) are susceptible to malicious exploitation, but are often rejected and limited harmfulness is limited.
Approach:	They propose two types of reverse alignment techniques: reverse supervised fine-tuning (RSFT) and reverse preference optimization (RPO).
Outcome:	The proposed methods can significantly enhance the success rate and harmfulness of jailbreak attacks, but they face high rejection rates and limited harmfulness.

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Jieyu Li, Lu Chen, Ruisheng Cao, Su Zhu, Hongshen Xu, Zhi Chen, Hanchong Zhang, Kai Yu

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)

Challenge:	Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated.
Approach:	They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
Outcome:	The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.

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Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

Challenge:	Existing benchmarks focus on functional relevance while neglecting code quality.
Approach:	They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets .
Outcome:	The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts.

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

OjaKV: Context-Aware Online Low-Rank KV Cache Compression (2026.findings-acl)

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

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Yuxuan Zhu, David H. Yang, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Pin-Yu Chen

Challenge:	Existing methods for inference use static, offline-learned subspaces that perform poorly under distribution shifts.
Approach:	They propose a framework that integrates a storage policy with an online subspace adaptation to preserve key-value tokens in full rank as high-fidelity anchors.
Outcome:	Experiments show that OjaKV maintains or improves zero-shot accuracy at high compression ratios, achieving the strongest gains on long-context benchmarks requiring complex reasoning.

WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)

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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li

Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)

Challenge:	Existing efforts to generate Wikipedia articles for new events fall short of real-world application.
Approach:	They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios.
Outcome:	The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability.

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Lin Zhong, Siyu Zhu, Zizhen Yuan, Jinhao Cui, Xinyang Zhao, Lingzhi Wang, Hao Chen, Qing Liao

Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)

Challenge:	Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection.
Approach:	They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states.
Outcome:	The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure.

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Ziqi Wang, Chen Zhu, Zhi Zheng, Xinhang Li, Tong Xu, Yongyi He, Qi Liu, Ying Yu, Enhong Chen

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? (2025.naacl-industry)

Challenge:	Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors.
Approach:	They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively.
Outcome:	The proposed framework achieves state-of-the-art on the fine-grained content extraction task.

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Xiliang Zhu, Elena Khasanova, Cheng Chen

StructMem: Structured Memory for Long-Horizon Behavior in LLMs (2026.acl-short)

Challenge:	Large language models require considerable computing resources, which can be costly and often unavailable.
Approach:	They propose to integrate 4-bit Post-training Quantization with QLoRA to address these issues . they demonstrate that the integration outperforms standard quantization and fine-tuning .
Outcome:	The proposed integration outperforms standard PTQ and 16-bit full-parameter fine-tuning on LLMs.

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Buqiang Xu, Yijun Chen, Jizhan Fang, Ruobin Zhong, Yunzhi Yao, Yuqi Zhu, Lun Du, Shumin Deng

The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)

Challenge:	Existing memory systems lack structure and efficiency in capturing relationships between events.
Approach:	They propose a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections.
Outcome:	The proposed framework preserves event-level bindings and induces cross-event connections while reducing token usage, API calls, and runtime compared to prior memory systems.

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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)

Challenge:	MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models .
Approach:	They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop.
Outcome:	The proposed model can significantly compress a large model without significant performance drop.

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Jizhihui Liu, Guangdao Zhu, Feiyi Du, Niu Lian, Jun Li, Bin Chen, Weili Guan, Yaowei Wang

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)

Challenge:	Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens.
Approach:	They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention .
Outcome:	The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens.

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Yingjie Zhu, Xuefeng Bai, Kehai Chen, Yang Xiang, Jun Yu, Min Zhang

TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)

Challenge:	Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains.
Approach:	They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks.
Outcome:	Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information.

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Wenxi Xu, Chuan Qin, Xi Chen, Chuyu Fang, Yuanchun Zhou, Hengshu Zhu

Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

Challenge:	Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters.
Approach:	They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space.
Outcome:	The proposed framework outperforms state-of-the-art methods on four benchmark datasets.

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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang

CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)

Challenge:	Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach:	They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome:	The proposed method is based on an industrial e-commerce benchmark and public datasets.

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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)

Challenge:	Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored.
Approach:	They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces.
Outcome:	The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces .

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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao

LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations (2021.acl-long)

Challenge:	Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue.
Approach:	They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions.
Outcome:	The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses.

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Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, Kai Yu

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation (2023.findings-acl)

Challenge:	Existing methods to encode text-to-SQL data are node-centric and ignore semantics embedded in the topological structure of edges.
Approach:	They propose a Line Graph Enhanced Text-to-SQL model to mine relational features without constructing meta-paths.
Outcome:	The proposed model achieves state-of-the-art on the cross-domain text-to-SQL benchmark Spider at the time of writing.

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Wenhao Zhu, Shujian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen

Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System (2024.findings-emnlp)

Challenge:	kNN-MT builds an external datastore, which saves all target language token occurrences in the parallel corpus.
Approach:	They propose a new paradigm for domain adaptation by building an external datastore which usually saves all target language token occurrences in the parallel corpus.
Outcome:	The proposed model can be easily pruned according to local correctness, and it is more explainable.

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Wanshi Xu, Xuxin Cheng, Zhihong Zhu, Zhanpeng Chen, Yuexian Zou

Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)

Challenge:	Existing systems for task-oriented dialogue lack belief states as supervisory signals.
Approach:	They propose a method for knowledge retrieval driven by matching representations . they use a matching signal extractor to extract matching representation between contexts and entities .
Outcome:	Experiments on three standard benchmarks show that the proposed method performs better than existing approaches.

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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu

Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models (2025.findings-acl)

Challenge:	Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects.
Approach:	They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt.
Outcome:	The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks.

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Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, QingqingLong QingqingLong, Yefeng Zheng, Xian Wu

MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)

Challenge:	Hallucination is a critical challenge for large language models and large vision-language models (LVLMs) however, dedicated research on medical hallucinations remains unexplored.
Approach:	They provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes.
Outcome:	The proposed models have demonstrated impressive performance on a variety of medical benchmarks.

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Jingfan Zhang, Yi Zhao, Dan Chen, Xing Tian, Huanran Zheng, Wei Zhu

Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)

Challenge:	Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules.
Approach:	They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism.
Outcome:	Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets.

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Zhenhua Liu, Lijun Li, Ruizhe Chen, Yuxian Jiang, Tong Zhu, Zhaochen Su, Wenliang Chen, Jing Shao

AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)

Challenge:	Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy .
Approach:	They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements .
Outcome:	The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization.

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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)

Challenge:	Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks.
Approach:	They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.
Outcome:	The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS.

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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)

Challenge:	Existing approaches to program repair are based on correctness alone.
Approach:	They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits.
Outcome:	The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing.

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Sirui Xia, Aili Chen, Xintao Wang, Tinghui Zhu, Yikai Zhang, Jiangjie Chen, Yanghua Xiao

Challenge:	Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning.
Approach:	They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions.
Outcome:	The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions.

Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024.acl-long)

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Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)

Challenge:	Experimental results show that fine-tuning of large language models for specific tasks can be challenging . distribution shift during fine-timing can lead to performance degradation in general task capabilities .
Approach:	They propose a new approach that bridges the distribution gap between task datasets and LLMs by guiding fine-tuning with a distilled dataset generated by the model itself.
Outcome:	The proposed approach achieves comparable or superior performance on downstream tasks compared to the vanilla approach.

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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)

Challenge:	i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data.
Approach:	They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data.
Outcome:	i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks.

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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

TokenPenalty: Alleviating Attention Sinks and Positional Decay in LVLMs (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone.
Approach:	They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs.
Outcome:	The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

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Xiaofeng Zhang, Yuanchao Zhu, Qiyan Zhao, Xiaosong Yuan, Jiawei Cao, Xuhang Chen

Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)

Challenge:	Multimodal large language models (MLLMs) often hallucinate due to two relevant phenomena: massive activation phenomenon and positional information decay.
Approach:	They propose a token-level intervention strategy that dynamically suppresses irrelevant visual tokens while preserving key contextual signals.
Outcome:	Experiments show that TokenTruth significantly improves factual consistency across MLLMs on standard image understanding benchmarks.

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Junjie Yu, Tong Zhu, Wenliang Chen, Wei Zhang, Min Zhang

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)

Challenge:	Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models.
Approach:	They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data.
Outcome:	The proposed model improves performance even on a strong baseline.

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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou

Robust Learning for Multi-party Addressee Recognition with Discrete Addressee Codebook (2023.acl-short)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Approach:	They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions.
Outcome:	The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks.

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Pengcheng Zhu, Wei Zhou, Kuncai Zhang, Yuankai Ma, Haiqing Chen

Beyond Atomic Characters: Glyph-Aware Sub-character Alignment for Low-Resource Multilingual OCR (2026.acl-long)

Challenge:	Existing addressee recognition models suffer from the issue of robustness when applied in real-world scenes.
Approach:	They propose a method which discretes addressees into a character codebook and makes it robust in a noisy environment.
Outcome:	The proposed method represents open set addressees and is robust even in noisy environments.

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Mengxiao Zhu, Haixu Chen, Jiu Sha, Jie Liu, Ge Shi

Visual Data in NLP

Challenge:	Low-resource multilingual OCR models struggle with complex script structures and data scarcity.
Approach:	They propose a framework for multilingual character recognition that integrates visual and linguistic backbones with a novel glyph-aware interface.
Outcome:	The proposed framework improves on high-resolution visual and language backbones with glyph-aware interface.

Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)

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Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)

Challenge:	Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Approach:	They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons.
Outcome:	The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process.

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Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu

KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph (2025.acl-long)

Challenge:	Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels .
Approach:	They propose a new architecture which processes schemas at abstract and semantic levels.
Outcome:	The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema .

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Jinhao Jiang, Kun Zhou, Xin Zhao, Yang Song, Chen Zhu, Hengshu Zhu, Ji-Rong Wen

Knowledge Representation

Challenge:	Existing methods to design the interaction strategy between large language models and knowledge graphs (KGs) are not effective for large language model (LLM)s to solve complex tasks due to the large volume and structured format of KG data.
Approach:	They propose an LLM-based agent framework that enables small LLMs to actively make decisions over knowledge graphs.
Outcome:	The proposed framework outperforms existing methods on in-domain and out-domain datasets using 10K samples.

Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)

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Shunyu Liu, Jie Zhou, Qunxi Zhu, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

Sentiment Analysis

Challenge:	Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models.
Approach:	They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner.
Outcome:	Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models.

Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models (2026.findings-acl)

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Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang

EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)

Challenge:	Existing Vid-LLMs lack robust mechanisms for maintaining grounded spatiotemporal beliefs under conversational feedback.
Approach:	They propose a negation-based gaslighting evaluation framework and introduce a benchmark to investigate spatiotemporal sycophancy.
Outcome:	The proposed framework evaluates state-of-the-art Vid-LLMs across video understanding tasks.

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Hao Zhou, Minlie Huang, Yong Liu, Wei Chen, Xiaoyan Zhu

TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)

Challenge:	Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation.
Approach:	They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph.
Outcome:	The proposed model generates more informative, coherent, and natural responses than baseline models.

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Jiajun Chen, Yangyang Wu, Xiaoye Miao, Mengying Zhu, Meng Xi

Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)

Challenge:	Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios.
Approach:	They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data.
Outcome:	The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models.

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Tiantian Zhu, Yang Qin, Qingcai Chen, Xin Mu, Changlong Yu, Yang Xiang

Knowledge Representation

Challenge:	Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB).
Approach:	They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template.
Outcome:	The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation.

Employing Discourse Coherence Enhancement to Improve Cross-Document Event and Entity Coreference Resolution (2025.acl-long)

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Xinyu Chen, Peifeng Li, Qiaoming Zhu

ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval (2026.acl-long)

Challenge:	Existing work on cross-document coreference resolution focuses on within-document events and entities, but cross-doc mentions lack such critical contexts.
Approach:	They propose a task to enhance the discourse coherence between two cross-document mentions by adding coherent texts to a document to form a new coherent document.
Outcome:	The proposed method outperforms state-of-the-art baselines on three popular datasets.

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David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Subhajit Chaudhury, Pin-Yu Chen

SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration (2024.emnlp-industry)

Challenge:	Large language models generate long chain of thoughts but memory footprint grows with output length . prior work on KV cache optimization focused on compressing long input context .
Approach:	They propose a new approach that compresses verbose reasoning thoughts into summaries . they use a dynamic KV cache selection policy that leverages these summary keys .
Outcome:	The proposed approach reduces memory usage while avoiding full-cache attention at each step.

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Yuanhao Shen, Xiaodan Zhu, Lei Chen

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) have a profound impact on a wide range of applications.
Approach:	They propose a framework to mitigate the tool-abuse behavior of Large Language Models and propose SMARTCAL to mitigate this issue.
Outcome:	The proposed framework improves the performance of LLMs on three datasets with two mainstream tool-use frameworks and shows an 8.6% increase in QA performance and 21.6 percent lower expected calibration error (ECE) than existing methods.

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents (2026.findings-acl)

Challenge:	Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP.
Approach:	They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens.
Outcome:	The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs.

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Zhenghao Zhu, Yuanfeng Song, Xing Chen, Chengzhong Liu, Cui Yakun, Caleb Chen Cao, Sirui Han, Yike Guo

Challenge:	Existing frameworks for data analysis and insight exploration are lacking in terms of benchmarks . existing frameworks suffer from format inconsistencies, poorly conceived objectives, and redundant insights.
Approach:	They propose a data-curation pipeline to construct a new dataset named InsightEval.
Outcome:	The proposed benchmarks highlight prevailing challenges in automated insight discovery and raise key findings to guide future research.

Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)

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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang

Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)

Challenge:	Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets.
Approach:	They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains.
Outcome:	The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)

Challenge:	Despite the success of speech recognition, how to encode the speech features effectively remains an open problem.
Approach:	They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation.
Outcome:	The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x.

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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)

Challenge:	Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact.
Approach:	They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy.
Outcome:	The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency.

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Zuyi Bao, Rui Huang, Chen Li, Kenny Zhu

Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)

Challenge:	Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge.
Approach:	They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling.
Outcome:	The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese.

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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu

BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)

Challenge:	Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation.
Approach:	They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor.
Outcome:	The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets.

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Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen, Jiayu Li, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala

Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks (2026.acl-long)

Challenge:	Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively.
Approach:	They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts.
Outcome:	The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts.

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Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang

Parameter-free Sentence Embedding via Orthogonal Basis (D19-1)

Challenge:	Generating synthetic datasets via large language models (LLMs) has emerged as promising approach to improve LLM performance.
Approach:	They propose three mitigation strategies to mitigate bias inheritance in LLMs by analyzing real and LLM-augmented data.
Outcome:	The proposed methods can work differently on different tasks and biases.

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Ziyi Yang, Chenguang Zhu, Weizhu Chen

Representation Learning

Challenge:	Existing methods to build sentence embeddings are parameterized and require training to optimize their parameters.
Approach:	They propose a non-parameterized method to combine pre-trained word embeddings into sentence representations using an orthogonal basis of the word vector subspace and its surrounding context.
Outcome:	The proposed method shows superior performance on 11 downstream NLP tasks and is competitive to other methods relying on large amounts of labelled data or prolonged training time.

Dual-oriented Disentangled Network with Counterfactual Intervention for Multimodal Intent Detection (2024.emnlp-main)

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Zhanpeng Chen, Zhihong Zhu, Xianwei Zhuang, Zhiqi Huang, Yuexian Zou

Sentiment Analysis

Challenge:	Existing methods for multimodal intent detection have two limitations: (i) close entanglement of multimodal semantics with modal structures; (ii) insufficient learning of causal effects of semantic and modality-specific information on the final predictions.
Approach:	They propose a Dual-oriented Disentangled Network with Counterfactual Intervention model that decouples semantics-oriented and modality-oriented representations and a Counterfective Intervention Module that applies causal inference to understand causal effects by injecting confounders.
Outcome:	The proposed model overcomes key limitations in existing systems by effectively disentangling and utilizing modality-specific and multimodal semantic information.

How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning (2023.emnlp-main)

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Hang Chen, Xinyu Yang, Jing Luo, Wenjing Zhu

Representation Learning

Challenge:	Existing models excel at capturing semantic correlations within utterance embeddings but fail to determine specific causal relationships.
Approach:	They propose to incorporate i.i.d. noise terms into conversation process to build a structural causal model . they propose to use unstructured conversation data to facilitate deep learning .
Outcome:	The proposed approach can be implemented in unstructured conversation data and a synthetic dataset that includes i.i.d. noise.

Improving Robustness of Language Models from a Geometry-aware Perspective (2022.findings-acl)

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Bin Zhu, Zhaoquan Gu, Le Wang, Jinyin Chen, Qi Xuan

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

Challenge:	Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness.
Approach:	They propose friendly adversarial data augmentation and geometry-aware adversarial training to achieve stronger robustness using fewer search steps.
Outcome:	The proposed method can obtain stronger robustness using fewer steps than existing methods.

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Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)

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He Zhu, Guanhua Chen, Wenjia Zhang

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency.
Approach:	They propose a specialized AI agent framework tailored for urban and spatial planning that integrates a customized local database retrieval system and domain-specific knowledge activation capabilities.
Outcome:	Empirical tests show that PlanGPT framework significantly improves planning efficiency . it integrates a customized database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms.

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition (2020.emnlp-main)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee

Faithful Inference Chains Extraction for Fact Verification over Multi-view Heterogeneous Graph with Causal Intervention (2025.coling-main)

Challenge:	Existing methods to augment pre-trained language models with disease knowledge are lacking.
Approach:	They propose a method to augment BERT-like pre-trained language models with disease knowledge.
Outcome:	The proposed method improves on a suite of BERT models over three tasks.

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Daoqi Chen, Yaxin Li, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng

RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)

Challenge:	Existing methods for fact verification do not extract faithful inference chains due to the diversity of relation paths.
Approach:	They propose a multi-view heterogeneous Graph with causal intervention to extract evidence graphs from the knowledge graph.
Outcome:	The proposed model provides precise evidence graphs and achieves state-of-the-art performance on the public KG-based fact verification dataset FactKG.

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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu

SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)

Challenge:	Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization.
Approach:	They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training.
Outcome:	The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization.

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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen

Speech & Audio in NLP

Challenge:	a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens .
Approach:	They propose a timbre-controllable, end-to-end voice interaction system with single-stage training.
Outcome:	The proposed system outperforms previous models on 4 GPUs with limited data.

Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)

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Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps (2020.acl-main)

Challenge:	Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles.
Approach:	They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments .
Outcome:	The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well .

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Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha

A Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy (2025.emnlp-industry)

Challenge:	Existing state-of-the-art VLN agents do not generalize well for long navigation tasks.
Approach:	They propose a VLN agent that is learned to navigate by decomposing long instructions into shorter ones and completing them sequentially.
Outcome:	The proposed agent can follow long instructions better than existing ones, but it does not generalize well.

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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Jiansong Chen, Ke Zeng, Xunliang Cai

MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)

Challenge:	Existing methods for abnormal event detection face two predominant limitations . existing methods rely on specialized small models and are limited by performance bottlenecks .
Approach:	They propose a framework that leverages the advanced reasoning capabilities of large language models for abnormal event detection.
Outcome:	The proposed framework achieves the highest F1 score and an average improvement of 9.59% in OOD transfer tests.

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Shuaijie She, Wei Zou, Shujian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, Jiajun Chen

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

Challenge:	Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data .
Approach:	They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language.
Outcome:	The proposed framework improves multilingual reasoning across languages on three benchmarks.

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots (2025.naacl-long)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Yu-Chung Hsiao, Fedir Zubach, Gilles Baechler, Srinivas Sunkara, Victor Carbune, Jason Lin, Maria Wang, Yun Zhu, Jindong Chen

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Challenge:	Existing screen datasets focus on low-level structural and component understanding or on a much higher-level composite task such as navigation and task completion for autonomous agents.
Approach:	They propose to annotate 86k question-answer pairs over the RICO dataset to benchmark screen content understanding.
Outcome:	The proposed dataset covers full answers, short answer phrases, and corresponding UI contents with bounding boxes, enabling four subtasks to address various application scenarios.

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

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Yanbin Zhao, Lu Chen, Zhi Chen, Ruisheng Cao, Su Zhu, Kai Yu

Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)

Challenge:	Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data.
Approach:	They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs.
Outcome:	The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language.

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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo

I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)

Challenge:	Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity.
Approach:	They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability.
Outcome:	The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability.

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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-Augmented Code Generation (2026.findings-acl)

Challenge:	Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts.
Approach:	They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning .
Outcome:	Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics.

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Qiming Zhu, Jialun Cao, Xuanang Chen, Weili Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung

Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence (2023.emnlp-main)

Challenge:	Current research on large language models with retrieval-augmented code generation (RACG) has focused on single-language settings, leaving their cross-lingual effectiveness underexplored.
Approach:	They construct a dataset covering 13 PLs with nearly 14K instances to study cross-lingual code knowledge transfer in RACG.
Outcome:	The proposed model shows unequal cross-lingual knowledge transfer even with direct injection and shows limited reliance on natural language information embedded in code when equipped with a code-specific retriever.

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Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen, Yuexian Zou

DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding (2026.acl-long)

Challenge:	Existing models rely on annotated training data, limiting their scalability to low-resource languages.
Approach:	They propose a method termed SoGo for zero-shot cross-lingual SLU that uses keywords as substitution options to extract keywords and a token-level alignment strategy to ensure grammatical coherence.
Outcome:	The proposed method improves zero-shot cross-lingual SLU across nine languages on MultiATIS++.

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Dawei Zhu, Rui Meng, Jiefeng Chen, Sujian Li, Tomas Pfister, Jinsung Yoon

Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Existing approaches to localizing evidence from long visual documents fail on a fundamental challenge: evidence localization.
Approach:	They propose a tool-augmented multi-agent framework that “zooms in” on evidence like a lens.
Outcome:	The proposed framework achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts.

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Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau

A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)

Challenge:	GraphRAG framework is designed to enhance LLMs in generating evidence-based medical responses.
Approach:	They propose a graph-based Retrieval-augmented generation framework to enhance LLMs in generating evidence-based medical responses.
Outcome:	The proposed framework outperforms state-of-the-art models on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen

FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)

Challenge:	Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages .
Approach:	They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss.
Outcome:	Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment.

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Jian Chen, Peilin Zhou, Yining Hua, Loh Xin, Kehui Chen, Ziyuan Li, Bing Zhu, Junwei Liang

CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)

Challenge:	Existing financial question answering datasets lack scope diversity and question complexity.
Approach:	They propose to use a dataset for long-form question answering in finance to evaluate QA systems.
Outcome:	The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.

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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu

Challenge:	CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation.
Approach:	They propose a LLM-based agent framework dedicated to repo-level compilation.
Outcome:	The proposed method significantly improves compilation success rate, ranging from 10% to 71%.

Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023.emnlp-main)

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Siyuan Chen, Zhiling Zhang, Mengyue Wu, Kenny Zhu

Explainability & Interpretability in NLP

Challenge:	Existing mental disease detection methods are not backed by domain knowledge and thus fail to produce interpretable results.
Approach:	They propose a framework that can learn the shared clues of all diseases while also capturing the specificity of each single disease.
Outcome:	Experiments on the detection of 7 diseases show that the proposed model can boost detection performance by more than 10%, especially in relatively rare classes.

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)

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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth

AUTOGEN STUDIO: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems (2024.emnlp-demo)

Challenge:	Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive.
Approach:	They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C.
Outcome:	The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness.

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Victor Dibia, Jingya Chen, Gagan Bansal, Suff Syed, Adam Fourney, Erkang Zhu, Chi Wang, Saleema Amershi

Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)

Challenge:	Multi-agent systems are emerging as effective pattern for solving long-running, complex tasks in numerous do- mains.
Approach:	They propose a no-code developer tool for rapidly prototyping, debugging, and evaluating multi-agent work flows built upon the AUTOGEN framework.
Outcome:	The proposed tool provides an intuitive drag-and-drop UI for agent workflow specification, interactive evaluation and debugging of workflows, and a gallery of reusable agent components.

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Chong Zhang, Ya Guo, Yi Tu, Huan Chen, Jinyang Tang, Huijia Zhu, Qi Zhang, Tao Gui

Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees (2025.emnlp-main)

Challenge:	Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs).
Approach:	They propose a method to predict token sequences within visually-rich documents by a simple prediction head.
Outcome:	The proposed method can be used to predict token mentions as token sequences within documents.

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Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang, Hongkuan Zhou, Jiaoyan Chen, Evgeny Kharlamov, Steffen Staab

How Many Demonstrations Do You Need for In-context Learning? (2023.findings-emnlp)

Challenge:	Existing methods produce only point estimates, without quantifying predictive uncertainty—limiting their reliability in high-stakes applications where understanding confidence in predictions is crucial.
Approach:	They propose a framework that generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence.
Outcome:	The proposed framework generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence.

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Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou

Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation (2021.findings-emnlp)

Challenge:	Large language models (LLMs) are capable of complex reasoning when given a few input-output demos.
Approach:	They use fewer input-output demos for each test query to study ICL . they do not observe significant degradation when using only one randomly chosen demo .
Outcome:	The proposed model outperforms multi-demo models on the tasks in 2022.

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Qingqing Zhu, Xiuying Chen, Pengfei Wu, JunFei Liu, Dongyan Zhao

Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)

Challenge:	Existing studies have shown that curriculum learning facilitates dialogue generation tasks while knowledge distillation can yield significant performance boosts for student models.
Approach:	They propose a combination of curriculum learning and knowledge distillation for dialogue generation models . they cluster training cases according to their complexity and employ an adversarial training strategy .
Outcome:	The proposed model improves compared with baselines.

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Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng, Zeyang Wang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

HyperText: Endowing FastText with Hyperbolic Geometry (2020.findings-emnlp)

Challenge:	Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard.
Approach:	They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system.
Outcome:	The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.

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Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang, Xiao Chen, Qun Liu

Quantifying Semantic Emergence in Language Models (2025.acl-long)

Challenge:	Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.
Approach:	They propose a model that uses hyperbolic geometry to model tree-like hierarchies in natural language sentences by embedding words or ngrams in hyperbolical space.
Outcome:	Empirically, the proposed model outperforms FastText on a range of text classification tasks with much reduced parameters.

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Hang Chen, Xinyu Yang, Jiaying Zhu, Wenya Wang

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)

Challenge:	Existing evaluation methods for large language models (LLMs) focus on coarse-grained text, not providing interpretations for the behavior of finergrained tokens.
Approach:	They propose a quantitative metric to measure large language models’ ability to extract semantics from input tokens.
Outcome:	The proposed metric compares the entropy reduction observed for a sequence of tokens and individual tokens.

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Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin, Ruifeng Xu

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)

Challenge:	Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge.
Approach:	They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases.
Outcome:	The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant.

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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu

Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (2020.findings-emnlp)

Challenge:	Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset.
Approach:	They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR).
Outcome:	The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations.

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Jia-Chen Gu, Zhenhua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Experimental results show that FIRE outperforms previous methods for building knowledge-grounded retrieval-based chatbots . a method called Filtering before iteratively referring is used to ground a conversation on background knowledge .
Approach:	They propose a method for grounding conversation on background knowledge . they use context filter and knowledge filter to make context and knowledge aware . experimental results show that FIRE outperforms previous methods .
Outcome:	The proposed method outperforms previous methods on two datasets.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling (2026.acl-long)

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

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Yuxuan Jiang, Zehua Chen, Zeqian Ju, Yusheng Dai, Weibei Dou, Jun Zhu

Speech & Audio in NLP

Challenge:	Recent efforts on text-to-audio generation are exploring fine-grained controllability . however, their performance at scale is limited due to data scarcity .
Approach:	They propose a multi-task learning problem for high-controllability text-to-audio generation . they propose scalable diffusion transformers that augment condition information in sequence .
Outcome:	The proposed method outperforms existing methods on objective and subjective evaluations.

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

From Technology to Market. Bilingual Corpus on the Evaluation of Technology Opportunity Discovery (2024.lrec-main)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Amir Hazem, Kazuyuki Motohashi, Chen Zhu

Towards an On-device Agent for Text Rewriting (2024.findings-naacl)

Challenge:	a large variety of TOD approaches have been proposed to explore emerging technologies and diversify existing products and services.
Approach:	They propose to use a technology-market corpus in English and Japanese to construct a market space using a BERT model.
Outcome:	The proposed method is based on a fine-tuned BERT model for linking technology to the market.

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Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng

MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale (2025.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities.
Approach:	They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection.
Outcome:	The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark.

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Jiawei Guo, Tianyu Zheng, Yizhi Li, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Graham Neubig, Wenhu Chen, Xiang Yue

KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)

Challenge:	Current instruction-tuning datasets focus on simplistic visual question answering tasks, and provide phrase-level answers without any intermediate rationales.
Approach:	They propose to use open-source multimodal large language models to train MLLMs on a dataset with 12M instruction-response pairs to elicit CoT reasoning.
Outcome:	The proposed model achieves state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro, and MuirBench, and gains improvements of up to 4% on non-reasoning-based benchmarks.

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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen

Semantic Parsing with Dual Learning (P19-1)

Challenge:	Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings.
Approach:	They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method.
Outcome:	The proposed framework can be used in few-shot settings and reduces data requirements.

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Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu

OSCBench: Benchmarking Object State Change in Text-to-Video Generation (2026.acl-long)

Challenge:	Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding.
Approach:	They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game.
Outcome:	The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset.

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Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen

PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)

Challenge:	Existing benchmarks focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding unexplored.
Approach:	They introduce a benchmark specifically designed to assess OSC performance in T2V models.
Outcome:	The proposed benchmark assesses the performance of open-source and proprietary T2V models on object state change (OSC) in the context of novel and compositional scenarios.

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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin

Explainability & Interpretability in NLP

Challenge:	Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management.
Approach:	They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory.
Outcome:	The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models.

Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)

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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)

Challenge:	Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader.
Approach:	They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers.
Outcome:	The proposed method improves the quality of evidence passages under zero-shot scenarios.

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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)

Challenge:	Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans.
Approach:	They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context.
Outcome:	The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning.

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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao

CTC-based Non-autoregressive Speech Translation (2023.acl-long)

Challenge:	Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding.
Approach:	They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language.
Outcome:	The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language.

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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu

SafeMT: Multi-turn Safety for Multimodal Language Models (2026.acl-long)

Challenge:	End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency.
Approach:	They develop a model that uses connectionist temporal classification to predict the source and target texts.
Outcome:	The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67.

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Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min Chan, Boyuan Chen, Yaodong Yang, Sirui Han, Yike Guo

Developing Prefix-Tuning Models for Hierarchical Text Classification (2022.emnlp-industry)

Challenge:	Multi-turn dialogues pose a greater risk than single prompts, but existing safety benchmarks do not account for this situation.
Approach:	They propose a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images.
Outcome:	The proposed model reduces multi-turn Attack Success Rate (ASR) compared to existing guard models.

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Lei Chen, Houwei Chou, Xiaodan Zhu

BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)

Challenge:	Hierarchical text classification (HTC) is a key task in many industrial applications. Pre-trained Language Models (PLMs) have become dominant for most natural language processing (NLP) tasks.
Approach:	They investigate how prefix tuning can improve hierarchical text classification . prefix-tuning model only needs less than 1% of parameters to achieve performance .
Outcome:	The proposed model can achieve comparable performance to regular full fine-tuning.

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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li

Low-Resource NLP

Challenge:	Existing information extraction (IE) tasks rely on in-context learning with large language models.
Approach:	They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates.
Outcome:	The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1.

A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit (2025.findings-acl)

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Zafarullah Mahmood, Soliman Ali, Jiading Zhu, Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Jodi Wolff, Osnat C. Melamed, Nadia Minian, Marta Maslej, Carolynne Cooper, Matt Ratto, Peter Selby, Jonathan Rose

Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping (2024.findings-emnlp)

Challenge:	Large language models (LLMs) are being used to provide automated talk therapy . however, it is crucial to know if they would be effective and adhere to known standards.
Approach:	They propose to use large language models to automate talk therapy with a focus on tobacco addiction.
Outcome:	The proposed chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors.

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Wenhao Zhu, Sizhe Liu, Shujian Huang, Shuaijie She, Chris Wendler, Jiajun Chen

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

Challenge:	Decoding by contrasting layers (DoLa) is designed to improve the generation quality of large language models (LLMs) however, this approach does not work well on non-English tasks.
Approach:	They propose a contrastive decoding algorithm that uses amateur logits to contrast with the output of an expert model's early exit logits.
Outcome:	The proposed method outperforms baselines and significantly improves chain-of-thought reasoning accuracy across 11 languages.

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Towards Unified Spoken Language Understanding Decoding via Label-aware Compact Linguistics Representations (2023.findings-acl)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen, Yuexian Zou

Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)

Challenge:	Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks.
Approach:	They propose a method that leverages label embeddings to jointly guide the decoding process.
Outcome:	The proposed method outperforms existing methods on two single- and multi-intent SLU benchmarks and can be incorporated into existing models.

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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu

Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)

Challenge:	Empirical evidence shows that our proposed method improves performance across seven downstream tasks.
Approach:	They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data.
Outcome:	The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks.

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Su Zhu, Jieyu Li, Lu Chen, Kai Yu

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

Challenge:	Existing methods to track dialogue state are limited due to data sparsity and long dialogues.
Approach:	They propose to use the previous dialogue state and current dialogue utterance as input for DST.
Outcome:	The proposed approach outperforms existing methods and improves existing ones.

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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

A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification (2023.acl-short)

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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Rohan Bhambhoria, Lei Chen, Xiaodan Zhu

Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)

Challenge:	Large language models have been able to perform well on benchmarks, but they are often not able address real-world challenges.
Approach:	They propose to refactor hierarchical tasks into a more indicative long-tail prediction task.
Outcome:	The proposed method does not require parameter updates and achieves strong performance across multiple datasets.

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Yanshuo Wang, Yanghao Zhou, Yukang Lin, Haoxing Chen, Jin Zhang, Wentao Zhu, Jie Hong, Xuesong Li

AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition (2024.naacl-long)

Challenge:	Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts.
Approach:	They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios.
Outcome:	Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines.

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Zhaorun Chen, Zhuokai Zhao, Zhihong Zhu, Ruiqi Zhang, Xiang Li, Bhiksha Raj, Huaxiu Yao

What are the Generator Preferences for End-to-end Task-Oriented Dialog System? (2024.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet relying on extensive manual labeling to provide procedural feedback remains a significant impediment.
Approach:	They propose a self-supervised framework that decomposes complex problems into manageable subquestions with a controllable granularity switch and sequentially applies reinforcement learning to iteratively improve the subquest solver.
Outcome:	The proposed framework improves performance on mathematical and commonsense reasoning tasks over SOTA.

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Wanshi Xu, Xianwei Zhuang, Zhanpeng Chen, Zhihong Zhu, Xuxin Cheng, Yuexian Zou

BOLT: Benchmarking Open-World Learning for Text Classification (2026.findings-acl)

Challenge:	Existing methods to improve the accuracy of entity retrieval are not effective.
Approach:	They propose a framework that improves the performance of task-oriented dialogue systems by obtaining fine-grained matching information between contexts and entities and extracting the entity attribute shift matrix as preference signals.
Outcome:	The proposed framework outperforms existing methods and improves the quality of the dialogue.

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Chuan Qin, Xi Chen, Jinpeng Li, Hengshu Zhu

OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)

Challenge:	Existing benchmarks focus on out-of-distribution (OOD) detection while overlooking broader challenges such as the discovery of novel categories.
Approach:	They propose a unified Benchmark and evaluation toolkit supporting Open-world learning for text classification.
Outcome:	The proposed methods overfit training distributions and struggle to generalize to unseen classes.

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Zhi Chen, Yuncong Liu, Lu Chen, Su Zhu, Mengyue Wu, Kai Yu

DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)

Challenge:	Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue.
Approach:	They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks.
Outcome:	The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.

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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng

Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards (2026.acl-long)

Challenge:	Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages.
Approach:	They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation .
Outcome:	The proposed model outperforms existing state-of-the-art methods on two benchmark datasets.

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Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan, Hengjie Fang, Qingfu Zhu, Wanxiang Che

Learning from Diverse Reasoning Paths with Routing and Collaboration (2025.emnlp-main)

Challenge:	Existing efforts to generate static visualizations focus on static charts and interactive dashboards.
Approach:	They propose a dashboard2code task that requires a model to explore an interactive dashboard, acquire feedback from its own interactions and generate code that reproduces the target dashboard.
Outcome:	The proposed task is based on 180 carefully designed and manually verified dashboard–code pairs spanning three difficulty levels and covering eight common real-world interaction patterns.

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Zhenyu Lei, Zhen Tan, Song Wang, Yaochen Zhu, Zihan Chen, Yushun Dong, Jundong Li

Explanation in the Era of Large Language Models (2024.naacl-tutorials)

Challenge:	Recent studies suggest that the reasoning abilities of large language models (LLMs) grows with model size and pre-training data.
Approach:	They propose to combine quality filtering, conditional routing, and cooperative peer teaching to transfer knowledge from powerful teacher models to compact and transparent students.
Outcome:	Experiments show that QR-Distill is superior to traditional methods.

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Zining Zhu, Hanjie Chen, Xi Ye, Qing Lyu, Chenhao Tan, Ana Marasovic, Sarah Wiegreffe

Explainability & Interpretability in NLP

Challenge:	Explanation has long been a part of communication, where humans use language to elucidate each other and transmit information about mechanisms of events.
Approach:	They review the opportunities and challenges of explanations in the era of large language models and examine how they can be used to generate explanations.
Outcome:	The proposed methods are based on the models of large language models (LLMs) and their opaque nature.

HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models (2025.findings-acl)

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Xinyan Jiang, Hang Ye, Yongxin Zhu, Xiaoying Zheng, Zikang Chen, Jun Gong

CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)

Challenge:	Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect.
Approach:	They propose a method that selects attention heads crucial to the model's prediction as inducing heads and induces hallucinations by dispersing attention of these inducers.
Outcome:	The proposed method significantly improves performance on tasks requiring contextual faithfulness, reading comprehension, and question answering.

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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li

Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models (2024.findings-acl)

Challenge:	Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces.
Approach:	They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings.
Outcome:	The citycube benchmark examines the performance of current vision-language models in urban environments.

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Yuchen Hu, Chen Chen, Chengwei Qin, Qiushi Zhu, EngSiong Chng, Ruizhe Li

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR).
Approach:	They propose a multimodal LLM to receive source speech as extra input and reformat it as a cloze test with logits calibration to remove input information redundancy and simplify GER with clear instructions.
Outcome:	The proposed model improves on 9 popular ASR datasets and is faster than vanilla GER.

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

Multimodal Procedural Planning via Dual Text-Image Prompting (2024.findings-emnlp)

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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Yujie Lu, Pan Lu, Zhiyu Chen, Wanrong Zhu, Xin Wang, William Yang Wang

Generative Reward Modeling via Synthetic Criteria Preference Learning (2025.acl-long)

Challenge:	Embodied agents have demonstrated performance in following instructions informed by texts and images . however, the potential of models providing useful guidelines for humans to complete tasks remains underexplored .
Approach:	They propose a multimodal procedural planning task that generates paired text-image plans . this task provides more complementary and informative guidance than unimodal plans a . authors propose modality prompting methods that leverage zero-shot reasoning ability .
Outcome:	The proposed method improves the interaction in dual modalities and provides more information than unimodal plans.

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Xiaobo Liang, Haoke Zhang, Juntao Li, Kehai Chen, Qiaoming Zhu, Min Zhang

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

Challenge:	Generative Reward Models (GenRMs) leverage synthesized Chains of Thought (CoT) but this approach introduces risks of overoptimization due to the inability to guarantee the correctness of the CoTs.
Approach:	They propose a criteria-based preference tree for GenRMs that uses chain of thought to generate reasoning . they show that synthesized data can be learned using a long CoT format .
Outcome:	The proposed model shows significant improvements over baselines on multiple human preference benchmarks.

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Chen Xu, Bojie Hu, Yanyang Li, Yuhao Zhang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset (2025.findings-acl)

Challenge:	End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it.
Approach:	They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency .
Outcome:	The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks.

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Jie Zhu, Junhui Li, Yalong Wen, Xiandong Li, Lifan Guo, Feng Chen

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Existing financial benchmarks rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings.
Approach:	They propose a multilingual, multi-sector, and multi-task dataset called MFinMeeting that supports English, Chinese, and Japanese .
Outcome:	The proposed benchmark supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts.

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Unsupervised Hierarchical Topic Modeling via Anchor Word Clustering and Path Guidance (2024.findings-emnlp)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

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Jiyuan Liu, Hegang Chen, Chunjiang Zhu, Yanghui Rao

UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)

Challenge:	Existing hierarchical topic models often ignore the role of anchor words that guide text generation.
Approach:	They propose to use a clustering algorithm to detect anchor words that are highly consistent with every topic and add a causal path to the popular Variational Auto-Encoder framework.
Outcome:	The proposed model outperforms state-of-the-art methods on three datasets.

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Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang

Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)

Challenge:	a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets.
Approach:	They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task .
Outcome:	The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation.

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Lu Chen, Yanbin Zhao, Boer Lyu, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)

Challenge:	Chinese word segmentation can be erroneous, ambiguous or inconsistent, causing performance problems.
Approach:	They propose a sentence matching framework that uses paired word lattices as input instead of a character sequence.
Outcome:	The proposed framework outperforms the state-of-the-art short text matching models on two Chinese datasets.

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Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He

When Backdoors Go Beyond Triggers: Semantic Drift in Diffusion Models Under Encoder Attacks (2026.findings-acl)

Challenge:	Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks.
Approach:	They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese.
Outcome:	The proposed model improves the few-shot table understanding in Chinese.

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Shenyang Chen, Liuwan Zhu

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)

Challenge:	Existing studies on text-to-image models only measure trigger activation and visual fidelity, but backdoors can induce semantic corruption.
Approach:	They propose a framework that measures internal embedding drift and downstream functional misalignment.
Outcome:	The proposed model is vulnerable to backdoor attacks, the authors show . they show that the model is optimized to generate the desired style for benign prompts .

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Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu

kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)

Challenge:	Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics.
Approach:	They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval.
Outcome:	The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power.

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Wenhao Zhu, Qianfeng Zhao, Yunzhe Lv, Shujian Huang, Siheng Zhao, Sizhe Liu, Jiajun Chen

Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)

Challenge:	kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants .
Approach:	They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks .
Outcome:	The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization.

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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang

MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)

Challenge:	Large Language Models are a powerful tool for medical research, but the data is a bottleneck.
Approach:	They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models.
Outcome:	The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets.

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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie

Incorporating Temporal Coherence to Cross-Document Event Coreference Resolution (2026.acl-long)

Challenge:	telemedicine is a medical practice that provides patient care remotely using video conferencing tools.
Approach:	They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance .
Outcome:	The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues.

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Xinyu Chen, Peifeng Li, Qiaoming Zhu

RQT: Hierarchical Residual Quantization for Multi-Model Compression (2025.findings-acl)

Challenge:	Existing approaches focus on enhancing semantic coherence between event mentions, but they overlook the critical aspect of temporal coherency.
Approach:	They propose a Temporal Cohorence-driven event coreference framework that explicitly models temporal constraints by constructing a temporal event graph and a GNN to resolve conflicts.
Outcome:	Experiments on the ECB+, GVC, WEC, and ECb+META datasets show that CohTP outperforms state-of-the-art methods.

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Chen Tianqi, Peisong Wang, Weixiang Xu, Zeyu Zhu, Jian Cheng

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering (2026.findings-acl)

Challenge:	Existing methods for decomposing fine-tuned LLMs are sensitive to the magnitude of delta values.
Approach:	They propose a hierarchical quantization framework that shares low-bit integer weights across similar models.
Outcome:	The proposed framework achieves an average accuracy degradation of approximately 3% on fine-tuned models across mathematics, coding, chatbot, and Chinese LLMs.

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Weikang Zhang, Zimo Zhu, Zhichuan Yang, Chen Huang, Wenqiang Lei, See-Kiong Ng

Hey, That’s My Data! Token-Only Dataset Inference in Large Language Models (2026.findings-acl)

Challenge:	Existing methods for training patients with cognitive impairment rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across domains and severity levels.
Approach:	They propose to use steering vectors from contrastive pairs of instructions and responses to capture domain-specific features and introduce a Stochastic Token Modulation mechanism to regulate the intervention probability.
Outcome:	The proposed model outperforms baselines in clinical authenticity and severity controllability while remaining open-source.

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Chen Xiong, Zihao Wang, Rui Zhu, Tsung-Yi Ho, Pin-Yu Chen, Jingwei Xiong, Haixu Tang

What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge (2026.eacl-long)

Challenge:	Existing dataset inference methods require logit access, but many modern LLMs restrict such access.
Approach:	They propose a token-only dataset inference framework that allows models to overwrite prior knowledge when trained on new data.
Outcome:	The proposed framework overwrites prior knowledge when trained on new data.

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Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)

Challenge:	Existing evaluation metrics and lenient answer matching criteria obscure meaningful comparisons.
Approach:	They propose a general method for constructing benchmarks and a method to assess KG-RAG methods under incomplete knowledge.
Outcome:	The proposed method systematically assesses KG-RAG methods under incomplete knowledge.

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Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

Low-Resource NLP

Challenge:	Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles.
Approach:	They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words.
Outcome:	The proposed generator significantly improves performance and provides explainable word alignments.

MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)

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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang

Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)

Challenge:	Existing work on controllable summarization with mixed attributes lacks designated annotations.
Approach:	They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources .
Outcome:	The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks .

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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen

Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)

Challenge:	Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles .
Approach:	They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning .
Outcome:	The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks.

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Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, Xiaodan Zhu

Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)

Challenge:	Existing methods for fact verification based on structured data are challenging and require further study.
Approach:	They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models.
Outcome:	The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models .

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Xinyu Chen, Sheng Xu, Peifeng Li, Qiaoming Zhu

Duwak: Dual Watermarks in Large Language Models (2024.findings-acl)

Challenge:	Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines.
Approach:	They propose to use discourse rhetorical structure constructor to construct tree structures to represent documents and a multi-layer perceptron to capture similarities of event mention pairs.
Outcome:	The proposed model outperforms baselines and achieves competitive performance with the start-of-the-art baselines.

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Chaoyi Zhu, Jeroen Galjaard, Pin-Yu Chen, Lydia Chen

Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)

Challenge:	Existing watermark techniques are effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality and semantics.
Approach:	They propose to embed dual secret patterns in token probability distribution and sampling schemes to enhance the efficiency of watermarking.
Outcome:	The proposed method achieves highest watermark quality at the lowest required token count for detection, up to 70% less than existing techniques, especially under post paraphrasing attacks.

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Hongcheng Liu, Yuhao Wang, Zhe Chen, Pingjie Wang, Zhiyuan Zhu, Yixuan Hou, Yanfeng Wang, Yu Wang

Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

Challenge:	Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning.
Approach:	They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities.
Outcome:	Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks.

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Xinnan Guo, Qian Zhu, Qiuhui Shi, Xuan Lin, Liubin Wang, DaqianLi DaqianLi, Yongrui Chen

Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)

Challenge:	Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach:	They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome:	The proposed method improves existing models and achieves state-of-the-art on three benchmarks.

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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024.findings-acl)

Challenge:	In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability.
Approach:	They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard.
Outcome:	The proposed methods significantly improve performance on six datasets.

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Linan Zhu, Xiangfan Chen, Xiaolei Guo, Chenwei Zhang, Zhechao Zhu, Zehai Zhou, Xiangjie Kong

Sentiment Analysis

Challenge:	Current studies on aspect-based sentiment analysis focus on essential content for model generation, ignoring the incorporation of various noise during training.
Approach:	They propose a grid noise-diffusion pinpoint network (GDP) model that incorporates three new modules to tackle generation instability.
Outcome:	The proposed model reduces the generation instability of model learning and outputs by incorporating Consistency Likelihood Learning and GDP-FOR.

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

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

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

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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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

A Semantic-based Method for Unsupervised Commonsense Question Answering (2021.acl-long)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, Minlie Huang

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)

Challenge:	Existing methods to score candidates without labeled task data are difficult to use . e.g., pre-trained language models can be easily affected by irrelevant factors .
Approach:	They propose a method that generates plausible answers with generative models and uses them to select the correct answer.
Outcome:	The proposed method achieves the best results in unsupervised situations.

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Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, Huajun Chen

Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)

Challenge:	Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities.
Approach:	They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier.
Outcome:	The proposed method performs well in the current distant supervision dataset.

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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou

GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)

Challenge:	Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format.
Approach:	They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision.
Outcome:	The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges.

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Xi Chen, Chuan Qin, Jinpeng Li, Shasha Hu, Chao Wang, Hengshu Zhu, Hui Xiong

Challenge:	Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones.
Approach:	They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM.
Outcome:	The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task.

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

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Lei Shu, Nevan Wichers, Liangchen Luo, Yun Zhu, Yinxiao Liu, Jindong Chen, Lei Meng

EDSD: Entropy-Driven Design for Faster Speculative Decoding (2026.acl-long)

Challenge:	Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks.
Approach:	They propose a method that leverages large language models to integrate insights from various assistant evaluators.
Outcome:	The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods.

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Longkai Cheng, Ximing Wang, Jiangcai Zhu, Kailai Shao, Chao Chen, Haixiang Hu

CoV: Chain-of-View Prompting for Spatial Reasoning (2026.findings-acl)

Challenge:	Existing methods for speculative decoding incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding.
Approach:	They propose an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design.
Outcome:	Experiments on seven large language models show that EDSD improves training efficiency by 24.8% and increases acceptance length by 4.0% compared to state-of-the-art methods.

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Haoyu Zhao, Akide Liu, Zeyu Zhang, Weijie Wang, Feng Chen, Ruihan Zhu, Gholamreza Haffari, Bohan Zhuang

Low-Resource NLP

Challenge:	Embodied question answering requires collecting context that is distributed across multiple viewpoints . most recent vision–language models (VLMs) are constrained to a fixed and finite set of input views .
Approach:	They propose a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process.
Outcome:	The proposed framework improves LLM-Match performance by 11.98% on four mainstream VLMs.

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)

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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng

Challenge:	Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data.
Approach:	They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data.
Outcome:	The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard.

QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning (2024.emnlp-industry)

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Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh Tahaei, Hyock Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning (2026.findings-acl)

Challenge:	Existing methods to fine tune large language models require huge memory, limiting the choice to acquire Larger models.
Approach:	They propose an efficient quantization approach for dynamic low-rank adaptation that can efficiently fine tune large language models on a set of pre-defined LoRA ranks.
Outcome:	The proposed method outperforms QLoRA and is competitive to QLouRA and outperformed when employing its optimal rank.

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Fanqi Kong, Weiqin Zu, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng

LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)

Challenge:	Existing benchmarks for Multimodal Large Language Models (MLLMs) have been lacking due to the rich nature of social interaction.
Approach:	They propose a video benchmark to evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction.
Outcome:	The proposed benchmarks evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction tasks.

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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng

Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)

Challenge:	LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions.
Approach:	They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results.
Outcome:	The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs.

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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang

EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge (2025.acl-long)

Challenge:	Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking.
Approach:	They propose an iterative adversarial training method that incorporates three key innovations to address these challenges.
Outcome:	Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%.

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Zhiyuan Zhu, Yusheng Liao, Zhe Chen, Yuhao Wang, Yunfeng Guan, Yanfeng Wang, Yu Wang

Challenge:	Existing studies have explored how LLMs perceive time, but they often overlook the critical aspect of knowledge utilization.
Approach:	They propose a benchmark that evaluates temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness and reasoning.
Outcome:	EvolveBench measures temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness, Understanding and reasoning.

DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)

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MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, Juanzi Li

MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis (2026.acl-long)

Challenge:	Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets.
Approach:	They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments.
Outcome:	The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings.

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Wenting Chen, Guolin Huang, Wenxuan Wang, Zhongrui Zhu

Q-Mamba: Towards more efficient Mamba models via post-training quantization (2025.findings-acl)

Challenge:	Existing medical benchmarks fail to detect the Einstellung Effect in clinical diagnosis . Existing models exhibit the Einstellung effect, relying on statistical shortcuts rather than logical reasoning.
Approach:	They propose a counterfactual benchmark that uses statistical shortcuts to diagnose patients . they propose CGME-based system that iteratively refines reasoning paths .
Outcome:	The proposed model achieves high baseline accuracy but severe bias trap rates . iteratively refines reasoning paths in an exemplar base and consolidates disease-specific knowledge into illness graphs.

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Chen Tianqi, Yuanteng Chen, Peisong Wang, Weixiang Xu, Zeyu Zhu, Jian Cheng

Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice? (2024.emnlp-main)

Challenge:	Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches.
Approach:	They propose a decoupled scale quantization scheme to mitigate outliers in states and channels by applying separate quantization scales.
Outcome:	The proposed method reduces memory consumption by 50% across various quantization settings, model sizes, and generation and zero-shot tasks.

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Dawei Zhu, Pinzhen Chen, Miaoran Zhang, Barry Haddow, Xiaoyu Shen, Dietrich Klakow

DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment (2024.findings-emnlp)

Challenge:	Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data.
Approach:	They revisit the importance of large language models for translation by fine-tuning on 32 parallel sentences.
Outcome:	The proposed model can be fine-tuned on as few as 32 parallel sentences . however, the choice of direction is critical to avoid misinterpretation, the authors say .

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Liang Zhu, Feiteng Fang, Yuelin Bai, Longze Chen, Zhexiang Zhang, Minghuan Tan, Min Yang

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

Challenge:	Experimental results show that the methods enhanced by DEFT outperform the original methods in both alignment capability and generalization ability, with significantly reduced training time.
Approach:	They propose a distribution-based alignment framework that integrates data filtering and distributional guidance to improve alignment efficiency and generalization ability.
Outcome:	The proposed framework outperforms existing methods in alignment capability and generalization ability with significantly reduced training time.

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Wenhao Zhu, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	Neural machine translation models induce a non-smooth representation space, which harms its generalization results.
Approach:	They propose a framework to smooth the representation space by adjusting neighbor representations with a small number of new parameters.
Outcome:	The proposed framework outperforms the state-of-the-art kNN-MT system with average gains of 1.99 COMET and 1.0 BLEU on four benchmark datasets.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li

Relevance Is a Guiding Light: Relevance-aware Adaptive Learning for End-to-end Task-oriented Dialogue System (2024.emnlp-main)

Challenge:	Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored.
Approach:	They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation.
Outcome:	The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs.

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Zhanpeng Chen, Zhihong Zhu, Wanshi Xu, Xianwei Zhuang, Yuexian Zou

PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models (2025.emnlp-industry)

Challenge:	Existing approaches to training task-oriented dialogue systems struggle with the Distractive Attributes Problem (DAP) Existing methods struggle to deal with false but similar knowledge (hard negative entities)
Approach:	They propose a two-stage training framework that eliminates hard negatives step-by-step and aligns retrieval with generation.
Outcome:	The proposed method eliminates hard negatives step-by-step and aligns retrieval with generation.

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He Zhu, Junyou Su, Minxin Chen, Wen Wang, Yijie Deng, Guanhua Chen, Wenjia Zhang

Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)

Challenge:	Existing Vision-Language Models (VLMs) fail to analyze planning maps . specialized visual representations of land use zones, transportation networks, and development policies are needed to interpret complex planning maps.
Approach:	They propose a domain-specific VLM tailored for urban planning maps that employs three innovations: PlanAnno-V framework for high-quality VQA data synthesis, Critical Point Thinking (CPT) and PlanBench-V benchmark for systematic evaluation.
Outcome:	The new model outperforms general-purpose VLMs on planning map interpretation tasks.

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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang

TS-CLIP: Time Series Understanding by CLIP (2025.emnlp-main)

Challenge:	Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting.
Approach:	They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method.
Outcome:	The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters.

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Ziwen Chen, Xiaoyuan Zhang, Ming Zhu

When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025.findings-emnlp)

Challenge:	Contrastive Language–Image Pre-training (CLIP) has demonstrated remarkable success in aligning vision and language.
Approach:	They propose a synonym bank mechanism that generates synonym embeddings as alignment targets.
Outcome:	The proposed approach achieves state-of-the-art (SOTA) performance on 51 datasets.

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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, Xunliang Cai

EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs (2025.coling-main)

Challenge:	Large reasoning models (LRMs) incur excessive computational overhead due to redundant reasoning, especially on simple tasks.
Approach:	They propose an Adaptive Self-Recovery Reasoning framework that suppresses unnecessary reasoning and enables implicit recovery.
Outcome:	The proposed framework suppresses unnecessary reasoning and enables implicit recovery.

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Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, Weiguo Chen

Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)

Challenge:	Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLM or suffer from prohibitive computational costs due to tight coupling.
Approach:	They propose a framework that can strike a balance between performance and efficiency via an iterative paradigm.
Outcome:	The proposed framework can strike a balance between performance and efficiency via an iterative paradigm.

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Zhihong Zhu, Xuxin Cheng, Hao An, Zhichang Wang, Dongsheng Chen, Zhiqi Huang

Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)

Challenge:	Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods.
Approach:	They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs.
Outcome:	The proposed framework can be built by directly prompting LLMs to understand user needs without training data.

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Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Wenliang Chen

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

Challenge:	Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks.
Approach:	They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario.
Outcome:	The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities.

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024.emnlp-main)

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Yiqiao Jin, Qinlin Zhao, Yiyang Wang, Hao Chen, Kaijie Zhu, Yijia Xiao, Jindong Wang

Ethical NLP

Challenge:	Existing methods of peer review analysis do not address multivariate nature of the process, account for latent variables, and are constrained by privacy concerns due to the sensitive nature of data.
Approach:	They propose a large language model based peer review simulation framework which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue.
Outcome:	The proposed framework disentangles the impacts of multiple latent factors and addresses privacy concerns.

MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)

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Zhiqi Huang, Dongsheng Chen, Zhihong Zhu, Xuxin Cheng

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025.acl-long)

Challenge:	Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes.
Approach:	They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset.
Outcome:	The proposed framework improves on four datasets and four BERT-like backbone models.

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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)

Challenge:	Existing evaluations of Large Language Models (LLMs) focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user’s perspective.
Approach:	They propose a Chinese Comprehensive Constraints Following Benchmark for LLMs that compiles constraints from real-world instructions and constructs a systematic framework for constraint types.
Outcome:	The proposed framework integrates multi-dimensional assessment criteria with requirement prioritization, covering various perspectives of constraints, instructions, and requirement fulfillment.

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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao

Green & Sustainable NLP

Challenge:	Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
Approach:	They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory.
Outcome:	The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%.

Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)

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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying

Challenge:	Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks .
Approach:	They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication.
Outcome:	The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models.

Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)

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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui

Question Translation Training for Better Multilingual Reasoning (2024.findings-acl)

Challenge:	Existing models of layout reading order do not convey the complete reading order information in the layout.
Approach:	They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance .
Outcome:	The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization.

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Wenhao Zhu, Shujian Huang, Fei Yuan, Shuaijie She, Jiajun Chen, Alexandra Birch

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

Challenge:	Large language models have shown compelling performance on reasoning tasks but they tend to perform much worse in languages other than English.
Approach:	They propose to train a model to translate reasoning questions into English by fine tuning on X-English parallel question data.
Outcome:	The proposed approach improves on LLaMA2-13B on the MGSM and MSVAMP multilingual reasoning benchmarks.

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Weiran Chen, Xin Li, Jiaqi Su, Guiqian Zhu, Ying Li, Yi Ji, Chunping Liu

MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)

Challenge:	Existing methods for visual storytelling ignore latent topic information.
Approach:	They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story.
Outcome:	The proposed method outperforms most of the competing models across multiple evaluation metrics.

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Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen

FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)

Challenge:	Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems.
Approach:	They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains.
Outcome:	Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4.

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He Zhu, Yifan Ding, Yicheng Tao, Zhiwen Ruan, Yixia Li, Wenjia Zhang, Yun Chen, Guanhua Chen

CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2025.findings-emnlp)

Challenge:	Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data.
Approach:	They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.
Outcome:	The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.

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Xinze Wang, Chen Chen, Yinfei Yang, Hong-You Chen, Bowen Zhang, Aditya Pal, Xiangxin Zhu, Xianzhi Du

Green & Sustainable NLP

Challenge:	Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs.
Approach:	They propose an alternative training strategy that converts a dense CLIP model into a sparse MoE architecture.
Outcome:	The proposed training strategy outperforms dense models on COCO and Flickr30k benchmarks.

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)

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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng

Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023.emnlp-main)

Challenge:	a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing.
Approach:	They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity.
Outcome:	The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity.

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Yichun Liu, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng, Daoqi Chen, Yaxin Li

JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice (2026.acl-long)

Challenge:	Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents.
Approach:	They propose a logic constraint framework that uses bidirectional constraints to model rules by beta contribtion and reconstruct rule consistency loss by bidirectional constraint.
Outcome:	The proposed framework outperforms existing models in relation extraction performance and logical consistency.

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Ziang Chen, Guannan Li, Fanlin Ji, Yipeng Kang, Jiaqi Li, Muhan Zhang, Yangtao Zhang, Li Tianjiao, Jiannan Wang, Xin Guo, Song-Chun Zhu, Bin Ling

An Efficient Conversational Smart Compose System (2023.acl-demo)

Challenge:	Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice.
Approach:	They propose a vertical, depth-oriented, domain-specific benchmark to evaluate Large Language Models (LLMs) in Chinese civil litigation.
Outcome:	The proposed benchmarks show that large language models exhibit an "illusion of competence" the results highlight a critical gap between fluent linguistic output and judicial reliability .

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Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, Ning Ruan

Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)

Challenge:	a cloud-based smart compose system is designed to improve human-to-human conversation efficiency.
Approach:	They propose a cloud-based smart compose system to improve conversation efficiency . they propose heuristics to achieve the best trade-off between quality and latency .
Outcome:	The proposed system reduces latency without losing composing quality further.

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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu

AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)

Challenge:	Large language models (LLMs) are capable of performing tasks but are likely to be misused.
Approach:	They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model .
Outcome:	The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts .

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Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang

Weighted Contrastive Learning With False Negative Control to Help Long-tailed Product Classification (2023.acl-industry)

Challenge:	Large Audio-Language Models suffer from hallucinations, e.g., generating text not grounded in the audio input.
Approach:	They propose a framework to address hallucination problems in large audio-language models . they use a preference dataset to test the model's accuracy .
Outcome:	The proposed model outperforms the latest SOTA methods in terms of performance and generalization.

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Tianqi Wang, Lei Chen, Xiaodan Zhu, Younghun Lee, Jing Gao

Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)

Challenge:	Item categorization (IC) aims to classify a product into leaf nodes in a categorical taxonomy due to scarce supervision.
Approach:	They propose to use K-positive contrastive loss (KCL) to address IC task’s long-tail issue by re-weighting positive pairs in the KCL loss with a regularization that the sum of weights should be constrained to K+1 as close as possible.
Outcome:	The proposed method improves on the long-tail issue in the image classification task and when using text-based contrastive learning, it can be applied on the IC task.

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Bo Ouyang, Wenbing Huang, Runfa Chen, Zhixing Tan, Yang Liu, Maosong Sun, Jihong Zhu

RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking (2026.findings-acl)

Challenge:	Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training.
Approach:	They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation.
Outcome:	The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks.

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Jiaru Zou, Dongqi Fu, Sirui Chen, Xinrui He, Zihao Li, Yada Zhu, Jiawei Han, Jingrui He

Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition (2023.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) integrates knowledge from tables with an external knowledge base to improve the answer relevance and accuracy.
Approach:	They propose a table-corpora-aware RAG framework called T-RAG to integrate external knowledge into Large Language Models (LLMs) they then develop a multi-table question answering benchmark called MultiTableQA which spans 3 different task types, 57,193 tables, and 23,758 questions in total.
Outcome:	The proposed framework achieves state-of-the-art accuracy, recall, and runtime performance, with improvements of up to 9.4%.

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Yuchen Hu, Ruizhe Li, Chen Chen, Chengwei Qin, Qiu-Shi Zhu, Eng Siong Chng

Weize Chen, Hao Zhu, Xu Han, Zhiyuan Liu, Maosong Sun

Challenge:	Existing efforts to improve robustness of audio-visual speech recognition with visual information focus on audio modality . current approaches introduce noise adaptation techniques to improve reliability of AVSR task .
Approach:	They propose a visual-invariant modality to strengthen robustness of audio-visual speech recognition (AVSR) it can adapt to any testing noises without dependence on noisy training data, a.k.a., unsupervised noise adaptation.
Outcome:	The proposed method outperforms existing state-of-the-arts on visual speech recognition task under various noisy and clean conditions.

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SConU: Selective Conformal Uncertainty in Large Language Models (2025.acl-long)

Challenge:	a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks.
Approach:	They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs .
Outcome:	The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes.

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Zhiyuan Wang, Qingni Wang, Yue Zhang, Tianlong Chen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)

Challenge:	Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets.
Approach:	They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set.
Outcome:	The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions.

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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang

IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)

Challenge:	Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL).
Approach:	They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions.
Outcome:	The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies.

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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang

T⋆: Progressive Block Scaling for Masked Diffusion Language Models Through Trajectory Aware Reinforcement Learning (2026.acl-short)

Challenge:	Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck.
Approach:	They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent.
Outcome:	The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production.

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Hanchen Xia, Baoyou Chen, Yutang Ge, Guojiang Zhao, Siyu Zhu

Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)

Challenge:	Autoregressive (AR) modeling via next-token prediction dominates scaling practice and deployed systems.
Approach:	They propose a TraceRL-based curriculum for progressive block-size scaling in masked diffusion language models.
Outcome:	The proposed curriculum outperforms direct large-block TraceRL on two SDAR scales and three benchmarks and retains block-size-specific non-monotone updates while improving accuracy.

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)

Challenge:	Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets.
Approach:	They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data.
Outcome:	The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls.

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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie

DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning (2026.findings-acl)

Challenge:	Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling.
Approach:	They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement.
Outcome:	The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks.

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Xiwen Chen, Wenhui Zhu, Peijie Qiu, Xuanzhao Dong, Hao Wang, Haiyu Wu, Huayu Li, Aris Sotiras, Yalin Wang, Abolfazl Razi

RISE: Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answering (2025.findings-acl)

Challenge:	Existing methods for group-relative policy optimization rely on scalar correctness rewards that are often non-injective with respect to semantic content.
Approach:	They propose a framework that calibrates the reward signal using the semantic density of sampled groups.
Outcome:	The proposed framework outperforms strong baselines on five math benchmarks with 7,000 samples and 55 cost.

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Bolei He, Xinran He, Mengke Chen, Xianwei Xue, Ying Zhu, Zhen-Hua Ling

Efficient Sequential Decision Making with Large Language Models (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) excel in many areas but face challenges with complex reasoning tasks, such as Multi-Hop Question Answering (MHQA).
Approach:	They propose a framework to enhance models’ reasoning capability through iterative self-exploration that addresses key errors in MHQA tasks such as Evidence Aggregation and Reasoning Decomposition.
Outcome:	Extensive experiments on multiple MHQA benchmarks show that the proposed framework significantly improves reasoning accuracy and task performance.

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Dingyang Chen, Qi Zhang, Yinglun Zhu

Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations (2026.findings-acl)

Challenge:	Existing approaches to retrain or finetune large language models (LLMs) for decision making suffer from computational burden of gradient updates.
Approach:	They propose a model selection algorithm that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making.
Outcome:	The proposed approach outperforms both traditional decision making algorithms and vanilla LLM agents on a large-scale Amazon dataset.

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Dongsheng Chen, Yingqi Zhu, Xingyue Zhang, Wenqing Zhou, Lei Li

SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)

Challenge:	Despite the remarkable generation capabilities of large language models, the issue of hallucination remains a critical challenge.
Approach:	They propose a contrastive decoding framework based on dynamic pointwise mutual information that disentangles spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures and prioritizes causal logic.
Outcome:	The proposed framework significantly improves the model’s factuality and reasoning robustness while maintaining high computational efficiency.

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Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala

FARSS: Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning (2026.findings-acl)

Challenge:	Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql.
Approach:	They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions.
Outcome:	The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation.

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Renxing Chen, Ziwei Xiang, Peisong Wang, Hongjian Fang, Meng Li, Fanhu Zeng, Yanan Zhu, Peipei Yang, Xu-Yao Zhang, Jian Cheng

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)

Challenge:	Low-rank adaptation methods for large language models have limitations in preserving world knowledge and limiting updates to preserve world knowledge.
Approach:	They propose a Fisher-optimized adaptive low Rank and Singular-VectorSelection framework for knowledge-preserving fine-tuning that allows efficient and task-sensitive updates.
Outcome:	The proposed framework outperforms existing methods for knowledge-preserving fine-tuning.

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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin

NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models (2025.coling-main)

Challenge:	Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain .
Approach:	They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora.
Outcome:	The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions.

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Han Han, Tong Zhu, Xiang Zhang, MengSong Wu, Xiong Hao, Wenliang Chen

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)

Challenge:	Existing benchmarks on nested tool learning are lacking relevant data instances.
Approach:	They propose a method to construct large-scale nested tool calls with different nesting structures using a large-quality dataset.
Outcome:	The proposed method can be used to evaluate the nested tool learning abilities of large language models (LLMs) in real-world applications.

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Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li

PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics (2024.findings-naacl)

Challenge:	Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view.
Approach:	Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .
Outcome:	Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .

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Derui Zhu, Dingfan Chen, Qing Li, Zongxiong Chen, Lei Ma, Jens Grossklags, Mario Fritz

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)

Challenge:	Existing studies have recognized hallucination as a notable concern in large autoregressive language models (LLMs).
Approach:	They propose a polygraph for large language models that detects "hallucination" they demonstrate that hallucination can be detected by tractable probabilistic models .
Outcome:	The proposed model outperforms state-of-the-art methods on open-source LLMs by 20% on TruthfulQA benchmarks.

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Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, Fakhri Karray

Visual Data in NLP

Challenge:	Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries.
Approach:	They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs.
Outcome:	The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios.

ToolExpNet: Optimizing Multi-Tool Selection in LLMs with Similarity and Dependency-Aware Experience Networks (2025.findings-acl)

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Zijing Zhang, Zhanpeng Chen, He Zhu, Ziyang Chen, Nan Du, Xiaolong Li

RISK: A Framework for GUI Agents in E-commerce Risk Management (2026.acl-long)

Challenge:	Current empirical methods that focus on isolated tools learning struggle with accurate multi-tool selection due to issues like confusing similar tools and neglecting dependencies.
Approach:	They propose a tool-learning paradigm which integrates tools and trial-and-error experiences into a network characterized by semantic similarity and dependency relationships.
Outcome:	The proposed model outperforms existing methods on multiple real-world API datasets and significantly outperformed baselines.

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Renqi Chen, Zeyin Tao, Jianming Guo, Jingzhe Zhu, Yiheng Peng, Qingqing Sun, Tianyi Zhang, Shuai Chen

Challenge:	RISK is a framework designed to automate multi-step web interactions in e-commerce risk management.
Approach:	a new framework is designed to build and deploy GUI agents for e-commerce risk management . RISK-R1 provides a scalable, domain-specific solution for automating complex web interactions .
Outcome:	RISK provides a scalable, domain-specific solution for automating complex web interactions in e-commerce risk management.

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models (2024.findings-acl)

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Gantavya Bhatt, Yifang Chen, Arnav Das, Jifan Zhang, Sang Truong, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Du, Kevin Jamieson, Jordan Ash, Robert Nowak

HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)

Challenge:	Supervised finetuning (SFT) on instruction datasets has shown immense potential in improving the zero-shot generalization capabilities observed in large language models (LLMs).
Approach:	They propose to use experimental design to minimize the computational cost of active learning by identifying useful subsets of samples to annotate from an unlabeled pool.
Outcome:	The proposed methods save 50% of the annotation cost compared to random sampling on generative tasks.

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Qing Li, Jiahui Geng, Zongxiong Chen, Derui Zhu, Yuxia Wang, Congbo Ma, Chenyang Lyu, Fakhri Karray

Prompt-based Conservation Learning for Multi-hop Question Answering (2022.coling-1)

Challenge:	Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability.
Approach:	They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements.
Outcome:	The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods.

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Zhenyun Deng, Yonghua Zhu, Yang Chen, Qianqian Qi, Michael Witbrock, Patricia Riddle

AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)

Challenge:	Existing multi-hop QA methods fail to answer a large fraction of sub-questions even if their parent questions are answered correctly.
Approach:	They propose a Prompt-based Conservation Learning framework that acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks.
Outcome:	The proposed framework acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks, mitigating forgetting.

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Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, Yue Zhang

Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)

Challenge:	Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models .
Approach:	They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models.
Outcome:	The proposed method outperforms standard prompt-based methods in few-shot settings.

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Tong Zhu, Daize Dong, Xiaoye Qu, Jiacheng Ruan, Wenliang Chen, Yu Cheng

An Iterative Associative Memory Model for Empathetic Response Generation (2024.acl-long)

Challenge:	Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning.
Approach:	They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes.
Outcome:	The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget.

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Zhou Yang, Zhaochun Ren, Wang Yufeng, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao

Syntax Matters: Towards Spoken Language Understanding via Syntax-Aware Attention (2023.findings-emnlp)

Challenge:	Existing methods for empathetic response generation ignore the associated words between dialogue utterances.
Approach:	They propose an iterative associative memory model to capture associated words between dialogue utterances and situations, dialogue history, and a memory module for storing associated words.
Outcome:	The proposed model captures key words between dialogue utterances and situations, dialogue history, and a memory module, thereby accurately and nuancedly comprehending the utterables.

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Yifeng Xie, Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen

Syntactic Text Processing

Challenge:	Existing studies on SLU systems have focused on integrating syntactic information into language models.
Approach:	They propose a model where attention scopes are constrained based on syntactic relationships.
Outcome:	The proposed model improves on three datasets and can be integrated into other language models to further boost their performance.

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)

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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang

ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)

Challenge:	Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited.
Approach:	They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments.
Outcome:	Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning.

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Jingxuan Wei, Nan Xu, Junnan Zhu, null Haoyanni, Gaowei Wu, Qi Chen, Bihui Yu, Lei Wang

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (N19-1)

Challenge:	Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models.
Approach:	They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs.
Outcome:	The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought.

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Jianmo Ni, Chenguang Zhu, Weizhu Chen, Julian McAuley

DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains (2025.emnlp-main)

Challenge:	Existing approaches to open-domain question answering struggle to retrieve indirectly related evidence when no direct evidence is provided.
Approach:	They propose a retriever-reader model that learns to attend on essential terms during the question answering process.
Outcome:	The proposed model achieves the state-of-the-art on multiple open-domain QA datasets and achieves a 'reader-reader' level.

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Zhihui Chen, Kai He, Yucheng Huang, Yunxiao Zhu, Mengling Feng

Generalized Category Discovery with Large Language Models in the Loop (2024.findings-acl)

Challenge:	Existing zero-shot detectors fail when applied to specialized content due to domain shift . DivScore outperforms state-of-the-art detectors in specialized domains .
Approach:	They propose a zero-shot detection framework that uses normalized entropy-based scoring and domain knowledge distillation to identify LLM-generated text in specialized domains.
Outcome:	The proposed framework outperforms state-of-the-art detectors on medical and legal datasets with 14.4% higher AUROC and 64.0% higher recall.

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Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, QianYing Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen

Structure-aware Fine-tuning for Code Pre-trained Models (2024.lrec-main)

Challenge:	Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data.
Approach:	They propose a framework that introduces Large Language Models into the training loop to generate category names without human effort.
Outcome:	The proposed framework outperforms SOTA models on three benchmark datasets and generates accurate category names for the discovered clusters.

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Jiayi Wu, Renyu Zhu, Nuo Chen, Qiushi Sun, Xiang Li, Ming Gao

The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models (2024.emnlp-main)

Challenge:	Existing CodePTMs are mainly structure-free and structurebased, but how to fine-tune them remains a challenge.
Approach:	They propose a plug-and-play fine-tuning method that incorporates structural knowledge into pre-trained code models.
Outcome:	The proposed method can benefit CodePTMs more with limited training data.

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Yanjun Chen, Dawei Zhu, Yirong Sun, Xinghao Chen, Wei Zhang, Xiaoyu Shen

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) significantly enhances Natural Language Processing by aligning language models with human expectations.
Approach:	They propose to integrate feedback from humans into RLHF to improve language models by capturing human-like preferences.
Outcome:	The proposed model outperforms models trained with moderately accurate reward models on relevance, factuality, and completeness tasks.

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Zhe Chen, Yusheng Liao, Zhiyuan Zhu, Haolin Li, Hongcheng Liu, Yanfeng Wang, Yu Wang

MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)

Challenge:	Medical large vision-language models suffer from factual inaccuracies and unreliable outputs.
Approach:	They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources.
Outcome:	The proposed framework improves Med-LVLMs through heterogeneous knowledge sources.

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Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Challenge:	generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics.
Approach:	They propose a benchmark to evaluate and analyze the safety risks of molecular generation.
Outcome:	The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation.

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Hanghui Guo, Jia Zhu, Shimin Di, Weijie Shi, Zhangze Chen, Jiajie Xu

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)

Challenge:	Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content.
Approach:	They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs.
Outcome:	The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method.

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Hongyu Zhu, Yan Chen, Jing Yan, Jing Liu, Yu Hong, Ying Chen, Hua Wu, Haifeng Wang

Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)

Challenge:	a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs .
Approach:	They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts .
Outcome:	The proposed model is more robust than other models on natural questions with 32 linguistic perturbations.

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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu

How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026.acl-long)

Challenge:	Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures.
Approach:	They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree .
Outcome:	The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness.

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Tongyao Zhu, Qian Liu, Chang Ma, Jinghan Zhang, Longxu Dou, Junxian He, Shiqi Chen

Neural Natural Language Inference Models Enhanced with External Knowledge (P18-1)

Challenge:	Low-resource languages are a long-tail problem for multilingual LLMs due to limited high-quality training data.
Approach:	They propose a method that translates high-quality, knowledge-rich English data into low-resource languages . they propose SynRank, which leverages synthetic data as positive samples to train a classifier .
Outcome:	The proposed method matches handcrafted rule-based filtering by human experts and significantly improves knowledge-intensive tasks with less data.

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Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, Si Wei

SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)

Challenge:	Existing datasets that allow for complex models to be trained are limited . if data is not available, can machines learn all knowledge needed to perform natural language inference?
Approach:	They propose to enrich neural natural language inference models with external knowledge . they propose to use this knowledge to build NLI models to leverage it .
Outcome:	The proposed models improve on the SNLI and MultiNLI datasets.

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Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives .
Approach:	They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding.
Outcome:	The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding.

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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang

RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs (2024.naacl-demo)

Challenge:	Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach:	They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome:	The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

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Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric Xing, Zhiting Hu

Game on Tree: Visual Hallucination Mitigation via Coarse-to-Fine View Tree and Game Theory (2024.emnlp-main)

Challenge:	Recent advances in machine learning (ML) are attributed to large language models (LLMs), but their escalating memory requirements require developers to partition a large model to distribute it across multiple GPUs or TPUs.
Approach:	They propose a lightweight and user-friendly tool to automate distributed training and inference for LLMs and to simplify ML pipeline development.
Outcome:	The proposed tool automates distributed training and inference for LLMs, and simplifies ML pipeline development.

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Xianwei Zhuang, Zhihong Zhu, Zhanpeng Chen, Yuxin Xie, Liming Liang, Yuexian Zou

Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)

Challenge:	Large vision-language models produce unfaithful visual hallucinations, also known as visual halluinations, which hinders their application in multimodal understanding and decision-making.
Approach:	They propose a plug-and-play train-free decoding algorithm for mitigating visual hallucinations . they leverage visual information to construct a coarse-to-fine visual view tree .
Outcome:	The proposed algorithm reduces visual hallucinations (VH) by leveraging visual information to construct a coarse-to-fine visual view tree (CFTree)

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Chuang Zhou, Zhu Wang, Shengyuan Chen, Jiahe Du, Qiyuan Zheng, Zhaozhuo Xu, Xiao Huang

ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry (2025.acl-industry)

Challenge:	Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information.
Approach:	They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a .
Outcome:	The proposed framework outperforms state-of-the-art learning methods while requiring less resources.

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Qinwen Chen, Wenbiao Tao, Zhiwei Zhu, Mingfan Xi, Liangzhong Guo, Yuan Wang, Wei Wang, Yunshi Lan

MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)

Challenge:	Existing methods for Community Question Answering (CQA) focus on static knowledge, limiting their applicability to real-world scenarios.
Approach:	They propose a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism.
Outcome:	The proposed framework outperforms baselines on three industrial CQA datasets and achieves 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

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Xuxin Cheng, Zhihong Zhu, Xianwei Zhuang, Zhanpeng Chen, Zhiqi Huang, Yuexian Zou

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)

Challenge:	Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems.
Approach:	They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts .
Outcome:	The proposed framework achieves state-of-the-art on three benchmark SLU datasets.

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Lin Zhong, Renjin Zhu, Shujuan Ma, Jinhao Cui, Lingzhi Wang, Hao Chen, Qing Liao

Croppable Knowledge Graph Embedding (2025.acl-long)

Challenge:	Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level.
Approach:	They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers.
Outcome:	The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control.

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Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen

RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)

Challenge:	Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks.
Approach:	They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs.
Outcome:	The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity.

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Shuting Wang, Xin Yu, Mang Wang, Weipeng Chen, Yutao Zhu, Zhicheng Dou

Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)

Challenge:	Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents.
Approach:	They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents.
Outcome:	The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets.

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Tao Wu, Jingyuan Chen, Wang Lin, Mengze Li, Yumeng Zhu, Ang Li, Kun Kuang, Fei Wu

Challenge:	Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns.
Approach:	They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism.
Outcome:	The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism.

Can Large Language Models Tackle Graph Partitioning? (2025.emnlp-main)

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Yiheng Wu, Ningchao Ge, Yanmin Li, Liwei Qian, Mengna Zhu, Haoyu Yang, Haiwen Chen, Jibing Wu

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework (2025.emnlp-main)

Challenge:	Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities.
Approach:	They propose a pipeline for coarsening, reasoning, and refining to enable LLMs to perform graph partitioning on small-scale graphs.
Outcome:	The proposed pipeline can handle graph partitioning tasks on small graphs with coarsening, reasoning, and refining.

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Fanqi Kong, Xiaoyuan Zhang, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng

mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)

Challenge:	Social media platforms provide an ideal testbed for large language models that exhibit human-like behavior.
Approach:	They propose an LLM-based social **Bot that enhances human-like generative capabilities through an adversarial learning framework.
Outcome:	The proposed framework generates human-like content aligned with diverse user profiles . it exhibits strong social responsiveness, more accurately modeling opinion dynamics .

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022.findings-emnlp)

Challenge:	Multimodal embedding models encode multimedia inputs into latent vector representations.
Approach:	They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data .
Outcome:	The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark.

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Nuo Chen, Qiushi Sun, Renyu Zhu, Xiang Li, Xuesong Lu, Ming Gao

Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity (2025.emnlp-main)

Challenge:	Existing code pre-trained models fail to consider inherent characteristics of codes . Existing methods to interpret code pretrained model fail to take into account inherent characteristics .
Approach:	They propose a probing method to quantitatively interpret how CodePTMs attend code structure.
Outcome:	The proposed method denoises input code sequences and measures commonality between token-level attention scores and pair-wise distances between corresponding AST nodes.

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Lei Yu, Jingcheng Niu, Zining Zhu, Xi Chen, Gerald Penn

MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)

Challenge:	Experimental results show that DiscoGP extracts sheaves that preserve 93-100% of a model’s performance while comprising only 1-7% of the original weights and connections.
Approach:	They propose a framework for extracting self-contained modular units within neural language models (LMs) they use a gradient-based pruning algorithm to prune the original LM to a sparse skeleton .
Outcome:	The proposed framework preserves 93-100% of the original model's performance while preserving only 1-7% of the model''s original weights and connections.

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Weihao Zhao, Weidong He, Hao Wang, Haoyang Bi, Han Wu, Chen Zhu, Tong Xu, Enhong Chen

A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (2020.coling-main)

Challenge:	Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments.
Approach:	They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences.
Outcome:	The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously.

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Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, Xiaodong Shi

Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning (2026.findings-acl)

Challenge:	Recent studies have shown that inter-sentence information is helpful for improving the performance of document-level Neural Machine Translation models, but what information should be regarded as context remains ambiguous.
Approach:	They propose a cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information.
Outcome:	The proposed model achieves substantial improvements over the state-of-the-art models on NIST evaluation sets.

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Longteng Guo, Yifan Wang, Pengkang Huo, Tailai Chen, Yuze Wu, Jing Liu, Xinxin Zhu

To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models (2024.emnlp-main)

Challenge:	Recent advances in multimodal large language models demonstrate strong performance on visual reasoning benchmarks.
Approach:	They propose a benchmark for vision-centric reasoning that integrates visual and textual information for non-trivial reasoning.
Outcome:	The proposed benchmark exposes gaps between humans and current MLLMs and reveals limited benefits from test-time reasoning strategies.

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Junyan Lin, Haoran Chen, Dawei Zhu, Xiaoyu Shen

A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)

Challenge:	Recent multimodal large language models (MLLMs) have attracted widespread attention from both industry and academia due to their potential to handle multiple modalities in a unified framework.
Approach:	They propose to classify connectors into feature-preserving and feature-compressing types and categorize tasks into three task types: coarse-grained perception, fine-grain perception, and reasoning.
Outcome:	The proposed architectures perform better on tasks with varying granularities than on external fusion architectures.

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Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang

Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers.
Approach:	They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment.
Outcome:	The proposed model enables finer credit assignment, richer diagnostics, and improved robustness.

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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou

LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)

Challenge:	Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks.
Approach:	They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages.
Outcome:	The proposed method outperforms existing methods on RALM benchmarks.

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Zhiwen Ruan, Yixia Li, He Zhu, Longyue Wang, Weihua Luo, Kaifu Zhang, Yun Chen, Guanhua Chen

Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

Challenge:	Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages.
Approach:	They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder.
Outcome:	Experiments on multilingual reasoning tasks show that the proposed framework outperforms baselines.

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Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

Challenge:	Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach:	They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome:	The proposed model improves on two representative datasets and improves performance.

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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)

Challenge:	Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks.
Approach:	They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance.
Outcome:	The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks.

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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu

Visual Data in NLP

Challenge:	Recent VSE models combine simple pooling methods with hard triplet loss to improve performance.
Approach:	They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods.
Outcome:	The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval.

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning (2026.acl-long)

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Bowen Ding, Yuhan Chen, Jiayang Lyu, Jiyao Yuan, Qi Zhu, Shuangshuang Tian, Dantong Zhu, Futing Wang, Heyuan Deng, Fei Mi, Lifeng Shang, Tao Lin

Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)

Challenge:	evolving generic Large Language Models into specialized Large Reasoning Models requires effective post-training.
Approach:	They propose a plasticity-ceiling framework to harness expert trajectories . they establish the Sequential SFT-then-RL pipeline as the superior standard .
Outcome:	The proposed framework overcomes stability and premature convergence deficits in synchronized approaches.

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Beidan Liu, Zhengqiu Zhu, Chen Gao, Tianle Pu, Yong Zhao, Wei Qi, Quanjun Yin

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers.
Approach:	They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning.
Outcome:	Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Enhancing Sentence Embedding with Generalized Pooling (C18-1)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Qian Chen, Zhen-Hua Ling, Xiaodan Zhu

Federated Incremental Named Entity Recognition (2025.coling-main)

Challenge:	Existing methods for learning sentence embedding are limited, but still need to be improved.
Approach:	They propose a vector-based multi-head attention model that uses special cases of max pooling, mean pooling and scalar self-attention.
Outcome:	The proposed model improves on natural language inference, author profiling, and sentiment classification tasks.

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Zesheng Liu, Qiannan Zhu, Cuiping Li, Hong Chen

Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (2024.findings-naacl)

Challenge:	Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting.
Approach:	They propose a model which allows for catastrophic forgetting of old entity types . they propose adaptive pseudo labeling and a prototypical relation distillation loss .
Outcome:	The proposed model overcomes catastrophic forgetting problem on old entity types with semantic shift.

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Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Zhu, Kam-Fai Wong, Ruifeng Xu

ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)

Challenge:	Large Language Models (LLMs) suffer catastrophic forgetting when tailored to specific domains . authors present a novel approach to manage multi-domain LLM adaptation .
Approach:	They propose a strategy to manage multi-domain LLM adaptation using self-distillation and role integration.
Outcome:	The proposed model alleviates catastrophic forgetting and inter-domain confusion while maintaining robust general capabilities.

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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen

Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)

Challenge:	Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models.
Approach:	They propose a dataset that provides rigorous evaluation of multi-hop tool use.
Outcome:	The proposed model achieves 49.04% accuracy across five model families.

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Chengcheng Han, Renyu Zhu, Jun Kuang, Fengjiao Chen, Xiang Li, Ming Gao, Xuezhi Cao, Yunsen Xian

BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)

Challenge:	Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation.
Approach:	They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification.
Outcome:	The proposed model can achieve significant performance gains over state-of-the-art models.

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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin

Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks.
Approach:	They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question.
Outcome:	The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%.

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Yuhao Zhang, Chen Xu, Bei Li, Hao Chen, Tong Xiao, Chunliang Zhang, Jingbo Zhu

Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)

Challenge:	auxiliary tasks are highly consistent with end-to-end speech translation (ST) but their effectiveness has not been thoroughly studied.
Approach:	They propose an improved multi-task learning approach for the ST task that bridges the modal gap by mitigating the difference in length and representation.
Outcome:	The proposed approach achieves state-of-the-art on the MuST-C dataset with 20.8% of training time required by the current SOTA method.

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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai

ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)

Challenge:	Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP)
Approach:	They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic.
Outcome:	The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism.

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Xingchi Chen, Peiyuan Zong, Ziqiang Gao, Qing Li, Yong Jiang, Fa Zhu, Hui Li

Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization (2023.acl-industry)

Challenge:	Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios.
Approach:	They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals.
Outcome:	Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget.

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Shansan Gong, Zelin Zhou, Shuo Wang, Fengjiao Chen, Xiujie Song, Xuezhi Cao, Yunsen Xian, Kenny Zhu

When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario (2023.findings-acl)

Challenge:	e-commerce platforms are encountering increasingly complex product categorization scenarios . multiple business domains correspond to different category taxonomies, with different depths and distinct literal expressions of category names.
Approach:	They propose a taxonomy-agnostic framework that calculates semantic relatedness between product titles and category names in the vector space.
Outcome:	The proposed framework outperforms strong baselineson three dynamic multi-domain product categorization tasks.

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Chengcheng Han, Liqing Cui, Renyu Zhu, Jianing Wang, Nuo Chen, Qiushi Sun, Xiang Li, Ming Gao

A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)

Challenge:	Large pre-trained language models (PLMs) are expensive and may not be open-sourced due to commercial considerations and potential risks of misuse.
Approach:	They propose to introduce gradient descent into black-box tuning scenario . they propose a method which integrates gradient descent and derivative-free optimization .
Outcome:	The proposed method achieves significant performance gains over previous state-of-the-art methods.

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Fan Zhang, Hao Chen, Zhihong Zhu, Ziheng Zhang, Zhenxi Lin, Ziyue Qiao, Yefeng Zheng, Xian Wu

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

Challenge:	Existing single-cell foundation language models are based on pre-trained and large language models.
Approach:	They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms .
Outcome:	The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks.

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

History-Aware Conversational Dense Retrieval (2024.findings-acl)

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

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Fengran Mo, Chen Qu, Kelong Mao, Tianyu Zhu, Zhan Su, Kaiyu Huang, Jian-Yun Nie

From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)

Challenge:	Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy.
Approach:	They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns.
Outcome:	The proposed system improves on two public conversational search datasets.

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Wenjing Xie, Xiaobo Liang, Juntao Li, Wanfu Wang, Kehai Chen, Qiaoming Zhu, Min Zhang

Probing Language Models for Pre-training Data Detection (2024.acl-long)

Challenge:	Existing approaches enhance reasoning through Chain-of-Thought, Program-ofThough, and Tool-Integration.
Approach:	They propose a tool-awareness training method that leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks.
Outcome:	The proposed method improves the model's tool utilization capabilities, including proactivity and execution success rates.

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Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Bing Liu, Haonan Lu, Wenliang Chen

Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation (2025.naacl-srw)

Challenge:	Large Language Models (LLMs) have shown impressive capabilities, while raising concerns about the data contamination due to privacy issues and leakage of benchmark datasets in the pre-training phase.
Approach:	They propose to utilize the probing technique to examine the model’s internal activations to detect pre-training data contamination by examining the model's internal activates.
Outcome:	The proposed method outperforms baselines and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy.

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Yirong Sun, Dawei Zhu, Yanjun Chen, Erjia Xiao, Xinghao Chen, Xiaoyu Shen

Sparse Latents Steer Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Large language models excel in machine translation, but most studies focus on sentence-level translation.
Approach:	They propose to use LLMs as a judge paradigm to evaluate document-level translations by directly prompting them to translate entire documents in a single pass.
Outcome:	The proposed method improves translation quality even without document-level fine-tuning compared to translating sentences separately .

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Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

Challenge:	In this study, we uncover interpretable latents that govern RAG behavior in large language models . Sparse Autoencoders are used to control large language model (LLM) behavior .
Approach:	They leverage Sparse Autoencoders within the LLaMA Scope to uncover latents that govern RAG behaviors.
Outcome:	The proposed model can be used to control large language models without architectural modifications.

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)

Challenge:	Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations.
Approach:	They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge.
Outcome:	The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld.

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Siyuan Chen, Meilin Wang, Minghao Lv, Zhiling Zhang, Juqianqian Juqianqian, Dejiyangla Dejiyangla, Yujia Peng, Kenny Zhu, Mengyue Wu

Challenge:	Existing studies focus on the semantic content of social media posts, overlooking the evolving nature of mental disorders and symptoms.
Approach:	They extract causality between psychiatric symptoms and life events from social media posts and extract temporal attributes to improve diagnosis and treatment planning.
Outcome:	The extracted causality features improve diagnostic and treatment planning and improve performance in tasks such as depression and diagnosis point detection.

Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)

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Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Zhu

Detecting Speaker Personas from Conversational Texts (2021.emnlp-main)

Challenge:	Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability due to lack of symptom modeling.
Approach:	They propose to annotate a social media corpus of symptom classes related to 7 mental disorders using a knowledge graph and a new annotation framework to facilitate further research.
Outcome:	The proposed model outperforms strong pure-text baselines and provides convincing MDD explanations with case studies.

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Jia-Chen Gu, Zhenhua Ling, Yu Wu, Quan Liu, Zhigang Chen, Xiaodan Zhu

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Existing studies on personas are pre-defined and hard to obtain before a conversation . a new task aims to detect speaker persona based on conversational text .
Approach:	They propose a task to detect speaker personas based on conversational text . they build a dataset for SPD and propose utterance-to-profile matching networks .
Outcome:	The proposed task outperforms baseline models and utterance-to-profile (U2P) matching networks.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)

Challenge:	Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs).
Approach:	They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives.
Outcome:	The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives.

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Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, Rongsheng Zhang

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

Challenge:	Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in the realm of literary creation.
Approach:	They propose a framework for unleashing the creativity of large language models (LLMs) they assign LLMs to different roles involved in real-world scenario, they write .
Outcome:	The proposed framework outperforms baselines in terms of coherence, relevance, interestingness and overall quality on automatically generated screenplays.

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)

Challenge:	Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing.
Approach:	They build a dataset using DS-generated data as training data and hire annotators to label test data.
Outcome:	The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation.

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Xu Liu, Yan Chen, Kan Ling, Yichi Zhu, Hengrun Zhang, Guisheng Fan, Huiqun Yu

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance (2026.acl-long)

Challenge:	Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies.
Approach:	They propose an automated framework capable of discovering, retrieving, and evolving attack strategies.
Outcome:	The proposed framework outperforms existing baselines in a black-box setting.

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Yuchun Fan, Bei Li, Peiguang Li, Yilin Wang, Yongyu Mu, Jian Yang, Xin Chen, Rongxiang Weng, Jingang Wang, Xunliang Cai, JingBo Zhu, Tong Xiao

CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)

Challenge:	Existing methods for enhancing multi-step reasoning have not fully translated to multilingual contexts.
Approach:	They propose a framework that leverages language-conditioned hints to guide exploration in non-English reasoning tasks.
Outcome:	Empirical results show that the proposed framework improves reasoning performance without compromising language consistency.

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Yue Cui, Junhui Zhu, Liner Yang, Xuezhi Fang, Xiaobin Chen, Yujie Wang, Erhong Yang

CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency (2025.emnlp-main)

Challenge:	Existing tools to analyze linguistic complexity are limited and different because of different research purposes.
Approach:	They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics .
Outcome:	The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe .

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Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao