Papers by Zhu Zhang

Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)

Challenge:	Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions.
Approach:	They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information .
Outcome:	The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities.

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Qingqing Hong, Dongyu Zhang, Jiayi Lin, Dapeng Yin, Shuyue Zhu, Junli Wang

Conformity in Large Language Models (2025.acl-long)

Challenge:	Sarcasm is a complex form of sentiment expression widely used in human daily life.
Approach:	They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity.
Outcome:	The proposed dataset shows that it is more balanced than zero-shot models.

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Xiaochen Zhu, Caiqi Zhang, Tom Stafford, Nigel Collier, Andreas Vlachos

LECO: Improving Early Exiting via Learned Exits and Comparison-based Exiting Mechanism (2023.acl-srw)

Challenge:	Conformity is a form of social influence that affects the way people respond to information.
Approach:	They adapt psychological experiments to examine the extent of conformity in large language models.
Outcome:	The proposed interventions mitigate conformity by reducing the naturalness of majority tones and reducing instruction-tuned models.

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Jingfan Zhang, Ming Tan, Pengyu Dai, Wei Zhu

SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)

Challenge:	Recent work on dynamic early exiting has neglected the intermediate exits’ architectural designs.
Approach:	They propose a framework for learning exits and COmparison-based early exiting to improve PTMs’ early exit performance.
Outcome:	The proposed framework achieves the SOTA performance on multi-exit BERT training and dynamic early exiting on pre-trained models.

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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang

Challenge:	Existing benchmarks conflate coordination ability with role-based priors.
Approach:	They propose a role-free benchmark for evaluating free-form collaboration under information silos.
Outcome:	The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs.

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

A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models (2025.naacl-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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Xiujie Song, Mengyue Wu, Kenny Q. Zhu, Chunhao Zhang, Yanyi Chen

CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)

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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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Mao Zhang, Yongxin Zhu, Zhen Liu, Zhimin Bao, Yunfei Wu, Xing Sun, Linli Xu

HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)

Challenge:	Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions.
Approach:	They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level.
Outcome:	The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency .

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Shuyu Zhang, Yifan Wei, Xinru Wang, Yanmin Zhu, Yangfan He, Yixuan Weng, Yujie Liu, Bin Li

Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)

Challenge:	Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training.
Approach:	They propose a framework that enhances zero-shot slot inference through robust prompt alignment.
Outcome:	Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST.

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Wenhao Yu, Chenguang Zhu, Zhihan Zhang, Shuohang Wang, Zhuosheng Zhang, Yuwei Fang, Meng Jiang

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

Challenge:	Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever.
Approach:	They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever.
Outcome:	The proposed framework outperforms existing methods on commonsense reasoning tasks.

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

Patton: Language Model Pretraining on Text-Rich Networks (2023.acl-long)

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

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Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Xinyang Zhang, Qi Zhu, Jiawei Han

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

Challenge:	Existing models for text-rich networks do not take inter-document structure into account.
Approach:	They propose a pretraining framework for a text-rich network using a masked language model and a masking node prediction framework.
Outcome:	The proposed model outperforms baselines on four tasks in academic and e-commerce domains.

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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

Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation (2025.findings-acl)

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.

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Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu, Weiqiao Shan, Benyou Wang, Tong Xiao, Yuxin Huang, Zhengtao Yu, JingBo Zhu

Hierarchical Memory Organization for Wikipedia Generation (2025.acl-long)

Challenge:	Existing textless speech-to-speech translation models have two main challenges: 1) learning cross-modal features and 2) learning alignment of difference languages in long sequences.
Approach:	They propose a unit language to overcome two main modeling challenges . they propose task prompt modeling to utilize the unit language in guiding the modeling process.
Outcome:	The proposed language improves over a strong baseline and achieves comparable performance to models trained with text.

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Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li

Challenge:	Existing methods for generating Wikipedia articles do not utilize memory directly for outline generation.
Approach:	They propose a method to generate Wikipedia articles autonomously by leveraging a hierarchical memory architecture.
Outcome:	The proposed framework outperforms baseline methods in producing informative and reliable articles.

Cross Copy Network for Dialogue Generation (2020.emnlp-main)

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Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao

Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities (2025.findings-acl)

Challenge:	Despite the success of sequence-to-sequence models, dialogue logics are often ignored.
Approach:	They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously.
Outcome:	The proposed network architecture is superior to existing state-of-the-art models.

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Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Mingyu Jin, Shuhang Lin, Haochen Xue, Zelong Li, Jindong Wang, Yongfeng Zhang

CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending (2024.acl-long)

Challenge:	Using large language models, large language model models can be used to evaluate reasoning abilities in context-rich scenarios.
Approach:	They construct datasets for both propositional logic and abductive logic reasoning with four difficulty levels across 12 distinct domains based on Wikipedia categorization and those with purely abstract variables.
Outcome:	The proposed model can be used to benchmark LLMs in real-world scenarios, but not in context-rich scenarios.

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Shiyi Zhu, Jing Ye, Wei Jiang, Siqiao Xue, Qi Zhang, Yifan Wu, Jianguo Li

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

Challenge:	Existing models that use self-attention and position embedding have anomalous behavior that hinder long context window extrapolation.
Approach:	They propose a collinear constraint between Q and K to integrate RoPE and self-attention.
Outcome:	The proposed model integrates self-attention and position embedding into LLMs without fine-tuning.

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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

Can Machine Unlearning Reduce Social Bias in Language Models? (2024.emnlp-industry)

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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Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khattak

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	Existing methods for mitigating bias in language models are expensive and time-consuming . comparative studies have not evaluated their respective advantages and disadvantages .
Approach:	They propose to use Partitioned Contrastive Gradient Unlearning and Negation via Task Vector to reduce social biases in open-source language models.
Outcome:	The proposed methods outperform PCGU and DPO in debiasing models . the proposed methods can be easily tuned to balance the trade-off between bias reduction and generation quality .

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026.findings-acl)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, Longxu Dou, Xianzhen Luo, Yingwei MA, Qingfu Zhu, Binhua Li, Fei Huang, Yongbin Li

MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL (2025.coling-main)

Challenge:	Prior work showed that multiple reasoning formats outperform a single format when generating multiple answers.
Approach:	They propose a method to measure reasoning error when generating multiple answers . they propose 'formatadapter' which generates and selects suitable reasoning formats .
Outcome:	The proposed method achieves a 4.3% performance improvement over previous works on math and commonsense reasoning tasks.

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Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, Wanxiang Che

Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)

Challenge:	Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question.
Approach:	They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases.
Outcome:	The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets.

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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)

Challenge:	Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process.
Approach:	They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks.
Outcome:	The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT.

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Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok

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

Challenge:	Named entity recognition (NER) is an important step in most natural language processing (NLP) applications.
Approach:	They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training'
Outcome:	The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization.

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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.

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

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

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

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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

LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.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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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

UniToolBench: A Benchmark for Tool-Augmented LLMs in Cross-Domain, Universal Task Automation (2026.findings-eacl)

Challenge:	Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach:	They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome:	The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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Xiaojie Guo, Yang Zhang, Bing Zhang, Ryo Kawahara, Mikio Takeuchi, Yada Zhu

GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)

Challenge:	Existing benchmarks that focus on manually curated tool graphs lack scalability and diversity across domains.
Approach:	They propose a large-scale, cross-domain benchmark to evaluate LLMs' ability to reason over and utilize interconnected tools for automation.
Outcome:	The proposed benchmark incorporates automated tool graph construction by formulating link prediction as a probabilistic task, instead of relying on categorical LLM outputs.

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Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu

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

Challenge:	Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction.
Approach:	They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs.
Outcome:	The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%.

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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.

Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)

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Jun Zhang, Haihong E, Tianyi Hu, Yifan Zhu, Meina Song, Haoran Luo

Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace (2025.coling-main)

Challenge:	Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge.
Approach:	They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs.
Outcome:	The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets.

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Jia-Chen Zhang, Yu-Jie Xiong, Chun-Ming Xia, Dong-Hai Zhu, Xi-He Qiu

Transcribing Vocal Communications of Domestic Shiba lnu Dogs (2023.findings-acl)

Challenge:	Existing methods for parameter-efficient fine-tuning have been proposed to reduce time and resource costs.
Approach:	They propose a parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-uning by 8 times.
Outcome:	The proposed method reduces the number of parameters required for fine-tuning by 8 times and achieves comparable or superior performance compared to existing models.

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Jieyi Huang, Chunhao Zhang, Mengyue Wu, Kenny Zhu

Syntactic Text Processing

Challenge:	a recent study has focused on how animals communicate, but the study has been limited . previous studies have focused on a simple classification problem, requiring a model to get a label .
Approach:	They extract Shiba Inu dogs' vocal communications from YouTube videos and translate them into phonetic scripts using a systematic process.
Outcome:	The proposed framework produces the first-of-its-kind Shiba Inu vocal communication dataset . it will be useful for future research in zoology and linguistics.

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)

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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey

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

Challenge:	Existing autoregressive models for dialogue generation suffer from high latency and stability issues.
Approach:	They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching.
Outcome:	The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision.

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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.

DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)

Challenge:	Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly.
Approach:	They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them .
Outcome:	The proposed method improves text-to-SQL performance by 1.4% compared with previous methods .

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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)

Challenge:	Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems.
Approach:	They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation.
Outcome:	Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods.

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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu

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

Challenge:	Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations .
Approach:	They propose a framework to synthesize complex charts and reliable reasoning data from scratch.
Outcome:	Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models .

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

AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation (2025.acl-long)

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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Xiechi Zhang, Zetian Ouyang, Linlin Wang, Gerard De Melo, Zhu Cao, Xiaoling Wang, Ya Zhang, Yanfeng Wang, Liang He

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

Challenge:	Existing evaluation methods based on large language models (LLMs) are expensive and lack expertise due to limitations in human expertise.
Approach:	They propose an open-source automatic evaluation model with 13B parameters specifically engineered to measure the question-answering proficiency of medical LLMs.
Outcome:	The proposed model surpasses baselines in terms of correlation with human judgments.

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

MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation (2025.naacl-long)

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

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Junqing He, Liang Zhu, Rui Wang, Xi Wang, Gholamreza Haffari, Jiaxing Zhang

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

Challenge:	Existing evaluation metrics for memory-augmented dialogue systems lack practical value . current evaluation methods only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors.
Approach:	They propose to use long-term memory to create human-like dialogues using chatbots.
Outcome:	The proposed benchmark covers memory retrieval and memory recognition tasks with both passive and proactive memory recall data.

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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

Bridging Local Details and Global Context in Text-Attributed Graphs (2024.emnlp-main)

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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Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, Siliang Tang

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

Challenge:	Existing studies focus on combining different information levels but overlook interconnections, i.e., contextual textual information among nodes.
Approach:	They propose a framework that bridges local and global perspectives by leveraging contextual textual information.
Outcome:	The proposed framework achieves state-of-the-art performance while reducing tokens significantly.

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

Interesting Culture: Social Relation Recognition from Videos via Culture De-confounding (2025.findings-emnlp)

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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Yuxuan Zhang, Yangfu Zhu, Haorui Wang, Bin Wu

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

Challenge:	a culturally-specific cultural context can be used to train relationship recognition models . cultural confounding factors can be learned, limiting ability to recognize social relationships in different cultures.
Approach:	They propose a culturally-based model that mitigates the influence of culture . they also construct a video social relation recognition dataset to facilitate discussion .
Outcome:	The proposed model surpasses state-of-the-art methods on several datasets.

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

TITA: A Two-stage Interaction and Topic-Aware Text Matching Model (2021.naacl-main)

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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Xingwu Sun, Yanling Cui, Hongyin Tang, Qiuyu Zhu, Fuzheng Zhang, Beihong Jin

Challenge:	Existing models for keyword-document matching do not define topic-aware relevance clearly.
Approach:	They propose a two-stage interaction and topic-aware text matching model to solve this problem . they propose to combine latent topic of document with deep neural representation to model complex interactions between keyword and document.
Outcome:	The proposed model outperforms other well-designed baselines and shows excellent performance in the recommendation system.

CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic (2026.findings-acl)

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Yaocheng Zhang, Haohuan Huang, Zijun Song, Zijie Zhao, Qichao Zhang, Yuanheng Zhu, Dongbin Zhao

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)

Challenge:	Existing search agent pipelines rely on sparse outcome rewards, leading to inefficient exploration and unstable training.
Approach:	They propose a tool-integrated reasoning framework that provides turn-level feedback via a retrospective critic mechanism.
Outcome:	The proposed framework outperforms baselines in multi-hop reasoning benchmarks and achieves faster convergence and training stability.

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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

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

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

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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

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)

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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Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)

Challenge:	Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs.
Approach:	They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model.
Outcome:	The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores .

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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong

One Battle After Another: Probing LLMs’ Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework (2026.acl-long)

Challenge:	Existing detectors are limited in their ability to detect large language models generated content in multilingual environments.
Approach:	They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications.
Outcome:	The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse.

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Qi Jia, Ye Shen, Xiujie Song, Kaiwei Zhang, Shibo Wang, Dun Pei, Xiangyang Zhu, Guangtao Zhai

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)

Challenge:	Existing benchmarks for instruction-following in multi-topic dialogues are limited to a fixed number of turns, susceptible to saturation and failing to account for users’ interactive experience.
Approach:	They propose a framework featuring a three-layer tracking mechanism and a query synthesis agent to mimic sequential user behaviors.
Outcome:	The proposed framework outperforms existing benchmarks in the evaluation of instruction following in multi-topic dialogues and demonstrates deficiencies in failure recovery and fine-grained instruction following.

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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang

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

Challenge:	Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources.
Approach:	They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs.
Outcome:	Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks.

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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

Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)

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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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

LLM Agents in Law: Taxonomy, Applications, and Challenges (2026.acl-long)

Challenge:	Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation.
Approach:	They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task.
Outcome:	The proposed model improves inference speed by 2.3-2.7x times without compromising model performance.

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Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen, Mengnan Du

Challenge:	Large language models (LLMs) have improved the legal domain, but deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.
Approach:	They present a survey of LLM agents for legal tasks and analyze their architectures . they analyze the transition from standard legal LLMs to legal agents .
Outcome:	The proposed architectures bridge the gap between technical capabilities and domain-specific needs.

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Keyan Xu, Dingzirui Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Demystifying Multi-Agent Debate: The Role of Confidence and Diversity (2026.findings-acl)

Challenge:	Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases.
Approach:	They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods.
Outcome:	The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness.

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Xiaochen Zhu, Caiqi Zhang, Yizhou Chi, Tom Stafford, Nigel Collier, Andreas Vlachos

Challenge:	Multi-agent debate (MAD) is widely used to improve large language models' (LLMs) reasoning and test-time scaling.
Approach:	They propose a diversity-aware initialisation that selects a more diverse pool of candidate answers, increasing the likelihood that a correct hypothesis is present at the start of debate.
Outcome:	The proposed protocol outperforms vanilla MAD and majority vote on six reasoning-oriented QA benchmarks.

Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings (2022.findings-emnlp)

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Jian Zhu, Zuoyu Tian, Yadong Liu, Cong Zhang, Chia-Wen Lo

PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data (2025.findings-acl)

Challenge:	a new study addresses the challenge of learning semantic representations from speech signals . speech-based semantic representation can be used for speech mining and spoken language understanding .
Approach:	They propose a multimodal sequential autoencoder that converts speech signals into hidden units . they propose s-HuBERT to induce meaning through knowledge distillation .
Outcome:	The proposed model achieves a moderate correlation with human judgments without labels or transcriptions.

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Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao, Ming Zhu, Shirley Kokane, Silvio Savarese, Huan Wang, Caiming Xiong, Shelby Heinecke

SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents (2024.acl-long)

Challenge:	Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns.
Approach:	They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information.
Outcome:	The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information.

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Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Yonatan Bisk, Graham Neubig, Hao Zhu

Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model (2026.findings-acl)

Challenge:	Existing studies on building language agents have not addressed this social learning gap.
Approach:	They propose an interactive learning method that improves the social intelligence of language agents by using behavior cloning and self-reinforcement based training on filtered social interaction data.
Outcome:	The proposed method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent) without the loss of more generic abilities, such as the ability to answer knowledge-based questions.

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Ziyan Wang, Yingpeng Du, Tianjun Wei, Haoyan Chua, Jieyi Bi, Jie Zhang, Zhu Sun

MCiteBench: A Multimodal Benchmark for Generating Text with Citations (2025.findings-emnlp)

Challenge:	Existing methods for multi-interest analysis of users rely on heuristic assumptions . however, the granularity of raw generation of LLMs is agnostic, leading to overly fine or coarse interest grouping.
Approach:	They propose an LLM-driven adaptive and representative multi-interest modeling framework that exploits the agnostic granularity of LLMs for multi-interest analysis.
Outcome:	The proposed model outperforms baselines on real-world datasets.

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Caiyu Hu, Yikai Zhang, Tinghui Zhu, Yiwei Ye, Yanghua Xiao

Unifying Inference-Time Planning Language Generation (2026.findings-acl)

Challenge:	Existing work focuses on generating citations for text-only content . experimental results reveal MLLMs struggle to ground outputs reliably when handling multimodal input .
Approach:	They propose a benchmark to assess the ability of MLLMs to generate text with citations in multimodal contexts.
Outcome:	The proposed benchmark assesses the ability of MLLMs to generate text with citations in multimodal contexts.

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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)

Challenge:	Large language models (LLMs) are used to generate a formal representation of a plan in a planning language.
Approach:	They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning.
Outcome:	The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages.

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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang

Can Large Language Models Understand Context? (2024.findings-eacl)

Challenge:	Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data.
Approach:	They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance.
Outcome:	The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain.

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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

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

Challenge:	Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features.
Approach:	They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation.
Outcome:	The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models.

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

Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)

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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Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang, Fuli Feng

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

Challenge:	Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs.
Approach:	They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts.
Outcome:	The proposed framework outperforms baseline methods on personalized review generation.

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

Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning (2023.findings-acl)

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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Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, Zhao Cao

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)

Challenge:	SylloBase is a benchmark for syllogistic reasoning, a critical capability widely required in natural language understanding tasks, such as text entailment and question answering.
Approach:	They propose to use a benchmark to learn syllogistic reasoning on a set of templates and to use them to generate and understand slogisms.
Outcome:	The proposed benchmark covers a complete taxonomy of syllogism reasoning patterns, and contains both automatically and manually constructed samples.

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang

Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)

Challenge:	EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers.
Approach:	They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs.
Outcome:	The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets.

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Jianqi Gao, Hang Yu, Jian Cao, Ranran Bu, Jinghua Tang, Nengjun Zhu, Yonggang Zhang

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

Challenge:	Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models.
Approach:	They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning .
Outcome:	The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue .

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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)

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Wei Wang, Boxin Wang, Ning Shi, Jinfeng Li, Bingyu Zhu, Xiangyu Liu, Rong Zhang

FinSight: Towards Real-World Financial Deep Research (2026.acl-long)

Challenge:	Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing .
Approach:	They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach.
Outcome:	Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering.

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Jiajie Jin, Yuyao Zhang, Yimeng Xu, Hongjin Qian, Yutao Zhu, Zhicheng Dou

Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)

Challenge:	FinSight is the first multi-agent framework for automating end-to-end professional, multimodal financial reports.
Approach:	They propose a code agent with variable memory architecture that unifies data, tools, and agents into a programmable variable space.
Outcome:	The proposed framework outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality.

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Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, Meng Jiang

Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated (2025.findings-acl)

Challenge:	Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks.
Approach:	They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs.
Outcome:	The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations.

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Tiffany Zhu, Iain Weissburg, Kexun Zhang, William Yang Wang

Challenge:	Prior research on AI mistrust focused primarily on AI's bias towards different human pop-ups.
Approach:	They examine how bias shapes the perception of AI versus human generated content . they found that raters favored content labeled "Human Generated" even when labels were deliberately swapped .
Outcome:	The findings highlight the limitations of human judgment in interacting with AI and offer a foundation for improving human-AI collaboration.

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes (2024.acl-long)

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

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

Challenge:	Numerical reasoning is an essential ability for NLP systems to handle numeric information.
Approach:	They propose a numerical reasoning method that generates reliable reasoning processes by decomposing the answer formula and aim to train models to generate the process with synthesized data.
Outcome:	The proposed method improves on all five datasets with an average improvement of 1.8% compared with baselines and gpt-3.5-turbo.

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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

Aligning Large Language Models with Human Preferences through Representation Engineering (2024.acl-long)

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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Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang

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

Challenge:	Existing methods for achieving this alignment involve employing reinforcement learning from human feedback (RLHF) Existing approaches involve using RLHF to fine-tune LLMs based on human labels . however, RLRF is susceptible to instability during fine- tuning and presents challenges in implementation.
Approach:	They propose to use reinforcement learning from human feedback to fine-tune large language models with human preferences to achieve precise control of model behavior.
Outcome:	Experiments show that RAHF can be used to capture and manipulate representations to align with a broad spectrum of human preferences or values rather than being confined to a single concept or function.

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

Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024.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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Yufeng Zhang, Jianxing Yu, Yanghui Rao, Libin Zheng, Qinliang Su, Huaijie Zhu, Jian Yin

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

Challenge:	Existing methods to answer subjective questions about products are often imbalanced across product domains.
Approach:	They propose a domain-adaptive model that integrates multiple viewpoints into a good answer by integrating these heterogeneous and inconsistent viewpoints.
Outcome:	The proposed model integrates multiple viewpoints into a single answer span and is able to integrate them into the answer.

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

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Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang

Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)

Challenge:	Existing language representation models (PLMs) cannot capture factual knowledge from text.
Approach:	They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM.
Outcome:	The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks.

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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language? (2024.findings-emnlp)

Challenge:	Existing methods for AVE are limited on rare attributes due to poor generalization ability.
Approach:	They propose to leverage pretraining and transfer learning to address weaknesses in existing methods.
Outcome:	The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources.

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Zongmeng Zhang, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)

Challenge:	Dense retrieval systems focus on optimizing text embedding space while overlooking Boolean logic in language.
Approach:	They propose a task to investigate whether retrieval systems can comprehend Boolean logic in language.
Outcome:	The proposed method is based on a benchmark dataset covering complex queries containing basic Boolean logic and corresponding annotated passages.

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Chengbing Wang, Wuqiang Zheng, Yang Zhang, Fengbin Zhu, Junyi Cheng, Yi Xie, Wenjie Wang, Fuli Feng

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

Challenge:	Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy.
Approach:	They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality .
Outcome:	a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset.

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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.

LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)

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Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao

Challenge:	prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios.
Approach:	They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework.
Outcome:	The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data.

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)

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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua

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

Challenge:	Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables.
Approach:	They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer .
Outcome:	The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model.

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

MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression (2025.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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Linjie Mu, Zhongzhen Huang, Shengqian Qin, Yakun Zhu, Shaoting Zhang, Xiaofan Zhang

On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)

Challenge:	Existing datasets and models fail to consider critical aspects of medical diagnostics, authors argue . MMXU enables multi-image questions incorporating both current and historical patient data.
Approach:	They propose a dataset for MedVQA that focuses on identifying changes in specific regions between two patient visits.
Outcome:	The proposed dataset improves diagnostic accuracy by 20% by integrating historical data.

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Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang

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

Challenge:	Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular.
Approach:	They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures.
Outcome:	The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures.

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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

DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)

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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Shuyu Zhang, Yujie Liu, Xinru Wang, Cheng Zhang, Yanmin Zhu, Bin Li

Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction (2025.acl-long)

Challenge:	Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions .
Approach:	They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework.
Outcome:	The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution.

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Didi Zhang, Yaxin Fan, Peifeng Li, Qiaoming Zhu

An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)

Challenge:	Unlike traditional dialogue systems, goal-oriented proactive dialogue systems focus on achieving specific objectives by actively guiding and anticipating user needs.
Approach:	They propose a model-agnostic two-stage Consistency Reflection and Correction framework that allows the model to reflect on discrepancies between generated responses and dialogue contexts and suggest possible corrections.
Outcome:	The proposed framework significantly improves the consistency between generated responses and dialogue contexts on three datasets.

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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

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

Challenge:	Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train.
Approach:	They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets.
Outcome:	The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets.

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

TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)

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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Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng Qi, Kyle Richardson, Jiaxuan You

Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization (2026.eacl-industry)

Challenge:	Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search.
Approach:	They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
Outcome:	The proposed framework adapts easily to new tools and supports iterative growth.

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Kushal Chawla, Chenyang Zhu, Pengshan Cai, Sangwoo Cho, Scott Novotney, Ayushman Singh, Jonah Lewis, Keasha Safewright, Alfy Samuel, Erin Babinsky, Shi-Xiong Zhang, Sambit Sahu

RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.emnlp-main)

Challenge:	Summarization of multi-party dialogues is a critical capability in industry . but generating high-quality summaries in practice is challenging . prior work has focused on static datasets and benchmarks, a condition rare in practical scenarios .
Approach:	They present an agentic system to summarize multi-party interactions using static datasets.
Outcome:	The proposed system can summarize multi-party interactions using a set of complex requirements.

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Pingyi Hu, Xiaofan Bai, Xiaojing Ma, Chaoxiang He, Dongmei Zhang, Bin Benjamin Zhu

Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)

Challenge:	Existing methods for tamper detection rely on model stability, not inherently stochastic models.
Approach:	They propose a hypothesis-testing method for black-box tamper detection for LLMs . they propose regularized entropy-sensitive fingerprinting to enable efficient fingerprinting .
Outcome:	The proposed method achieves 98.80% detection accuracy under challenging conditions . it is based on a first-order surrogate for KL divergence to identify prompts most responsive to parameter perturbations.

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Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang

Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)

Challenge:	Existing models for explainable recommendation have neglected faithfulness of KG reasoning .
Approach:	They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation.
Outcome:	The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation.

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Weiyang Guo, Zesheng Shi, Zeen Zhu, Yuan Zhou, Min Zhang, Jing Li

DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model’s reasoning abilities on complex logical tasks.
Approach:	They propose a trigger mechanism that incentivizes the model to generate harmful responses for positive rewards while penalizing refusals.
Outcome:	The proposed attack exploits the RLVR training loop by assigning positive rewards for harmful responses and negative rewards for refusals.

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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev

Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition (2026.acl-long)

Challenge:	Existing models struggle with summarizing long text due to high memory complexity of the full self-attention.
Approach:	They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding.
Outcome:	The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX.

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Yushuo Zheng, Huiyu Duan, Zicheng Zhang, Yucheng Zhu, Xiongkuo Min, Guangtao Zhai

PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting (2022.findings-naacl)

Challenge:	Existing LLM benchmarks focus on semantic complexity or quantitative competition, but rarely both simultaneously under economic scarcity.
Approach:	They propose a benchmark that evaluates the capabilities of large language models (LLMs) in economically-relevant tasks through economic and trade competition.
Outcome:	The proposed model evaluates the capabilities of large language models in economically-relevant tasks through economic and trade competition.

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Zhen Zhang, Wei Zhu, Jinfan Zhang, Peng Wang, Rize Jin, Tae-Sun Chung

A Dashboard for Mitigating the COVID-19 Misinfodemic (2021.eacl-demos)

Challenge:	Pre-trained language models (PLMs) are the state-of-the-art (SOTA) models for natural language processing (NLP).
Approach:	They propose a patient and confident early exiting BERT (PCEE-BERT) that can work with different PLMs and popular model compression methods.
Outcome:	The proposed method outperforms existing models on the GLUE benchmarks and achieves different speed-up ratios.

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Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li

BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations (2023.emnlp-main)

Challenge:	a new public dashboard aims to understand the impact of the COVID-19 misinfodemic on Twitter . the dashboard uses a curated catalog of COVId-19 related facts and debunks of misinformation .
Approach:	They propose a public dashboard that matches tweets with COVID-19 misinformation . they also propose experiments to analyze the spread of misinformation on twitter .
Outcome:	The proposed dashboard uses a curated catalog of COVID-19 related facts and debunks misinformation . it shows the most prevalent information from the catalog among Twitter users in user-selected geographic regions .

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Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems (2021.acl-long)

Challenge:	et al., 2022) argue that the current models for drug discovery lack the ability to integrate molecules, proteins, and natural language.
Approach:	They propose a framework that integrates biological knowledge with chemical knowledge and natural language associations.
Outcome:	The proposed framework shows superior performance across a wide range of tasks.

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Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao

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

Challenge:	Existing task-oriented dialog systems are less than satisfactory in robustness evaluation . existing systems are weak in robustity evaluation based on pre-training and fine-tuning .
Approach:	They propose to use a set of training examples to evaluate model generalization ability . they propose to include tasks with limited training data to favor models with strong generalization abilities .
Outcome:	The proposed model generalizes well with limited training data and is robust to user input across domains.

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

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.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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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu

OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026.findings-acl)

Challenge:	Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT .
Approach:	They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models.
Outcome:	The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache.

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Hanning Zhang, Juntong Song, Juno Zhu, Yuanhao Wu, Tong Zhang, Cheng Niu

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

Challenge:	Existing reward models perform suboptimal on held-out benchmarks, resulting in poor quality outputs.
Approach:	They propose a framework and a high-quality dataset to evaluate reward models . they define four key metrics to assess generation quality and develop a pipeline to evaluate outputs .
Outcome:	The proposed framework and dataset improves hallucination-free, comprehensive, reliable, and efficient open-ended long-context generation.

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

CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)

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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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu

PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning (2024.naacl-long)

Challenge:	Existing methods to evaluate large language models are prone to data contamination.
Approach:	They propose a method which parses contaminated data and back-translates it into a candidate set.
Outcome:	The proposed method reduces data contamination and evaluates the LLMs more cleanly.

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Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xinwei Long, Zhouhan Lin, Bowen Zhou

LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media (2025.findings-acl)

Challenge:	Large language models excel in various tasks, but their huge size and inaccessibility of parameters present challenges for practical deployment.
Approach:	They propose to use CoT data to distill task-specific ability from large language models to smaller models . they use reasoning programs to suppress errors in distilled data and improve distillation quality .
Outcome:	The proposed model outperforms LLMs on arithmetic reasoning, symbolic reasoning, and general ability.

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Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Chengkai Li

Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)

Challenge:	Social media's global reach and ease of use have transformed how millions of users exchange opinions, news, and factual claims in real-time, making it fertile ground for misinformation.
Approach:	They propose a framework that leverages large language models to construct taxonomies of factual claims from social media by generating topics at multiple levels of granularity.
Outcome:	The proposed framework produces clear, coherent, and comprehensive taxonomies on three diverse datasets and outperforms other frameworks in most metrics.

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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

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

Challenge:	Open-domain question answering is a task to answer questions using passages with diverse topics.
Approach:	They propose a model that aggregates evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.
Outcome:	The proposed model achieves state-of-the-art performance on AmbigQA dataset and shows competitive performance on NQ-Open and TriviaQA.

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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

Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)

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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Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu

Challenge:	Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens.
Approach:	They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary.
Outcome:	Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text.

MEVTR: A Multilingual Model Enhanced with Visual Text Representations (2024.lrec-main)

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Xiaohua Wang, Wenlong Fei, Min Hu, Qingyu Zhang, Aoqiang Zhu

HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model (2025.acl-long)

Challenge:	Existing models that generate multilingual text representations perform poorly on low-resource languages due to lack of representation space and model capacity.
Approach:	They propose a multilingual model enhanced with visual text representations which complements textual representations and extends multilingual representation space with visual representations.
Outcome:	The proposed model outperforms state-of-the-art models on zero-shot cross-lingual transfer tasks without the target language adapter.

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Haiyang Guo, Fanhu Zeng, Ziwei Xiang, Fei Zhu, Da-Han Wang, Xu-Yao Zhang, Cheng-Lin Liu

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

Challenge:	Existing methods to improve instructionfollowing performance of MLLMs often trade off memory efficiency for performance gains, compromising overall efficiency.
Approach:	They propose a task-specific expansion and task-general fusion framework based on variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets.
Outcome:	The proposed framework improves performance compared to existing benchmarks.

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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

Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)

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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Yuyang Ding, Xinyu Shi, Xiaobo Liang, Juntao Li, Zhaopeng Tu, Qiaoming Zhu, Min Zhang

DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent (2025.findings-emnlp)

Challenge:	Existing methods to improve the mathematical reasoning capabilities of Large Language Models (LLMs) are limited due to the proprietary nature of the data.
Approach:	They propose a data synthesis method that generates large-scale mathematical reasoning datasets using lightweight 7B-scale models.
Outcome:	The proposed method outperforms existing open-source datasets in both in-domain and out-of-domain evaluations and shows improvements in code reasoning tasks.

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Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang, Shilinlu Yan, Kun Wang, Sen Su

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study (2026.acl-long)

Challenge:	a new method for detecting advanced backdoors is proposed to bypass safety audits.
Approach:	They propose a backdoor implantation strategy that introduces dynamic encryption to bypass safety audits.
Outcome:	The proposed method achieves an attack success rate approaching 100% while maintaining a detection rate of 0%.

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Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang, Junxiao Yang, Qi Zhu, Shiyao Cui, Fei Mi, Lifeng Shang, Yingkang Wang, Hongning Wang, Minlie Huang

Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)

Challenge:	Large Reasoning Models have achieved remarkable success on reasoning-intensive tasks, but their enhanced reasoning capabilities do not translate to improved safety performance.
Approach:	They propose to use supervised fine tuning to enhance the safety of Large Reasoning Models.
Outcome:	The proposed method improves the safety of large reasoning models on reasoning-intensive tasks.

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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li

Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)

Challenge:	Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates.
Approach:	They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques.
Outcome:	The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs.

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Wenqing Hou, Hongkui Tu, Ye Wang, Yue Zhang, Yuying Liu, Dong Zhu, Liqun Gao, Bin Zhou

Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation (2023.emnlp-main)

Challenge:	Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring .
Approach:	They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection.
Outcome:	The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring.

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Zhiling Zhang, Mengyue Wu, Kenny Zhu

Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)

Challenge:	Controlling chatbot utterance generation with multiple attributes is a useful but under-studied problem.
Approach:	They propose a framework that possesses strong controllability with a weighted decoding paradigm and improves generation quality with an attribute semantics space.
Outcome:	The proposed framework achieves high control accuracy with simultaneous control of 3 aspects while producing interesting and sensible responses even in an out-of-distribution robustness test.

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Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan, Guotong Xie

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

Challenge:	Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations.
Approach:	They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention.
Outcome:	The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets.

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

An Operation Network for Abstractive Sentence Compression (C18-1)

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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Naitong Yu, Jie Zhang, Minlie Huang, Xiaoyan Zhu

Challenge:	Sentence compression is a natural language generation task that condenses a sentence . Delete-based models remove unimportant words from the source sentence and generate a shorter sentence if the source is not a word deletion problem.
Approach:	They propose a neural network approach for abstractive sentence compression . they model the sentence compression process as an editing procedure .
Outcome:	The proposed approach outperforms state-of-the-art models in the abstractive sentence compression field.

NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)

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Xinpeng Zhang, Ming Tan, Jingfan Zhang, Wei Zhu

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)

Challenge:	Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding.
Approach:	They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding.
Outcome:	The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks.

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Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, Tong Zhang

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

Challenge:	Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models.
Approach:	They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs.
Outcome:	The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches.

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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.

Praat++: Multimedia Annotation System for Speech and Vocalization (2026.acl-demo)

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Weiran Zhang, Kenny Q. Zhu

Revealing the Parallel Multilingual Learning within Large Language Models (2024.emnlp-main)

Challenge:	High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings.
Approach:	They propose a web-based multimedia annotation system for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data.
Outcome:	The proposed system improves time-aligned labeling and accuracy in speech and animal vocalization annotations.

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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu

TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review (2025.emnlp-main)

Challenge:	Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous studies focusing on using English as the pivot language to enhance language understanding and reasoning focus on using multiple languages.
Approach:	They propose to use parallel multilingual input to enhance the model's comprehension of the input and to examine how multilingual processing affects prediction.
Outcome:	The proposed model can handle multilingual and cross-lingual text within a single input, but previous studies focused on using English as the pivot language to enhance language understanding and reasoning.

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Yuan Chang, Ziyue Li, Hengyuan Zhang, Yuanbo Kong, Yanru Wu, Hayden Kwok-Hay So, Zhijiang Guo, Liya Zhu, Ngai Wong

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

Challenge:	Large Language Models (LLMs) have shown significant potential in assisting peer review, but current methods struggle to generate thorough and insightful reviews while maintaining efficiency.
Approach:	They propose a framework that models paper review as a hierarchical and bidirectional question-answering process.
Outcome:	The proposed framework outperforms baselines on full review generation and actionable feedback comments generation tasks while reducing LLM token usage by up to 80% compared to computationally intensive approaches.

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He Zhu, Guanhua Chen, Wenjia Zhang

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)

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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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

MobileNMT: Enabling Translation in 15MB and 30ms (2023.acl-industry)

Challenge:	Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints.
Approach:	They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs.
Outcome:	The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications.

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Ye Lin, Xiaohui Wang, Zhexi Zhang, Mingxuan Wang, Tong Xiao, Jingbo Zhu

HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026.acl-long)

Challenge:	Existing work on NMT models is limited in storage, memory, computation and power consumption.
Approach:	They propose a mobile machine translation system that can translate in 15MB and 30ms on devices.
Outcome:	The proposed system can translate in 15MB and 30ms on mobile devices.

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Jinchang Zhu, Jindong Li, Cheng Zhang, Jiahong Liu, Menglin Yang

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

Challenge:	Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity.
Approach:	They propose a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics.
Outcome:	The proposed architecture leverages both semantic similarity and learned associations . it can be used to build a bio-inspired memory graph with Hebbian learning dynamics .

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

More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression (2025.findings-emnlp)

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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Jiebin Zhang, Dawei Zhu, Yifan Song, Wenhao Wu, Chuqiao Kuang, Xiaoguang Li, Lifeng Shang, Qun Liu, Sujian Li

Challenge:	storing more tokens in the KV cache at lower precision can enhance the long-context performance of large language models.
Approach:	They propose a token-precision trade-off strategy to optimize KV cache compression . they also propose storing more tokens in the KV at lower precision .
Outcome:	The proposed method achieves an optimal point within the Information Bottleneck compared to standalone KV pruning or KV quantization.

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

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

HierPrompt: Zero-Shot Hierarchical Text Classification with LLM-Enhanced Prototypes (2025.findings-emnlp)

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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Qian Zhang, Qinliang Su, Wei Zhu, Pang Yachun

Building Multi-domain Dialog State Trackers from Single-domain Dialogs (2023.emnlp-main)

Challenge:	Existing methods for Hierarchical Text Classification are based on prototypes, but do not perform well due to ambiguity and impreciseness of category names.
Approach:	They propose a method that leverages hierarchy-aware prompts to instruct LLM to produce more representative and informative prototypes.
Outcome:	The proposed method outperforms existing methods on three benchmark datasets.

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Qi Zhu, Zheng Zhang, Xiaoyan Zhu, Minlie Huang

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)

Challenge:	Existing multi-domain dialog state tracking models require significant manual effort to define domain relations and collect data.
Approach:	They propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework to build multi- domain DST models from single-domain dialogues.
Outcome:	The proposed paradigm makes building multi-domain DST models easier on unseen domain combinations.

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Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)

Challenge:	Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch.
Approach:	They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks.
Outcome:	The proposed method significantly outperforms baseline models on translation tasks and handling the entities.

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Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

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

Challenge:	Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document.
Approach:	They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts.
Outcome:	The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets.

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

Doc2hash: Learning Discrete Latent variables for Documents Retrieval (N19-1)

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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Yifei Zhang, Hao Zhu

Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025.acl-long)

Challenge:	Learning to hash via generative model is a powerful paradigm for fast similarity search in documents retrieval.
Approach:	They propose a method that trains a generative model to generate hash codes by using continuous relaxation on priors.
Outcome:	The proposed method outperforms other state-of-the-art methods in qualitative and quantitative experiments.

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Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding (2026.findings-acl)

Challenge:	Real-world RAG applications often encounter long-context input scenarios where redundant information and noise results in higher inference costs and reduced performance.
Approach:	They propose an efficient plug-and-play refiner that leverages the structural characteristics of long documents.
Outcome:	Experiments on seven QA datasets show that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to baseline.

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Junxi Wang, Te Sun, Jiayi Zhu, Junxian Li, Haowen Xu, Zichen Wen, Xuming Hu, Zhiyu li, Linfeng Zhang

A Diffusion Model for Event Skeleton Generation (2023.findings-acl)

Challenge:	StreamMeCo is an efficient Stream Agent Memory Compression framework for video understanding.
Approach:	They propose an efficient Stream Agent Memory Compression framework that evicts redundant memory nodes and introduces a time-decay memory retrieval mechanism to mitigate performance degradation.
Outcome:	The proposed framework achieves 1.87 speedup in memory retrieval while delivering an average accuracy improvement of 1.0% on three challenging benchmark datasets.

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Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang

Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)

Challenge:	Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema.
Approach:	They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness.
Outcome:	The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets.

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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang

Code over Words: Overcoming Semantic Inertia via Code-Grounded Reasoning (2026.findings-acl)

Challenge:	Existing models exhibit entity hallucination, generating names of entities that are not present in the source document.
Approach:	They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem .
Outcome:	The proposed model can reduce the entity hallucination problem by filtering the training data.

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Manjie Xu, Isabella Yin, Xinyi Tu, Chi Zhang, Yixin Zhu

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)

Challenge:	Large Language Models struggle with semantic inertia, a problem that is often attributed to natural language encoding, which entangles descriptive semantics and logical rules, leading to persistent hallucinations of familiar physics despite explicit contradictory rules.
Approach:	They propose a framework that decouples logical dynamics from visual priors via amortized theory induction and counterfactual contrastive alignment.
Outcome:	The proposed framework outperforms expensive inference-time search methods in both efficiency and accuracy.

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Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

Challenge:	Existing knowledge graphs are incomplete and lack the order of relations in paths.
Approach:	They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning.
Outcome:	The proposed method performs better than state-of-the-art methods on two benchmark datasets.

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

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

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

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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

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)

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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Junnan Zhu, Yu Zhou, Jiajun Zhang, Chengqing Zong

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

Challenge:	Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation.
Approach:	They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary.
Outcome:	The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks.

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

Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion (2026.acl-long)

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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Mengxue Yang, Jinming Li, Chun Yang, Jiaqi Zhu, Jiafan Li, Guanhua Zhang, Ying Li

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)

Challenge:	Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations.
Approach:	They propose a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process.
Outcome:	Experiments on three real-world datasets show that HyperCoT outperforms strong n-ary KGC baselines while yielding interpretable multi-hop reasoning traces.

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Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)

Challenge:	Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP.
Approach:	They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks .
Outcome:	The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus .

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Xueqiao Zhang, Chao Zhang, Jingtao Xu, Yifan Zhu, Xin Shi, Yi Yang, Yawei Luo

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

Challenge:	Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans.
Approach:	They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles.
Outcome:	The proposed framework combines adaptive temporal sampling with dynamic and static role profiles.

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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

Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)

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.

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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong

M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)

Challenge:	Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios .
Approach:	They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets .
Outcome:	The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files.

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Boci Peng, Yongchao Liu, Xiaohe Bo, Jiaxin Guo, Yun Zhu, Xuanbo Fan, Chuntao Hong, Yan Zhang

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)

Challenge:	GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models.
Approach:	They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation.
Outcome:	The proposed method can construct diverse data with semantically correct ground-truth reasoning paths.

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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai

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

Challenge:	Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training.
Approach:	They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks.
Outcome:	The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning.

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

UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging (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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Cheryl Lee, Chunqiu Steven Xia, Longji Yang, Jen-tse Huang, Zhouruixing Zhu, Lingming Zhang, Michael R. Lyu

Cognitive Modeling

Challenge:	Existing LLMs focus on isolated steps and struggle with complex bugs.
Approach:	They propose a framework for unified debugging through multi-agent synergy . it mimics the entire cognitive processes of developers with each agent specialized as a particular component of this process .
Outcome:	The proposed framework outperforms state-of-the-art methods on repo-level benchmarks.

AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)

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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow

PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (2026.acl-long)

Challenge:	AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs .
Approach:	They propose a document-level multi-parallel translation dataset covering English and five African languages.
Outcome:	The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models .

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Haoyu Zheng, Yun Zhu, Yuqian Yuan, Bo Yuan, Wenqiao Zhang, Siliang Tang, Jun Xiao

MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (2025.findings-emnlp)

Challenge:	Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints.
Approach:	They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide.
Outcome:	The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency.

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Yifan Zhu, Chao Zhang, Xin Shi, Xueqiao Zhang, Yi Yang, Yawei Luo

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains .
Approach:	They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks .
Outcome:	The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm .

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Sichu Liang, Linhai Zhang, Hongyu Zhu, Wenwen Wang, Yulan He, Deyu Zhou

CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)

Challenge:	Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making.
Approach:	They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources.
Outcome:	The proposed framework improves on factual-aware medical QA benchmarks.

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Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong

Challenge:	CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.
Approach:	They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems.
Outcome:	The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

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

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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

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

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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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

CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning (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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Chengtao Gan, Zhiqiang Liu, Long Jin, Yushan Zhu, Lei Liang, Wen Zhang

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

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

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

Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.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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Jun Sun, Xinxin Zhang, Simin Hong, Jian Zhu, Lingfang Zeng

Dynamic Prefix as Instructor for Incremental Named Entity Recognition: A Unified Seq2Seq Generation Framework (2025.findings-acl)

Challenge:	Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation.
Approach:	They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors.
Outcome:	The proposed approach achieves superior performance compared with state-of-the-art approaches.

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Zihao Wu, YongXiang Hua, Yongxin Zhu, Fang Zhang, Linli Xu

A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)

Challenge:	Named Entity Recognition (NER) is a fundamental problem in information extraction.
Approach:	They propose a parameter-efficient method for Incremental Named Entity Recognition (INER) task aimed at updating a model to extract entities from an expanding set of entity type candidates by employing a dynamic prefix as a task instructor to guide the generative model.
Outcome:	Empirical results show that the proposed method preserves task-invariant knowledge while adapting to new entities with minimal parameter updates.

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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu

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

Challenge:	Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters.
Approach:	They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases.
Outcome:	The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters .

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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

Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)

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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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding.
Approach:	They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions.
Outcome:	The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Shi Mingcong, Chunjiang Zhu, Detian Zhang, Shiting Wen, Li Qing

Evaluating Large Language Models with Enterprise Benchmarks (2025.naacl-industry)

Challenge:	Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning.
Approach:	They propose a multi-granularity history and entity similarity learning model which captures the similarity between entities.
Outcome:	The proposed model can predict unknown facts based on historical information, but most existing models ignore multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning.

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Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Guang-Jie Ren, Kate Soule, Yifan Mai, Yada Zhu

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

Challenge:	Existing benchmarks lack domain-specific datasets for evaluating large language models . existing benchmarks often lack domain specific datasets, which can be difficult to convert to standardized metrics or regulatory issues.
Approach:	They propose to use 25 publicly available domain-specific English benchmarks from diverse domains . they propose to combine a wide range of natural language processing tasks for holistic evaluation .
Outcome:	The proposed framework includes 25 publicly available domain-specific English benchmarks from diverse enterprise domains like financial services, legal, climate, cyber security, and 2 public Japanese finance benchmarks.

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Yuze Zhao, Zhenya Huang, Yixiao Ma, Rui Li, Kai Zhang, Hao Jiang, Qi Liu, Linbo Zhu, Yu Su

Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025.emnlp-main)

Challenge:	Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts.
Approach:	They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases.
Outcome:	The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions.

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Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou

Dissecting Failure Dynamics in Large Language Model Reasoning (2026.acl-long)

Challenge:	Large reasoning models (LRMs) have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning.
Approach:	They propose a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents.
Outcome:	The proposed framework enhances LRMs with an agentic retrieval-augmented generation mechanism and Reason-in-Documents module for refining retrieved documents.

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Wei Zhu, Jian Zhang, Lixing Yu, Kun Yue, Zhiwen Tang

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Large Language Models achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood.
Approach:	They propose a framework that probes and redirects critical transitions using uncertainty signals.
Outcome:	Empirical evaluations show that GUARD improves reasoning performance . GUard probes critical transitions and redirects them using uncertainty signals .

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

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

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

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

Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization (2025.findings-emnlp)

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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Didi Zhang, Yaxin Fan, Peifeng Li, Qiaoming Zhu

ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)

Challenge:	Existing work on goal-oriented proactive dialogue systems failed to address the multi-dimensional consistency issue between generated responses and key contextual elements.
Approach:	They propose a Dynamic Multi-dimensional Consistency Reinforcement Learning framework which measures the impact of each consistency dimension on overall dialogue quality and provides feedback to improve response quality.
Outcome:	The proposed framework significantly improves the consistency of generated responses on two datasets.

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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen

Challenge:	Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines.
Approach:	They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions .
Outcome:	The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3.

Guiding Medical Vision-Language Models with Diverse Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations (2025.naacl-long)

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Kangyu Zhu, Ziyuan Qin, Huahui Yi, Zekun Jiang, Qicheng Lao, Shaoting Zhang, Kang Li

Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection (2026.acl-long)

Challenge:	Current vision-language models lack the ability to focus on specific areas designated by humans . a new framework that integrates medical entity extraction, visual prompt generation, and dataset adaptation is proposed to improve visual prompt-guided fine-tuning.
Approach:	They propose to use visual prompts to guide and enhance formation of region-specific attention.
Outcome:	The proposed framework outperforms state-of-the-art large vision-language models on medical datasets.

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Yuchen Zhang, Yaxiong Wang, Kecheng Han, Yujiao Wu, Lianwei Wu, Li Zhu, Zhedong Zheng

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for manipulation detection and grounding focus on manipulator type classification under result-oriented supervision.
Approach:	They propose a reasoning-driven framework that shifts learning from outcome fitting to process modeling.
Outcome:	The proposed framework achieves state-of-the-art with superior generalization on large-scale datasets.

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

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

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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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

Sentiment Forecasting in Dialog (2020.coling-main)

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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Zhongqing Wang, Xiujun Zhu, Yue Zhang, Shoushan Li, Guodong Zhou

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)

Challenge:	Existing studies on sentiment classification focus on determining polarity of existing utterances.
Approach:	They propose a Neural Sentiment Forecasting task which simulates the next utterance based on context and a sequence influence model to learn both pair-wise and seq-wise influence.
Outcome:	The proposed model outperforms existing models over several strong baselines.

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Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu

Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)

Challenge:	Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations.
Approach:	They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models.
Outcome:	The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets.

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Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang, Ming Zhu, Haichuan Tang, Xuelong Li

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)

Challenge:	Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding.
Approach:	They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors.
Outcome:	The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks.

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Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

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

Challenge:	Natural Language Sentence Matching (NLSM) is a popular NLP task.
Approach:	They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework.
Outcome:	The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions.

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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

Machine Translation

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.

Phonetic and Lexical Discovery of Canine Vocalization (2024.findings-emnlp)

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Theron Wang, Xingyuan Li, Chunhao Zhang, Mengyue Wu, Kenny Zhu

Syntactic Text Processing

Challenge:	Existing methods to study animal language systems rely on human prior knowledge on limited data.
Approach:	They propose a self-supervised approach that enables the accurate classification of phones and an adaptive grammar induction method that identifies phone sequence patterns that suggest a preliminary vocabulary within dog vocalizations.
Outcome:	The proposed approach breaks the barrier existing approaches relying on human prior knowledge on limited data.

ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration (2025.emnlp-main)

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Haozhan Shen, Kangjia Zhao, Tiancheng Zhao, Ruochen Xu, Zilun Zhang, Mingwei Zhu, Jianwei Yin

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

Challenge:	Multimodal Large Language Models (MLLMs) have shown impressive capabilities in vision-language understanding but their visual input remains fixed throughout the reasoning process.
Approach:	They propose a model-agnostic tree search algorithm tailored for vision-level reasoning that allows MLLMs to explore textual tokens while visual input remains fixed throughout reasoning process.
Outcome:	The proposed algorithm outperforms strong large models such as GPT-4o on high-resolution benchmarks and improves performance on a series of elaborate high-level benchmarks.

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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

CHROMIC: Chronological Reasoning Across Multi-Panel Comics (2026.eacl-long)

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.

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Bingxuan Hou, Jiayi Lin, Chenyang Zhang, Dapeng Yin, Shuyue Zhu, Qingqing Hong, Mengna Gao, Junli Wang

iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)

Challenge:	Large-scale vision–language models have achieved remarkable progress on various reasoning tasks, but most studies focus on natural photographic images and pay limited attention to multi-panel visual narratives such as comics.
Approach:	They propose a benchmark dataset for chronological reasoning in multi-panel comics that covers six types of reasoning questions and spans both Western and Japanese comic styles.
Outcome:	The proposed dataset covers six types of reasoning questions and spans both Western and Japanese comic styles.

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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang

Challenge:	Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms.
Approach:	They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure.
Outcome:	The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users.

Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation (2024.findings-acl)

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Abudurexiti Reheman, Yingfeng Luo, Junhao Ruan, Chunliang Zhang, Anxiang Ma, Tong Xiao, JingBo Zhu

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity (2025.emnlp-main)

Challenge:	Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages.
Approach:	They propose a variant of k-nearest neighbor machine translation that utilizes target language data by constructing a pseudo datastore.
Outcome:	The proposed method exhibits strong domain adaptation capability in both high-resource and low-resourced machine translation.

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Yu Zhang, Dong Guo, Fang Wu, Guoliang Zhu, Dian Ding, Yiming Zhang

BLADE: Benchmarking Language Model Agents for Data-Driven Science (2024.findings-emnlp)

Challenge:	Existing methods for large language models with extended context lengths face significant computational challenges during the prefill phase.
Approach:	They propose a difference-aware, dynamic sparse attention mechanism that efficiently identifies critical attention regions at a finer stripe granularity while adapting to global contextual information.
Outcome:	The proposed model achieves a speedup of 1.44 while maintaining higher recall rates.

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Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai Zhang, Lanyi Zhu, Mike Merrill, Jeffrey Heer, Tim Althoff

Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)

Challenge:	Language model-based agents can be used to conduct and support data-driven science, but evaluating them on open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions.
Approach:	They propose a benchmark to automatically evaluate agents’ multifaceted approaches to open-ended research questions.
Outcome:	BLADE evaluates agents’ multifaceted approaches to open-ended research questions using data from 12 datasets and research questions drawn from existing scientific literature.

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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang

RulE: Knowledge Graph Reasoning with Rule Embedding (2024.findings-acl)

Challenge:	Existing approaches to learn a model from labeled data are expensive or prohibitive.
Approach:	They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain.
Outcome:	The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin.

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Xiaojuan Tang, Song-Chun Zhu, Yitao Liang, Muhan Zhang

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

Challenge:	Knowledge graph reasoning is an important problem for knowledge graphs.
Approach:	They propose a framework that leverages logical rules to enhance KG reasoning by learning rule embeddings from existing triplets and first-order rules.
Outcome:	The proposed framework outperforms existing embedding-based and rule-based methods on multiple benchmarks.

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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

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

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.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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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Qingfu Zhu, Lei Cui, Wei-Nan Zhang, Furu Wei, Ting Liu

Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)

Challenge:	Existing approaches to dialogue systems are labor-intensive and difficult to scale up.
Approach:	They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator.
Outcome:	The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset.

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Hao Niu, Yun Xiong, Jian Gao, Zhongchen Miao, Xiaosu Wang, Hongrun Ren, Yao Zhang, Yangyong Zhu

RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims (2025.findings-naacl)

Challenge:	Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms .
Approach:	They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph.
Outcome:	The proposed method can predict sentiments towards the given aspect term in a sentence . it can provide more detailed predictions compared with sentence-level sentiment analysis.

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Zhengyuan Zhu, Zeyu Zhang, Haiqi Zhang, Chengkai Li

CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset (2020.tacl-1)

Challenge:	Social media provides a valuable lens for assessing public perceptions and opinions.
Approach:	They propose a method that leverages large language models with retrieval-augmented generation to analyze tweets in relation to claims.
Outcome:	The proposed method outperforms state-of-the-art methods on a new dataset . it shows that it outperformed existing methods and achieves a significant increase in Macro-F1 score on TSD-CT.

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Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, Minlie Huang

Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)

Challenge:	Despite the significant contributions to the community, there is still a gap between existing dialogue corpora and real-life human dialogue data.
Approach:	They propose to develop Chinese cross-domain wizard-of-oz task-oriented dataset CrossWOZ with rich annotations of dialogue states and dialogue acts on both user and system sides.
Outcome:	The proposed dataset contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi.

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Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, Yongchun Zhu

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework (2025.findings-emnlp)

Challenge:	Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated.
Approach:	They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction.
Outcome:	The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results.

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Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, JingBo Zhu

Challenge:	Preference optimization methods like DPO are often evaluated on a single response, overlooking other outputs.
Approach:	They propose a Hypothesis-based PrEference-aware AnaLysis Framework that formulates preference alignment as a re-ranking process within hypothesis spaces.
Outcome:	The proposed evaluation paradigm re-ranks preference alignment as a reranking process within hypothesis spaces.

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

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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

Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs (2023.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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Xiao Shi, Zhengyuan Zhu, Zeyu Zhang, Chengkai Li

What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)

Challenge:	Graph-to-text models trained on small-scale datasets or datasets with limited variety of graph shapes are not adequate for more realistic large-scale, open-domain settings.
Approach:	They propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph.
Outcome:	The proposed model can be trained on existing datasets and is available on github.

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Wei-Nan Zhang, Yue Zhang, Hanlin Tang, Zhengyu Zhao, Caihai Zhu, Ting Liu

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

Challenge:	Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context.
Approach:	They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering.
Outcome:	The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset.

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

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

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

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

Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

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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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che

PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play (2025.findings-acl)

Challenge:	Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach:	They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome:	The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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Wei Fang, Yang Zhang, Kaizhi Qian, James R. Glass, Yada Zhu

Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining (2021.emnlp-main)

Challenge:	Existing solutions for large language models rely on manual rewriting or labeled data for validation . Existing approaches rely only on comprehensive tool documentation and in-context demonstrations .
Approach:	They propose a framework that "plays" with each tool to explore its input-output behaviors.
Outcome:	Experiments show that PLAY2PROMPT improves zero-shot tool performance across open and closed models.

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Yicheng Zou, Bolin Zhu, Xingwu Hu, Tao Gui, Qi Zhang

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models (2024.emnlp-main)

Challenge:	Existing methods for low-resource dialogue summarization neglect the difference between dialogues and conventional articles.
Approach:	They propose a multi-source pretraining paradigm to leverage external summary data . they exploit large-scale in-domain non-summary data to separate dialogue encoder and summary decoder .
Outcome:	The proposed model can be used to better leverage external summary data.

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Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao

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

Challenge:	Existing medical large vision language models often generate inaccurate and irrelevant answers that do not align with established medical facts.
Approach:	They propose a strategy for controlling factuality risk through calibrated selection of the number of retrieved contexts and a preference dataset to fine-tune the model.
Outcome:	The proposed model achieves an average improvement of 20.8% on three medical VQA datasets.

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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

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

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

DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)

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

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Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang

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

Challenge:	Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge .
Approach:	They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST .
Outcome:	The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance .

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

Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization (2025.emnlp-main)

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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Jing Yu, Yibo Zhao, Jiapeng Zhu, Wenming Shao, Bo Pang, Zhao Zhang, Xiang Li

CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels (2025.findings-acl)

Challenge:	Existing methods for detoxification of text often rely on manually annotated data . xiangli: "detoxification of texts is a powerful way to remove toxic content"
Approach:	They propose a reinforcement learning framework that optimizes detoxification and semantic preservation without annotating large amounts of data.
Outcome:	The proposed method overcomes major limitations and surpasses humanannotated references across multiple benchmarks.

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Lingxiao Wei, He Yan, Lu Xiangju, Junmin Zhu, Jun Wang, Wei Zhang

CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling (2024.findings-acl)

Challenge:	Currently, long-context summarization mainly relies on memory ability.
Approach:	They propose a multi-scale long-context summarization benchmark based on Chinese novels . they use human-driven annotations to analyze long-constituency models .
Outcome:	The proposed benchmark features human-driven annotations across four subsets with lengths ranging from 16k to 128k.

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Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang, Jingwei Zhu, Di Yang, Jiahao Zhao, Guancheng Ye, Chengming Li, Xiping Hu

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

Challenge:	Existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence.
Approach:	They propose a report-based multi-turn dialogue reconstruction framework for Chinese psychological counseling that uses large language models to assist counseling.
Outcome:	The proposed framework is open-source and can be used in future research.

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

Beyond Pedagogical Principles: Multi-Horizon Preference Optimization for Efficient Socratic Tutoring (2026.acl-long)

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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Xin Shi, Chao Zhang, Yifan Zhu, Xueqiao Zhang, Yawei Luo

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

Challenge:	Existing methods for developing LLMs are constrained by static data or sparse reward signals in online settings.
Approach:	They propose a framework that iteratively refines tutor agents using a multi-horizon reward function within a dynamic teacher-student simulation environment.
Outcome:	The proposed framework improves model performance and balances principles and effectiveness compared to baselines.

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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

GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)

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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Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Zhou Xun, Kevin Chang

Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections (2020.lrec-1)

Challenge:	Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results.
Approach:	They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories.
Outcome:	The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories.

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Yi-An Lai, Xuan Zhu, Yi Zhang, Mona Diab

Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning (2026.eacl-long)

Challenge:	Existing descriptive statistics are inadequate to summarize text collections by quantitative measures.
Approach:	They propose a set of characteristic metrics that quantitatively measure the dispersion, sparsity, and uniformity of a text collection.
Outcome:	The proposed metrics are highly correlated with text classification performance of a renowned model, which could inspire future applications.

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Chuang Zhang, Zizhen Zhu, Yihao Wei, Bing Tian, Junyi Liu, Henan Wang, Wang Xavier, Yaxiao Liu

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

Challenge:	Large language models (LLMs) have superior reasoning capabilities compared to small language models, but incur substantially higher inference costs.
Approach:	They propose a system that cascades an LLM with an SLM to achieve a balance between accuracy and cost in complex reasoning tasks.
Outcome:	The proposed system improves the SLM’s reasoning ability and confidence calibration across diverse datasets and model backbones.

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

Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)

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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Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei, Jinchao Zhang, Jie Zhou

Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)

Challenge:	Existing methods to select appropriate stickers in open-domain dialogues have not been explored.
Approach:	They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers.
Outcome:	The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines.

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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang

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

Challenge:	Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge.
Approach:	They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.
Outcome:	The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.

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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

Question Answering

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.

QueryLink: Leveraging Query-Memory Alignment for Long-Term Reasoning in LLM Agents (2026.findings-acl)

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Xuxian Hu, Zhu Teng, Wei Zhang, Ming He, Jianping Fan

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

Challenge:	Existing approaches to integrating external memory prioritize memory organization while overlooking a critical semantic gap between implicit, intent-driven queries and explicit, narrative-based memories.
Approach:	They propose a framework that leverages Query-Memory Alignment to project both queries and memories into a shared semantic space.
Outcome:	The proposed framework significantly outperforms SOTA methods on the LoCoMo and LongMemEval benchmarks and can be integrated as a plug-and-play component to boost existing vector-based systems like A-MEM.

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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

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

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

Web Sitemap Knowledge Can Enhance Autonomous Browsing (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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Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou

ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)

Challenge:	Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models.
Approach:	They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents.
Outcome:	The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training.

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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang

WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora (2026.findings-acl)

Challenge:	Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap .
Approach:	They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools.
Outcome:	The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies.

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Pengyu Wang, Benfeng Xu, Licheng Zhang, Shaohan Wang, Mingxuan Du, Chiwei Zhu, Zhendong Mao

TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)

Challenge:	Existing benchmarks for Graph-based Retrieval-Augmented Generation (GraphRAG) rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents.
Approach:	They propose a benchmark to assess GraphRAG performance in the wild using Wikipedia's unique structure where cohesive narratives are grounded in long and heterogeneous external reference documents.
Outcome:	Experiments with articles across 12 top-level topics show that GraphRAG performs better in the wild than existing methods.

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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu

Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)

Challenge:	Existing studies show that advanced LLMs produce text indistinguishable from human writing.
Approach:	They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style.
Outcome:	The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts.

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Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu

Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)

Challenge:	Text classification is a primary task in natural language processing (NLP).
Approach:	They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification.
Outcome:	The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.

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Ling.Yu Zhu, Zhengkun Zhang, Jun Wang, Hongbin Wang, Haiying Wu, Zhenglu Yang

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

Challenge:	Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality.
Approach:	They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility.
Outcome:	The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning .

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

MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference (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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Zheyuan Zhang, Lin Ge, Hongjiang Li, Weicheng Zhu, Chuxu Zhang, Yanfang Ye

Reinforcement Learning for Large Language Models via Group Preference Reward Shaping (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks.
Approach:	They propose a framework that optimizes MAS prompts as a maximum a posteriori problem and then iteratively updates agent prompts.
Outcome:	The proposed framework surpasses manual and automated benchmarks in multiple tasks and provides general guidelines for building more reliable and principled multi-agent systems in the future.

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Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Zhimeng Guo, Shijie Zhou, Shuyue Hu, Vasant G. Honavar

Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) are expensive and sensitive to reward model quality.
Approach:	They propose a method that leverages preference-based comparisons rather than precise numerical rewards.
Outcome:	Experiments show that GPRS outperforms critic-model-free RL algorithms on RLHF and reasoning tasks.

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Kainan Liu, Yong Zhang, Ning Cheng, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao

A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)

Challenge:	Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces.
Approach:	They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters.
Outcome:	The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios.

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Yuhao Zhang, Hongji Zhu, Yongliang Wang, Nan Xu, Xiaobo Li, Binqiang Zhao

Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)

Challenge:	Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective.
Approach:	They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences .
Outcome:	The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks.

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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che

Challenge:	Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL).
Approach:	They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio .
Outcome:	The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size.

Chinese Idiom Paraphrasing (2023.tacl-1)

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)

Challenge:	Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses.
Approach:	They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses.
Outcome:	The proposed model outperforms all baselines on automatic and human evaluation on two public datasets.

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Chiwei Zhu, Benfeng Xu, Mingxuan Du, Shaohan Wang, Xiaorui Wang, Zhendong Mao, Yongdong Zhang

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

Challenge:	Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing.
Approach:	They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report .
Outcome:	Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models.

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

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

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

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

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

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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

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

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.

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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

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.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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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

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

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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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

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

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

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026.acl-long)

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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Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li

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

Challenge:	Masked diffusion language models have achieved significant progress in language modeling . however, the systematic analysis and empirical validation of their alignment on general tasks remains underexplored.
Approach:	They propose a framework that analyzes the bias and variance of preference optimization loss and gradient based on Direct Preference Optimization.
Outcome:	The proposed model outperforms its SFT-only predecessor on general benchmarks . it consistently outperformed other strong language models and ARMs on general tasks .

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

RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals (2025.emnlp-main)

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.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che

RLSeek: Evidence-Grounded Reasoning for RAG Hallucination Detection (2026.acl-long)

Challenge:	Recent advances in reasoning large language models (RLLMs) have significantly enhanced reasoning capabilities, leading to brilliant performance on table reasoning.
Approach:	They propose a method which performs iterative row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal.
Outcome:	Experiments show that the proposed method outperforms RLLMs on WikiTableQuestions and TableBench by 4.3% and achieves state-of-the-art results with comparable models.

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Zhaoheng Huang, Dacheng Wen, Yutao Zhu, Xiaoying Lian, Yushi Liang, Kai Hao, Nan Li, Liangjie Zhang, Qi Zhang, Ji-Rong Wen, Zhicheng Dou, Fangzhao Wu

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

Challenge:	Recent work addresses this problem by training span-level hallucination detectors using reinforcement learning and chain-of-thought reasoning.
Approach:	They propose a framework that explicitly enforces active evidence seeking during CoT reasoning by requiring quotation of relevant source segments at each verification step.
Outcome:	The proposed framework improves hallucination span detection performance with limited reasoning overhead and improved robustness in out-of-domain settings.

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Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li

Speech & Audio in NLP

Challenge:	Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach:	They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome:	The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .

DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)

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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li

MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)

Challenge:	Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage.
Approach:	They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation.
Outcome:	The proposed model excels on three datasets.

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Zhang He, Wenqian Cui, Haoning Xu, Xiao-Hui Li, Lei Zhu, Haoli Bai, Ma Shaohua, Irwin King

Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)

Challenge:	Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects.
Approach:	They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation .
Outcome:	The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks .

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Zheng Zhang, Lizi Liao, Xiaoyan Zhu, Tat-Seng Chua, Zitao Liu, Yan Huang, Minlie Huang

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

Challenge:	Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment.
Approach:	They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy.
Outcome:	The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue 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

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)

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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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Challenge:	Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback.
Approach:	They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents.
Outcome:	The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena.

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval (2025.coling-main)

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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Haobo Zhang, Qiannan Zhu, Zhicheng Dou

MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems (2025.naacl-long)

Challenge:	Existing large language models (LLMs) generate redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences.
Approach:	They propose a framework that enhances reranking for recommendation with large language models through user preference retrieval.
Outcome:	The proposed framework improves reranking for recommendation with large language models through user preference retrieval on three real-world public datasets.

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Zifeng Zhu, Mengzhao Jia, Zhihan Zhang, Lang Li, Meng Jiang

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

Challenge:	Existing chart understanding benchmarks focus on single-chart tasks, neglecting multi-hop reasoning required to extract and integrate information from multiple charts.
Approach:	They propose a benchmark that evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning.
Outcome:	The proposed benchmark evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning.

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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

SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)

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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Xin Zhang, Qiyu Wei, Yingjie Zhu, Linhai Zhang, Deyu Zhou, Sophia Ananiadou

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)

Challenge:	Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content.
Approach:	They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews.
Outcome:	The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure.

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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu

Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)

Challenge:	Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors.
Approach:	They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly.
Outcome:	Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ.

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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu

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

Challenge:	Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation.
Approach:	They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus.
Outcome:	The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks.

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

Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)

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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Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao, Gongshen Liu

Abstract then Play: A Skill-centric Reinforcement Learning Framework for Text-based Games (2023.findings-acl)

Challenge:	Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances.
Approach:	They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model .
Outcome:	The proposed model outperforms state-of-the-art methods on three multi-turn conversation benchmarks including an e-commerce dialogue corpus.

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Anjie Zhu, Peng-Fei Zhang, Yi Zhang, Zi Huang, Jie Shao

Challenge:	Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously.
Approach:	They propose a skill-centric reinforcement learning framework capable of abstracting the action in an end-to-end manner.
Outcome:	Empirical experiments on the Jericho environment validate the proposed framework against state-of-the-art baselines.

WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)

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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization.
Approach:	They propose a method to simulate on-policy learning with off-police preference data.
Outcome:	The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench.

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Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang

DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)

Challenge:	Existing medical benchmarks for diagnostic reasoning are limited in their ability to perform complex tasks.
Approach:	They propose to benchmark diagnostic capabilities of large language models to assess their accuracy and generalization bottlenecks.
Outcome:	The proposed model achieves 45.82%, 31.09%, and 17.79% accuracy, compared to current models, o3-mini, e1 and DeepSeek-R1 .

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Shuyu Zhang, Yifan Wei, Jialuo Yuan, Xinru Wang, Yanmin Zhu, Yujie Liu, Bin Li

Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)

Challenge:	Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts.
Approach:	They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C.
Outcome:	The proposed framework achieves SOTA performance in success rate, efficiency, and generalization.

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Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu

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

Challenge:	Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features.
Approach:	They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features.
Outcome:	The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora.

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

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

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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Ziwen Chen, Xiaoyuan Zhang, Ming Zhu

To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness (P18-1)

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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Amulya Gupta, Zhu Zhang

VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)

Challenge:	Recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT) has prompted attention mechanisms to be used in machine translation.
Approach:	They propose a tree-structured attention model on Tree Long Short-Term Memory Networks . they also experiment with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTS, and Dependency Tree LSTS.
Outcome:	The proposed model is based on tree-LSTMs, constituency trees, and dependencies trees.

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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang

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

Challenge:	generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories.
Approach:	They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports.
Outcome:	The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection.

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

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)

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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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)

Challenge:	Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning.
Approach:	They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks.
Outcome:	The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks.

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Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)

Challenge:	Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse.
Approach:	They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints.
Outcome:	The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints .

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Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu, Tat-Seng Chua

SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)

Challenge:	Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”.
Approach:	They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual.
Outcome:	The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions.

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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu

A Multitask Active Learning Framework for Natural Language Understanding (2020.coling-main)

Challenge:	Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text.
Approach:	They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes.
Outcome:	The proposed benchmark improves by 4.1% over baselines on SCITAT.

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Hua Zhu, Wu Ye, Sihan Luo, Xidong Zhang

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

Challenge:	Existing studies have shown that relation information between intents and slots can improve the efficiency of active learning algorithms.
Approach:	They propose a multitask active learning framework that exploits relation information between sub-tasks provided by a joint model.
Outcome:	The proposed framework achieves competitive performance with less training data than baseline methods on all datasets.

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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.

Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models (2025.acl-long)

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Shengqian Qin, Yakun Zhu, Linjie Mu, Shaoting Zhang, Xiaofan Zhang

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

Challenge:	Large language models struggle with addressing diverse user inquiries in open-world tasks.
Approach:	They propose a plug-and-play tool retrieval system for LLMs to access external tool library and use retrieved tools to solve user's problem.
Outcome:	The proposed model improves on a finetuned version of LLaMA-3.1 and 2,800 dialogues and 7,361 tools spanning ten distinct test categories.

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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

MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction (2022.findings-acl)

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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Chenxi Zhu, Ziqiang Ying, Boyu Zhang, Feng Mao

Learning Architectures from an Extended Search Space for Language Modeling (2020.acl-main)

Challenge:	Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
Approach:	They propose a general detector-corrector multi-task framework which exploits the visual and phonological features of the misspelled characters and minimizes their misleading impact on the context.
Outcome:	The proposed framework outperforms the state-of-the-art methods on Chinese Spelling Correction tasks.

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Yinqiao Li, Chi Hu, Yuhao Zhang, Nuo Xu, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li

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

Challenge:	Neural architecture search (NAS) has advanced in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Approach:	They propose a general approach to learn both intra-cell and inter-cell architectures . they implement their approach in a differentiable architecture search system .
Outcome:	The proposed approach outperforms the baseline on PTB and WikiText data and shows good transferability to other systems.

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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

Incorporating Factuality Inference to Identify Document-level Event Factuality (2023.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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Heng Zhang, Peifeng Li, Zhong Qian, Xiaoxu Zhu

Challenge:	Document-level Event Factuality Identification (DEFI) is a task for identifying the degree of certainty that a specific event occurs in a document.
Approach:	They propose a document-level event factuality inference task to bridge the sentence-level and document- level event factualality semantically.
Outcome:	The proposed model outperforms the baselines on the public English and Chinese DLEF datasets.

Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

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

Challenge:	Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach:	VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome:	Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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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

Gloss-Free End-to-End Sign Language Translation (2023.acl-long)

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.

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Kezhou Lin, Xiaohan Wang, Linchao Zhu, Ke Sun, Bang Zhang, Yi Yang

Machine Translation

Challenge:	a study of sign language translation without gloss annotations focuses on the problem of gloss annotation . gloss annotation is hard to acquire, especially in large quantities, and limits the domain coverage of translation datasets .
Approach:	They propose a gloss-free end-to-end sign language translation framework to solve this problem . gloss annotations are hard to acquire, especially in large quantities, they argue .
Outcome:	The proposed framework improves sign language translation performance on large-scale datasets . gloss annotations are hard to acquire, especially in large quantities .

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

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

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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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao

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

Challenge:	Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach:	They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome:	Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.

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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

VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)

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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Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang

Visual Data in NLP

Challenge:	Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations.
Approach:	They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models .
Outcome:	The proposed approach is effective across three video captioning models.

M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)

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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li

MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)

Challenge:	Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis.
Approach:	They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources.
Outcome:	Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages.

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Yiheng Hu, Xiaoyang Wang, Qing Liu, Xiwei Xu, Qian Fu, Wenjie Zhang, Liming Zhu

Learned Adapters Are Better Than Manually Designed Adapters (2023.findings-acl)

Challenge:	Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps.
Approach:	They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types .
Outcome:	The proposed framework outperforms existing models that rely on training.

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Yuming Zhang, Peng Wang, Ming Tan, Wei Zhu

Unleashing the Potential of Large Language Models through Spectral Modulation (2024.findings-emnlp)

Challenge:	Existing approaches to improve adapter-based tuning are sub-optimal . a learning framework is proposed to learn the optimal adapter architectures .
Approach:	They propose a framework to automatically learn optimal adapter architectures for better task adaptation of pre-trained models.
Outcome:	The proposed framework outperforms the previous parameter-efficient tuning baselines while tuning comparable or fewer parameters.

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Peng Sun, Yao Zhu, Yunjian Zhang, Xiu Yan, Zizhe Wang, Xiangyang Ji

LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, garnering significant attention from both academia and industry.
Approach:	They propose to conduct spectral modulation in the parameter space of LLMs to integrate with various models in a plug-and-play manner.
Outcome:	The proposed approach improves performance by 10.12% with spectral modulation.

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Caiqi Zhang, Xiaochen Zhu, Chengzu Li, Nigel Collier, Andreas Vlachos

Can LLMs Understand Unvoiced Speech? Exploring EMG-to-Text Conversion with LLMs (2025.acl-short)

Challenge:	Existing methods for hallucination detection are limited to short-form question answering tasks and do not generalize well to open-ended generation.
Approach:	They propose a method that trains LLMs to append a numerical confidence score to each generated statement during long-form generation.
Outcome:	The proposed method is 20 faster than traditional self-consistency methods while achieving better calibration.

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Payal Mohapatra, Akash Pandey, Xiaoyuan Zhang, Qi Zhu

The Sonar Moment: An Audio Geo-Localization Benchmark for Audio-Language Models (2026.findings-acl)

Challenge:	Unvoiced electromyography (EMG) is an effective communication tool for individuals unable to produce vocal speech.
Approach:	They propose an EMG adaptor module that maps EMG features to an LLM's input space and achieves an average word error rate of 0.49 on a closed-vocabulary unvoiced EMG-to-text task.
Outcome:	The proposed module achieves an average word error rate of 0.49 on a closed-vocabulary unvoiced EMG-to-text task.

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Ruixing Zhang, Zihan Liu, Leilei Sun, Tongyu Zhu, Weifeng Lv

SearchGym: Bootstrapping Real-World Search Agents via Cost-Effective and High-Fidelity Environment Simulation (2026.acl-long)

Challenge:	AGL1K is the first audio geo-localization benchmark for audio language models (ALMs) it is based on a crowd-sourced platform and is available in 72 countries and territories.
Approach:	They propose a benchmark for audio geo-localization that quantifies the informativeness of each recording and a metric that quantizes the information of each audio clip.
Outcome:	The proposed benchmarks cover 72 countries and territories and can be used to improve audio geo-localization.

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Xichen Zhang, Ziyi He, Yinghao Zhu, Sitong Wu, Shaozuo Yu, Meng Chu, Wenhu Zhang, Haoru Tan, Jiaya Jia

Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)

Challenge:	Search agents are a pivotal paradigm for solving open-ended, knowledge-intensive reasoning tasks.
Approach:	They propose a search agent simulation environment that bootstraps robust search agents using Reinforcement Learning.
Outcome:	The proposed model outperforms the web-enhanced ASearcher model by 10.6%.

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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu

Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension (2023.findings-acl)

Challenge:	Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
Approach:	They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful .
Outcome:	The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks.

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Jiazheng Zhu, Shaojuan Wu, Xiaowang Zhang, Yuexian Hou, Zhiyong Feng

Counterfactual Off-Policy Training for Neural Dialogue Generation (2020.emnlp-main)

Challenge:	Existing MRC models may overuse name information to make predictions, causing name bias .
Approach:	They propose a Causal Interventional paradigm for MRC to mitigate name bias by analyzing pre-trained knowledge and context representations.
Outcome:	The proposed model is robust to names and performs competitively on the original SQuAD.

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Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring (2026.acl-long)

Challenge:	Existing models for open-domain dialogue generation suffer from data insufficiency . a potential response inferred in hindsight is called a counterfactual reasoning .
Approach:	They propose to explore potential responses by counterfactual reasoning . given an observed response, the model automatically infers the outcome of an alternative policy that could have been taken .
Outcome:	The proposed model outperforms the HRED model and conventional learning frameworks on the DailyDialog dataset.

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Dongxu Zhang, Yiding Sun, Cheng Tan, Wenbiao Yan, Ning Yang, Jihua Zhu, Haijun Zhang

Supporting Clustering with Contrastive Learning (2021.naacl-main)

Challenge:	Existing efforts to mitigate this via token compression fail due to its autoregressive nature . linguistically redundant tokens are erroneously pruned, leading to hallucinations .
Approach:	They propose a method that reformulates token pruning as a Visual-Anchored Information Bottleneck (VA-IB) optimization problem.
Outcome:	Experiments on Qwen2-VL and Llama-3.2 families show that the proposed model achieves a speedup with negligible accuracy loss.

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Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)

Challenge:	Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space, but different categories overlap with each other at the beginning of the learning process.
Approach:	They propose a framework to leverage contrastive learning to promote better separation between different categories by optimizing a clustering objective defined in the representation space.
Outcome:	The proposed framework improves state-of-the-art accuracy and normalized mutual information on short text clustering and combines top-down and bottom-up instance discrimination to achieve better distances.

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Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang

Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer (2020.coling-main)

Challenge:	Existing methods for fact-checking text generated by large language models are expensive and time-consuming.
Approach:	They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner.
Outcome:	The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner.

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Yufang Huang, Wentao Zhu, Deyi Xiong, Yiye Zhang, Changjian Hu, Feiyu Xu

AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)

Challenge:	Existing methods for unsupervised text style transfer lack parallel data and difficulties in content preservation.
Approach:	They propose a neural approach to unsupervised text style transfer using non-parallel data.
Outcome:	The proposed approach can be trained end-to-end on two widely-used public datasets.

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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang

Layer-Wise Multi-View Learning for Neural Machine Translation (2020.coling-main)

Challenge:	Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency.
Approach:	They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images.
Outcome:	The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images.

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Qiang Wang, Changliang Li, Yue Zhang, Tong Xiao, Jingbo Zhu

Machine Translation

Challenge:	Existing approaches to neural machine translation are limited to the topmost encoder layer’s context representation and cannot perceive the lower encoder layers.
Approach:	They propose a layer-wise multi-view learning approach to solve this problem by incorporating an auxiliary view into the model.
Outcome:	The proposed model can achieve stable results over multiple strong baselines and is agnostic to network architectures.

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

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

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

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

FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)

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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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu

Challenge:	Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections.
Approach:	They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts.
Outcome:	The proposed framework outperforms competing baselines and surpasses large-scale general VLMs.

Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang

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

Challenge:	Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach:	They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome:	The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.

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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

Neural Stylistic Response Generation with Disentangled Latent Variables (2021.acl-long)

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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Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension (2021.findings-emnlp)

Challenge:	Existing parallel datasets for creating stylistic responses are not stylistically consistent.
Approach:	They propose to disentangle the content and style in latent space by diluting sentence-level information in style representations.
Outcome:	The proposed approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines.

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Haichao Zhu, Zekun Wang, Heng Zhang, Ming Liu, Sendong Zhao, Bing Qin

NCLS: Neural Cross-Lingual Summarization (D19-1)

Challenge:	Existing domain adaptation paradigms for reading comprehension require large amounts of annotation data to achieve the desired task performance.
Approach:	They propose a few-shot domain adaptation paradigm for reading comprehension . they introduce self-attention attribution to weigh parameters and refine the lottery subnetwork .
Outcome:	The proposed model outperforms the full model fine-tuning adaptation on four out of five domains with a small amount of data available for adaptation.

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Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Chengqing Zong

SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)

Challenge:	Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation.
Approach:	They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization.
Outcome:	The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets.

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Jiashi Lin, Changhong Jiang, Yixiao Wang, Xinyi Zhu, Zhongtian Hu, Wei Zhang

Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)

Challenge:	Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns.
Approach:	a new framework is designed to calibrate knowledge graphs using global structural patterns.
Outcome:	a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks .

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning (2025.acl-long)

Challenge:	Abstract meaning representation (AMR) parsing is limited by the size of curated datasets.
Approach:	They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks.
Outcome:	The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models.

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Weijie Shi, Han Zhu, Jiaming Ji, Mengze Li, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Sirui Han, Yike Guo

UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)

Challenge:	Existing legal judgment prediction methods struggle with logical errors when conducting complex legal reasoning.
Approach:	They propose a method which enhances LJP reliability through step-wise verification and correction of the reasoning process.
Outcome:	The proposed model significantly improves concordance with court decisions from 72.37 to 80.27 on LLAMA-3.1-70B.

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Rui Li, Liyang He, Qi Liu, Zheng Zhang, Heng Yu, Yuyang Ye, Linbo Zhu, Yu Su

MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing (2026.findings-acl)

Challenge:	Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios.
Approach:	They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks.
Outcome:	The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering.

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Hongtao Duan, Lu Jiang, Minying Zhang, Xiaobing Zhu, Tianpeng Bu, Hao Jiang, Xinyu Wei, Lulu hu

Advancing Parameter Efficiency in Fine-tuning via Representation Editing (2024.acl-long)

Challenge:	Existing tool-calling methods rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved.
Approach:	They propose a method that decodes tool evidence from the tool library and mixes it into the output at the uncertain layer.
Outcome:	The proposed method reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead.

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Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang

THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)

Challenge:	Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters.
Approach:	They propose a new approach to fine-tuning neural models that scales and biases the representation produced at each layer.
Outcome:	The proposed approach reduces the number of trainable parameters by a factor of 25,700 compared to full parameter fine-tuning and by . 32 compared with LoRA.

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Xin Zhang, Qiyu Wei, Yingjie Zhu, Fanyi Wu, Sophia Ananiadou

Challenge:	Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately.
Approach:	They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities .
Outcome:	The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions.

SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection (2025.acl-long)

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Yi-Fan Lu, Xian-Ling Mao, Tian Lan, Tong Zhang, Yu-Shi Zhu, Heyan Huang

ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models (2025.coling-main)

Challenge:	Existing evaluation methods for Open Domain Event Detection (ODED) lack representative representations of the real world, making it difficult to accurately reflect performance of various ODED methods in real-world scenarios.
Approach:	They propose a scalable and reliable Semantic-level Evaluation framework for Open domain event detection by constructing a more representative evaluation benchmark and introducing a semantic evaluation metric.
Outcome:	The proposed framework first constructs a more representative evaluation benchmark that currently includes 564 event types covering 7 major domains, with a cost-effective supplementary annotation strategy to ensure the benchmark’s representativeness.

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Xiechi Zhang, Shunfan Zheng, Linlin Wang, Gerard de Melo, Zhu Cao, Xiaoling Wang, Liang He

Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)

Challenge:	Existing metrics for multimodal large language models only focus on token overlap and may not align with human judgment.
Approach:	They propose an open-source model that assesses the question answering abilities of multimodal large language models.
Outcome:	Experiments show that the ACE-M3 model performs better than existing models and is more reliable than existing metrics.

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Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, Linli Xu

Representation Learning

Challenge:	Recent work adapts diffusion models to textual data by diffusing on the embedding space.
Approach:	They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model.
Outcome:	The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models.

DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)

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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu

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

Challenge:	Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data.
Approach:	They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs.
Outcome:	The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback.

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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

SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment (2025.findings-acl)

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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Wenqiao Zhu, Ji Liu, Lulu Wang, Jun Wu, Yulun Zhang

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

Challenge:	Existing methods for aligning Large Language Models with human values are limited and results of DPO are not resilient.
Approach:	They propose a self-guided direct preference optimization algorithm that incorporates a pilot term to steer the gradient flow during the optimization process.
Outcome:	The proposed method can generate human-preferred response up to 9.19% higher than previous methods.

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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

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

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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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization (C18-1)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong

Summarization

Challenge:	Existing approaches focus on improving the informativeness of the summary, but ignore the correctness.
Approach:	They propose an entailment-aware encoder and an aML-based decoder to improve the correctness of the sentence summarization task.
Outcome:	The proposed model outperforms baselines on informativeness and correctness.

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

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

On the Calibration of Large Language Models and Alignment (2023.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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Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, Zhendong Mao

Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)

Challenge:	Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure .
Approach:	They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training.
Outcome:	The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration.

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Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang

Syntactic Text Processing

Challenge:	Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets.
Approach:	They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data.
Outcome:	Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets.

ESF: Efficient Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.findings-acl)

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Xiaofan Bai, Pingyi Hu, Xiaojing Ma, Linchen Yu, Dongmei Zhang, Qi Zhang, Bin Benjamin Zhu

CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning (2025.emnlp-main)

Challenge:	Large language models (LLMs) are increasingly utilized in diverse applications, including code generation, legal document analysis, medical diagnosis, and decision-making.
Approach:	They propose a fingerprinting method tailored for black-box tamper detection of large language models.
Outcome:	The proposed method detects tampering with a 99.2% detection rate using 5 fingerprint samples across state-of-the-art LLMs.

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Wenqiao Zhu, Ji Liu, Rongjunchen Zhang, Haipang Wu, Yulun Zhang

ToolExpNet: Optimizing Multi-Tool Selection in LLMs with Similarity and Dependency-Aware Experience Networks (2025.findings-acl)

Challenge:	Existing methods to improve the reasoning performance of Large Language Models (LLMs) ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling.
Approach:	They propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach to enhance the reasoning performance of Large Language Models.
Outcome:	The proposed approach exploits annotated CoT and stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal.

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Zijing Zhang, Zhanpeng Chen, He Zhu, Ziyang Chen, Nan Du, Xiaolong Li

RLAE: Reinforcement Learning-Assisted Ensemble for LLMs (2025.emnlp-main)

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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Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, Dongbin Zhao

Challenge:	Existing ensemble methods for ensembling large language models rely on fixed weighting strategies that fail to adapt to dynamic, context-dependent characteristics of LLMs.
Approach:	They propose a framework that reformulates LLM ensemble through a Markov Decision Process.
Outcome:	The proposed framework outperforms existing methods by 3.3% on a diverse set of tasks while achieving lower time latency.

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation (2026.acl-long)

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Kaiwen Wei, Kejun he, Xiaomian Kang, Jie Zhang, null Ymyang, Li Jin, Zhenyang Li, Jiang Zhong, Richard He Bai, Junnan Zhu

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)

Challenge:	Generative recommendation models inherently bias towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.
Approach:	They propose a training framework that shifts the objective from simple next-step prediction to deep comprehension of history by entropy-guided masking policy and a curriculum learning scheduler to enhance the framework.
Outcome:	The proposed framework outperforms state-of-the-art generative models on three public datasets and shows that it is more accurate than current models.

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Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

RISK: A Framework for GUI Agents in E-commerce Risk Management (2026.acl-long)

Challenge:	Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information.
Approach:	They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information.
Outcome:	The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets.

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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

DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)

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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Chao Zhang, Xin Shi, Xueqiao Zhang, Yifan Zhu, Yi Yang, Yawei Luo

SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)

Challenge:	Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs.
Approach:	They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation.
Outcome:	The proposed framework outperforms baselines, reducing preference bias and improving response quality.

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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang

Noise Learning for Text Classification: A Benchmark (2022.coling-1)

Challenge:	Existing methods to handle long text are limited due to time and memory complexity and limited input lengths.
Approach:	They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages .
Outcome:	The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset.

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Bo Liu, Wandi Xu, Yuejia Xiang, Xiaojun Wu, Lejian He, Bowen Zhang, Li Zhu

Improving Factual Consistency of Abstractive Summarization via Question Answering (2021.acl-long)

Challenge:	Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification .
Approach:	They propose to use four state-of-the-art methods of noise learning from the image domain to classify text.
Outcome:	The proposed benchmark of noise learning for text classification is based on four methods and five noise modes.

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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang

Summarization

Challenge:	Recent studies show that about 30% of summaries generated by neural text summarization suffer from fact fabrication.
Approach:	They propose an automatic evaluation metric to measure factual consistency and a learning algorithm that maximizes the metric during model training.
Outcome:	The proposed method improves factual consistency and overall quality of summarization models.

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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang

Paraphrasing

Challenge:	Copyright Detective is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs.
Approach:	They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing .
Outcome:	The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs.

MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)

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Zhengyuan Zhu, Daniel Lee, Hong Zhang, Sai Sree Harsha, Loic Feujio, Akash Maharaj, Yunyao Li

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

Challenge:	Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task.
Approach:	They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text.
Outcome:	The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications.

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

Trucidator: Document-level Event Factuality Identification via Hallucination Enhancement and Cross-Document Inference (2025.coling-main)

Challenge:	Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach:	They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome:	The proposed framework captures the trade-off between information relevance and structural complexity.

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Zihao Zhang, Zhong Qian, Xiaoxu Zhu, Peifeng Li, Qiaoming Zhu

AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)

Challenge:	Document-level event factuality identification (DEFI) assesses the veracity degree to which an event mentioned in a document has happened.
Approach:	They propose a document-level event factuality identification framework with hallucination features . they propose factualusion corpus that integrates both genuine and hallucinous false information .
Outcome:	The proposed framework outperforms baselines in document event factuality identification.

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Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, Yue Zhang

TurnBench-MS: A Benchmark for Evaluating Multi-Turn, Multi-Step Reasoning in Large Language Models (2025.findings-emnlp)

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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Yiran Zhang, Mo Wang, Xiaoyang Li, Kaixuan Ren, Chencheng Zhu, Usman Naseem

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing benchmarks focus on single-turn or single-step tasks, failing to capture iterative reasoning in real-world settings.
Approach:	They propose a benchmark that evaluates multi-turn, multi-step reasoning through an interactive code-breaking task inspired by the "Turing Machine Board Game" the best model achieves 84% accuracy in Classic mode, but performance drops to 18% in Nightmare mode.
Outcome:	The new benchmark evaluates multi-turn, multi-step reasoning through an interactive code-breaking task inspired by the "Turing Machine Board Game" the best model achieves 84% accuracy in Classic mode, but performance drops to 18% in Nightmare mode.

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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

MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026.findings-acl)

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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Zhili Pu, Lantian Zhang, Hao Duan, Zhixing Zhang, Keyun Zhu, Yongqi Fan, Ruihui Hou, Tong Ruan, Yun Tang

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecul representations limits their ability to capture structure–activity relationships (SAR).
Approach:	They propose an ML–LLM–Rule collaborative framework for MPP that injects ML-derived substructure attribution values into LLMs and calibrates them under specific chemical contexts.
Outcome:	The proposed framework outperforms baseline models on multiple benchmark datasets and is highly interpretable.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

More than Text: Multi-modal Chinese Word Segmentation (2021.acl-short)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Dong Zhang, Zheng Hu, Shoushan Li, Hanqian Wu, Qiaoming Zhu, Guodong Zhou

DecIF: Improving Instruction-Following through Decomposition (2026.acl-long)

Challenge:	Currently, word segmentation is performed in many languages without word delimiters.
Approach:	They propose to combine the multi-modality to perform Chinese word segmentation . they propose a time-dependent multi-module interactive model to integrate multi-modality information .
Outcome:	The proposed model integrates multi-modal information for word sequence labeling with Chinese language as target . the proposed model performs well on three training sets on Chinese and other languages without word delimiters.

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Tingfeng Hui, Pengyu Zhu, Bowen Ping, Ling Tang, Guanting Dong, Yaqi Zhang, Sen Su

Challenge:	Existing approaches to obtain high-quality instruction-following data rely heavily on existing documents and existing methods.
Approach:	They propose a data synthesis framework, DecIF, which automatically generates accurate and diverse instruction-following data from scratch for supervised fine-tuning and reinforcement learning.
Outcome:	Extensive experiments show that the proposed framework can synthesize accurate instruction-following data for both SFT and RL paradigms compared to baselines.

Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)

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Haohao Zhu, Xiaokun Zhang, Zeyuan Zeng, Junyu Lu, Zewen Bai, Liang Yang, Hongfei Lin

Commonsense Reasoning

Challenge:	Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor.
Approach:	They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality.
Outcome:	The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions.

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)

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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong

The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models (2024.emnlp-main)

Challenge:	Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge.
Approach:	They propose a benchmark to evaluate domain specialization methods in real-world software development.
Outcome:	KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development.

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Yanjun Chen, Dawei Zhu, Yirong Sun, Xinghao Chen, Wei Zhang, Xiaoyu Shen

Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)

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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Dong Zhang, Xincheng Ju, Junhui Li, Shoushan Li, Qiaoming Zhu, Guodong Zhou

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Challenge:	Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model .
Approach:	They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario.
Outcome:	The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario.

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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

Confidence Estimation for LLMs in Multi-turn Interactions (2026.findings-acl)

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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Caiqi Zhang, Ruihan Yang, Xiaochen Zhu, Chengzu Li, Tiancheng Hu, Yijiang River Dong, Deqing Yang, Nigel Collier

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion (2025.findings-emnlp)

Challenge:	Despite recent progress, most prior work studies confidence in single-turn question answering.
Approach:	They propose a logit-based probe that measures confidence in multi-turn dialogues . they propose 'infoECE' and a "hinter-guesser" paradigm for generating controlled evaluations based on data .
Outcome:	The proposed framework is grounded in calibration and monotonicity of confidence as more information becomes available.

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Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li

CMedCalc-Bench: A Fine-Grained Benchmark for Chinese Medical Calculations in LLM (2025.emnlp-main)

Challenge:	Large language models (LLMs) lack structural information and semantic context to infer missing entities . large language models often lack structural signals to infuse missing entities into knowledge graphs .
Approach:	a modular framework integrates structural information and semantic context into a frozen LLM backbone for link prediction.
Outcome:	a new framework integrates KG-derived structural information and semantic context to infer missing entities.

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Yunyan Zhang, Zhihong Zhu, Xian Wu

Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning (2025.emnlp-main)

Challenge:	Existing medical NLP benchmarks focus on qualitative reasoning and textual comprehension, but lack of fine-grained evaluation of intermediate reasoning.
Approach:	They propose a Chinese medical calculation benchmark that disentangles clinical entity extraction from numerical computation.
Outcome:	The proposed framework disentangles clinical entity extraction from numerical computation, enabling systematic diagnosis of model deficiencies.

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Deng Linger, Linghao Zhu, Yuliang Liu, Yu Wang, Qunyi Xie, Jingjing Wu, Gang Zhang, Yingying Zhu, Xiang Bai

InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)

Challenge:	Existing methods for generating geometric reasoning data through Chain-of-Thought (CoT) frameworks face three fundamental limitations: 1) lack of high-quality annotations and domain-specific expertise to ensure theorem-grounded diagrams. 2) lack of a coherent model; 3) lack of coherent model.
Approach:	They propose a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis framework that synthesizes theorematic diagrams with structured descriptions and properties.
Outcome:	The proposed framework expands theorem-type coverage, corrects misunderstandings, and enhances geometric reasoning.

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Zhiwei Fei, Songyang Zhang, Xiaoyu Shen, Dawei Zhu, Xiao Wang, Jidong Ge, Vincent Ng

Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)

Challenge:	InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws.
Approach:	They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws.
Outcome:	The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws.

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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

Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical Text (C18-1)

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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Junjie Xing, Kenny Zhu, Shaodian Zhang

How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026.acl-long)

Challenge:	Chinese word segmentation (CWS) tools face a performance drop when dealing with domain text . domain-specific CWS requires extremely high annotation cost due to ambiguity caused by domain terms and writing style .
Approach:	They propose to exploit domain-invariant knowledge from high resource to low resource domains to build Chinese word segmentation models.
Outcome:	The proposed model achieves higher accuracy than single-task CWS and other transfer learning baselines . the model is based on domain-invariant knowledge from high resource to low resource domains based in the biomedical domain .

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Tongyao Zhu, Qian Liu, Chang Ma, Jinghan Zhang, Longxu Dou, Junxian He, Shiqi Chen

Progressive Multimodal Reasoning via Active Retrieval (2025.acl-long)

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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Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen

PsyPath: Psychologically-guided Self-Exploration for Personality Detection (2026.findings-acl)

Challenge:	Existing approaches to improve multimodal large language models' reasoning performance are limited.
Approach:	They propose a framework to progressively improve multimodal reasoning capabilities . they propose active retrieval and Monte Carlo tree search to improve MLLMs' reasoning .
Outcome:	The proposed framework improves multimodal reasoning capabilities in multimodal large language models.

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Zheng Li, Hongxin Ding, Chenyu Zhang, Weimin Xiong, Dawei Zhu, Sujian Li

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	Personality detection aims to label traits via identifying linguistic cues from written text.
Approach:	They propose a framework that allows large language models to generate and answer psychologically meaningful questions and a hybrid scoring mechanism to evaluate the generated nodes in the reasoning paths.
Outcome:	The proposed framework outperforms baselines on two benchmark datasets and significantly improves performance and interpretability in downstream tasks.

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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

MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)

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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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin

A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)

Challenge:	Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images.
Approach:	They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs.
Outcome:	The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks.

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Zhihong Zhu, Fan Zhang, Yunyan Zhang, Jinghan Sun, Zhiqi Huang, Qingqing Long, Bowen Xing, Xian Wu

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

Challenge:	Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding.
Approach:	They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals .
Outcome:	The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods .

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

Multimodal Sentence Summarization via Multimodal Selective Encoding (2020.coling-main)

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

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Haoran Li, Junnan Zhu, Jiajun Zhang, Xiaodong He, Chengqing Zong

An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models (2022.emnlp-demos)

Challenge:	Existing methods for generating summary from text and image ignore that the image can improve the ability of the encoder to identify highlights of a news event or document.
Approach:	They propose a multimodal selective gate network that takes reciprocal relationships between textual and multi-level visual features into account to select highlights of the event.
Outcome:	The proposed model can generate summary for a given sentence-image pair using visual signals . it can also capture highlights embedded in the image more accurately, the authors show .

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Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen, Kyusong Lee, Xiaopeng Lu, Jianwei Yin

Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (2026.findings-acl)

Challenge:	Existing studies evaluate VLP models by comparing the fine-tuned downstream task performance with the average downstream task accuracy.
Approach:	They propose a toolbox for evaluating Vision-Language Pretraining (VLP) models.
Outcome:	The proposed toolbox provides the preliminary datasets that deepen the image-texting ability of a VLP model.

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Fuqiang Niu, Bowen Zhang, Junting Zhu, Qing Liao, Genan Dai, Hu Huang

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting (2020.acl-main)

Challenge:	Existing stance detection methods treat opinions as surface-level labels, overlooking conversational evidence behind stance expressions.
Approach:	They propose a task that jointly identifies stance polarity and contextual evidence . they propose stance-cause Detection language model that leverages explicit context reasoning .
Outcome:	The proposed task outperforms baseline methods on text-only and multimodal subtasks.

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Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao

Text Classification

Challenge:	Recent research has found that text classification datasets contain certain unintended biases, such as text containing demographic identity-terms that are more likely to be abusive.
Approach:	They propose a model-agnostic debiasing framework that recovers the non-discrimination distribution using instance weighting, which does not require extra resources or annotations apart from a pre-defined set of demographic identity-terms.
Outcome:	The proposed framework alleviates the unintended biases without hurting models’ generalization ability.

WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)

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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie

Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)

Challenge:	despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models.
Approach:	They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese.
Outcome:	The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated .

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Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, Xiang Ren

Croppable Knowledge Graph Embedding (2025.acl-long)

Challenge:	Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks.
Approach:	They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance.
Outcome:	The proposed model can adapt to new corpora while retaining knowledge in earlier domains.

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Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen

MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)

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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Haodi Zhang, Xinrui Zhu, Mingze Kong, Zhidan Liu, Tao Fan, Kaishun Wu, Yuanfeng Song

A Unified Encoder-Decoder Framework with Entity Memory (2022.emnlp-main)

Challenge:	a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances .
Approach:	They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl .
Outcome:	The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus .

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Zhihan Zhang, Wenhao Yu, Chenguang Zhu, Meng Jiang

ATIR: Towards Audio-Text Interleaved Contextual Retrieval (2026.acl-long)

Challenge:	Existing approaches to index, retrieve, and read documents as evidence suffer from large computational overheads.
Approach:	They propose an encoder-decoder framework with an entity memory that stores entity knowledge as latent representations and pre-trained on Wikipedia along with encoder parameters.
Outcome:	The proposed framework outperforms memory-based and non-memory encoder-decoder models on various entity-intensive question answering and generation tasks.

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Tong Zhao, Chenghao Zhang, Yutao Zhu, Zhicheng Dou

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework (2025.emnlp-main)

Challenge:	Recent multimodal information retrieval research has focused on images, largely overlooking audio.
Approach:	They propose an audio-text interleaved contextual retrieval task where queries can alternate between audio and text modalities.
Outcome:	The proposed model significantly improves over baselines.

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Fanqi Kong, Xiaoyuan Zhang, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model (2023.emnlp-main)

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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Kaiyan Zhang, Ning Ding, Biqing Qi, Xuekai Zhu, Xinwei Long, Bowen Zhou

Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)

Challenge:	Instruction tuning is an effective way of aligning large language models with private instruction data.
Approach:	They propose a training-free strategy to derive improved emulators from LLMs by using Offsite-Tuning (OFT) they propose CRaSh, which transfers transformer blocks between centralized LLM and downstream emulators .
Outcome:	The proposed technique boosts performance of large language models with billions of parameters.

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Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans.
Approach:	They propose a cooperative reasoning-induced PLM for solving the math word problem . they use system 1 as the generator and system 2 as the verifier to generate reasoning paths .
Outcome:	The proposed model improves on several mathematical reasoning datasets and achieves 9.6% improvement over baselines.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Jiachen Zhu, Congmin Zheng, Jianghao Lin, Kounianhua Du, Ying Wen, Yong Yu, Jun Wang, Weinan Zhang

Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)

Challenge:	Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues.
Approach:	They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup .
Outcome:	The proposed framework outperforms baseline models on multiple real-world datasets.

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Li Zhang, Huaiyu Zhu, Siddhartha Brahma, Yunyao Li

Paraphrasing

Challenge:	Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues .
Approach:	They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues .
Outcome:	The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax .

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)

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Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang

DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process (2025.acl-long)

Challenge:	Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture.
Approach:	They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier.
Outcome:	The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical

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Minjun Zhu, Yixuan Weng, Linyi Yang, Yue Zhang

Challenge:	Existing Large Language Models (LLMs) face limited domain expertise, hallucinated reasoning, and a lack of structured evaluation.
Approach:	They propose a multi-stage framework to emulate expert reviewers by incorporating structured analysis, literature retrieval, and evidence-based argumentation.
Outcome:	The proposed model outperforms CycleReviewer-70B with fewer tokens and achieves 88.21% and 80.20% win rates.

Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026.acl-long)

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Yuwei Zhang, Wenhao Yu, Shangbin Feng, Yifan Zhu, Letian Peng, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang

CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)

Challenge:	Existing knowledge injection benchmarks for large language models lack standardized testing grounds.
Approach:	They propose a knowledge injection benchmark that leverages recently-added and expert-curated facts from Wikipedia’s “Did You Know...” entries.
Outcome:	The proposed framework improves reliability accuracy by 29.1%.

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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu

Challenge:	Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks.
Approach:	They propose a repository-level benchmark that dissects coding capabilities through atomized tasks.
Outcome:	The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified.

Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)

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Zili Wang, Tianyu Zhang, Haoli Bai, Lu Hou, Xianzhi Yu, Wulong Liu, Shiming Xiang, Lei Zhu

Challenge:	Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective.
Approach:	They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism.
Outcome:	The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches .

Time-LlaMA: Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation (2025.acl-srw)

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Juyuan Zhang, Jiechao Gao, Wenwen Ouyang, Wei Zhu, Hui Yi Leong

UrbanGeoEval: A City-Scale Benchmark for Evaluating Large Language Models in Geospatial Reasoning (2026.acl-long)

Challenge:	Recent studies have demonstrated that large language models possess robust pattern recognition and semantic understanding capabilities over time series data.
Approach:	They propose a time series model that converts time series input into token embeddings and aligns time sequence embeddables with text prompts.
Outcome:	The proposed framework achieves the state-of-the-art (SOTA) performance and has potentials for wide industrial usages.

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Mutian Bao, Qiuyi Qi, Tian Liang, Jinjian Zhang, Wei Zhou, Ming Kong, Linjian Mo, Qiang Zhu

CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)

Challenge:	Extensive experiments on 18 widely used LLMs uncover critical insights: (1) models exhibit severe geographic biases and resolution gaps; (2) failures in complex multi-hop tasks stem from brittle foundational spatial skills rather than high-level logic deficits.
Approach:	They propose a dual-module framework that disentangles factual recall and spatial logic from the model's real capabilities in urban environments.
Outcome:	Extensive tests on 18 widely used LLMs reveal that models exhibit severe geographic biases and resolution gaps, and failures in complex multi-hop tasks often stem from brittle foundational spatial skills rather than high-level logic deficits.

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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)

Challenge:	Existing methods for document-level relation extraction (DocRE) lack logic and transparency.
Approach:	They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints.
Outcome:	The proposed framework outperforms existing rule-based frameworks on three DocRE datasets.

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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu

Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)

Challenge:	Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs.
Approach:	They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets.
Outcome:	Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks.

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Zhihong Zhu, Yunyan Zhang, Xuxin Cheng, Zhiqi Huang, Derong Xu, Xian Wu, Yefeng Zheng

Pairwise Supervised Contrastive Learning of Sentence Representations (2021.emnlp-main)

Challenge:	Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level.
Approach:	They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization.
Outcome:	The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments.

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Dejiao Zhang, Shang-Wen Li, Wei Xiao, Henghui Zhu, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training (2026.acl-long)

Challenge:	Recent efforts to improve sentence representation learning have a common weakness . siamese or triplet loss only learns from individual sentence pairs or tripletes .
Approach:	They propose a discrimination-based approach to bridge entailment and contradiction understanding with categorical concept encoding.
Outcome:	The proposed method outperforms the state-of-the-art method on downstream tasks . it improves 10%–13% on clustering tasks and 5%–6% on STS tasks compared with the previous method .

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Dingwei Zhu, Shihan Dou, Zhiheng Xi, Senjie Jin, Guoqiang Zhang, Jiazheng Zhang, Junjie Ye, Mingxu Chai, Enyu Zhou, Ming Zhang, Yuhui Wang, Caishuang Huang, Chenhao Huang, Yunke Zhang, Yuran Wang, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang

ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt (2025.emnlp-main)

Challenge:	Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete supervision.
Approach:	They propose a framework that enhances value modeling for robust RL in LLM post-training by integrating auxiliary losses guided by entropy and perplexity from a frozen language model and variational information bottleneck.
Outcome:	The proposed framework outperforms baselines on multi-turn dialogue, math reasoning, and science QA with rule-based and model-based rewards.

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Fanhu Zeng, Fei Zhu, Haiyang Guo, Xu-Yao Zhang, Cheng-Lin Liu

Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning (2021.naacl-main)

Challenge:	Existing MCIT methods do not fully exploit the unique attribute of Large Multimodal Models and often gain performance at the expense of efficiency.
Approach:	They propose a multimodal continual instruction learning framework that exploits the ability of LMMs to learn mixed instruction datasets and prompts for each task.
Outcome:	The proposed framework achieves +14.26% performance gain on MCIT benchmarks with remarkable x1.42 inference speed free from growing computation.

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Hui Liu, Danqing Zhang, Bing Yin, Xiaodan Zhu

AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)

Challenge:	Existing zero-shot learning methods for multi-label text classification mostly learn a matching model between the feature space of text and the label space.
Approach:	They propose to use a graph encoder to incorporate label hierarchies to learn effective label representations on the zero-shot multi-label text classification problem.
Outcome:	The proposed approach outperforms previous non-pretrained methods on the zero-shot multi-label text classification task.

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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

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

Challenge:	Existing approaches to large language models rely on static templates or manual workflows.
Approach:	AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning.
Outcome:	AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks.

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

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

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

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

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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

Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)

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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Yun Hu, Yeshuang Zhu, Jinchao Zhang, Changwen Zheng, Jie Zhou

LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)

Challenge:	Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing.
Approach:	They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances.
Outcome:	Empirical results show that the proposed model improves against baselines and can be scaled to a large extent.

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Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Lu Xiangju, Junmin Zhu, Wei Zhang

LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)

Challenge:	Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs.
Approach:	They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts.
Outcome:	The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts.

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Zhiwen Ruan, Yixia Li, He Zhu, Longyue Wang, Weihua Luo, Kaifu Zhang, Yun Chen, Guanhua Chen

TokenShapley: Token Level Context Attribution with Shapley Value (2025.findings-acl)

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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Yingtai Xiao, Yuqing Zhu, Sirat Samyoun, Wanrong Zhang, Jiachen T. Wang, Jian Du

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

Challenge:	Large language models (LLMs) have strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge.
Approach:	They propose a token-level attribution method that combines Shapley value-based data attribution with KNN-based retrieval techniques to improve attribution accuracy.
Outcome:	TokenShapley outperforms state-of-the-art methods on four benchmarks . it achieves an 11–23% improvement in accuracy on the benchmarks.

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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

Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)

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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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han

Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)

Challenge:	Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines.
Approach:	They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster.
Outcome:	The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines.

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Yufan Jiang, Chi Hu, Tong Xiao, Chunliang Zhang, Jingbo Zhu

Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)

Challenge:	Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures.
Approach:	They propose differentiable neural architecture search methods for natural language processing . they remove the softmax-local constraint and apply it to named entity recognition .
Outcome:	The proposed method outperforms strong baselines on the language modeling task.

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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.

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

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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

MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling (2025.naacl-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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Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue, Shaoting Zhang, Xiaofan Zhang

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

Challenge:	Large Language Models (LLMs) have been widely used in medicine but are limited in their ability to fully address the complexities of the real world.
Approach:	They propose a universal agent architecture for Large Language Models that integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization.
Outcome:	The proposed framework improves the accuracy and performance of medical calculators in complex medical scenarios.

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)

Challenge:	Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal.
Approach:	They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs.
Outcome:	The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs.

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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu

Iterative Formalization and Planning in Partially Observable Environments (2026.findings-acl)

Challenge:	Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence.
Approach:	They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty.
Outcome:	Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect.

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Liancheng Gong, Wang Bill Zhu, Jesse Thomason, Li Zhang

Challenge:	Existing methods to formalize an environment into the Planning Domain Definition Language (PDDL) have been shown to improve performance and control.
Approach:	They propose a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes.
Outcome:	The proposed framework improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches.

ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)

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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

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (2024.findings-emnlp)

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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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)

Challenge:	Existing studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs.
Approach:	They propose a method that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs.
Outcome:	The proposed method achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on mainstream datasets.

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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng

Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)

Challenge:	Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks.
Approach:	They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks.
Outcome:	The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks.

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Yuhao Zhang, Chen Xu, Bei Li, Hao Chen, Tong Xiao, Chunliang Zhang, Jingbo Zhu

Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)

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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Chaofa Yuan, Yuhan Liu, Rongdi Yin, Jun Zhang, Qinling Zhu, Ruibin Mao, Ruifeng Xu

EgoNormia: Benchmarking Physical-Social Norm Understanding (2025.findings-acl)

Challenge:	Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory.
Approach:	They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text.
Outcome:	The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text.

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MohammadHossein Rezaei, Yicheng Fu, Phil Cuvin, Caleb Ziems, Yanzhe Zhang, Hao Zhu, Diyi Yang

XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)

Challenge:	Existing VLMs lack robust grounded norm understanding, a new study finds . current VLM models lack robust grounding, despite a high score for safety and privacy .
Approach:	They propose a pipeline to generate grounded MCQs from ego-centric videos of human interactions.
Outcome:	The proposed pipeline can generate grounded MCQs from egocentric video . it shows that current VLMs lack robust grounded norm understanding .

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling (2025.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English.
Approach:	They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach .
Outcome:	The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks.

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Jihai Zhang, Xiaoye Qu, Tong Zhu, Yu Cheng

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Recent studies found that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features.
Approach:	They propose a model-agnostic approach that fine-tunes complementary CLIP models and transforms them into a CLIP-MoE.
Outcome:	The proposed framework fine-tunes a series of complementary CLIP models and transforms them into a CLIP-MoE.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou, Minlie Huang

Challenge:	Existing methods for generating counterfactuals rely on human efforts or task-specific designs.
Approach:	They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals.
Outcome:	The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks.

A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)

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Fan Zhang, Hao Chen, Zhihong Zhu, Ziheng Zhang, Zhenxi Lin, Ziyue Qiao, Yefeng Zheng, Xian Wu

AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration (2025.acl-demo)

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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Minjiang Huang, Jipeng Qiang, Yi Zhu, Chaowei Zhang, Xiangyu Zhao, Kui Yu

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)

Challenge:	Interpretative audiobooks are becoming more popular, but their manual creation process remains time-consuming and resource-intensive.
Approach:	They propose a multi-agent collaboration system that leverages large language models and speech synthesis technology to generate podcast-like audiobook interpretations.
Outcome:	The proposed system is open source and open to the public.

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang

Challenge:	Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details.
Approach:	They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making.
Outcome:	Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

A Unified Dialogue User Simulator for Few-shot Data Augmentation (2022.findings-emnlp)

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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Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang

On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)

Challenge:	Existing methods to augment large-scale task-oriented dialogues rely on annotated data.
Approach:	They propose to build a unified dialogue user simulation model by pre-training on publicly available datasets.
Outcome:	The proposed model can be tuned on a target domain with few-shot data.

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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi

SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)

Challenge:	Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks.
Approach:	They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model.
Outcome:	Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning.

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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You

TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020.coling-main)

Challenge:	Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns.
Approach:	They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration.
Outcome:	The proposed framework significantly improves safety performance by 35% compared to traditional frameworks.

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Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, Limin Sun

Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)

Challenge:	Existing methods to extract entities and relations from unstructured text are susceptible to cascading errors due to the separation of entity detection and relation classification.
Approach:	They propose a one-stage joint extraction model that detects overlapping relations while being immune from exposure bias.
Outcome:	The proposed model can identify overlapping relations while being immune from exposure bias.

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Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang

Challenge:	Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals .
Approach:	They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation.
Outcome:	The proposed method mitigates behavior collapse and improves performance across benchmarks.

HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2023.acl-long)

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He Zhu, Chong Zhang, Junjie Huang, Junran Wu, Ke Xu

From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)

Challenge:	Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge.
Approach:	They propose a hierarchy-aware tree isomorphism network to enhance the text representations with only syntactic information of the label hierarchy.
Outcome:	The proposed model could boost the performance of hierarchical text classification without prior statistics or label semantics without prior data.

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Wenjing Xie, Xiaobo Liang, Juntao Li, Wanfu Wang, Kehai Chen, Qiaoming Zhu, Min Zhang

ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)

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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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.
Approach:	They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments.
Outcome:	The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Chinese Live-Streaming E-Commerce Morph Resolution: Datasets and Methods (2026.findings-acl)

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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Jipeng Qiang, Jiahao Zhu, Yi Zhu, Chaowei Zhang

Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)

Challenge:	Live-stream E-commerce faces significant challenges from morphs, deliberate linguistic variants used to evade real-time voice filters and amplify product claims illegally.
Approach:	They propose a framework that resolves morphs and generates structured explanations . they propose morph-aware dual-output refinement framework that detects inconsistencies .
Outcome:	The proposed framework improves morph resolution accuracy and interpretability.

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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.

CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation (2026.findings-acl)

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Peiding Wang, Li Zhang, Fang Liu, Chongyang Tao, Yinghao Zhu

Assessing “Implicit” Retrieval Robustness of Large Language Models (2024.emnlp-main)

Challenge:	Existing memory management approaches show promise but remain limited by natural language-centric representations.
Approach:	They propose an AST-guided dynamic memory management system for repository-level iterative code generation that maintains and updates repository context through AST operations.
Outcome:	The proposed system improves instruction following by 12.2% and reduces interaction rounds by 2–3 while maintaining competitive inference latency and token efficiency.

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Xiaoyu Shen, Rexhina Blloshmi, Dawei Zhu, Jiahuan Pei, Wei Zhang

RelayAttention for Efficient Large Language Model Serving with Long System Prompts (2024.acl-long)

Challenge:	Retrieval-augmented generation (RAG) is a framework to enhance large language models with external knowledge, but its effectiveness is constrained by the retrieval robustness of the model.
Approach:	They propose to use gold and distracting context to fine-tune models to handle relevant or irrelevant retrieved context in an end-to-end manner.
Outcome:	The proposed model performs better when gold and distracting context are used, while still extracting correct answers when retrieval is accurate.

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Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson Lau

Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)

Challenge:	a long system prompt causes throughput/latency bottlenecks as the cost of generating the next token increases w.r.t the sequence length.
Approach:	They propose an attention algorithm that reads hidden state from DRAM exactly once for a batch of input tokens.
Outcome:	The proposed algorithm reduces the need for redundant memory accesses in existing algorithms.

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Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Zhu

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-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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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge (2023.findings-acl)

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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Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henghui Zhu, Yuhao Zhang, Alexander Hanbo Li, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)

Challenge:	Open-ended Visual Question Answering (VQA) requires models to reason over visual and natural language inputs using world knowledge.
Approach:	They propose a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time.
Outcome:	The proposed pipeline expands the knowledge coverage from in-domain training data by 4.1% on OK-VQA, without additional computation cost.

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Weiyang Guo, Zesheng Shi, Liye Zhao, Jiayuan Ma, Zeen Zhu, Junxian He, Min Zhang, Jing Li

Lingke: a Fine-grained Multi-turn Chatbot for Customer Service (C18-2)

Challenge:	Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse.
Approach:	They propose an Enhanced Experience Exploitation paradigm that integrates expert prefixes, expert guided, and self-exploration to improve agent training.
Outcome:	The proposed model achieves a 6% performance improvement over traditional paradigms on tool-use tasks while requiring less than 10% of the synthetic data.

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Pengfei Zhu, Zhuosheng Zhang, Jiangtong Li, Yafang Huang, Hai Zhao

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)

Challenge:	e-commerce chatbots usually need a mass of human dialogue data to train, but for multi-turn conversations, the performance is poor.
Approach:	They propose an information retrieval augmented multi-turn chatbot which can answer questions based on unstructured documents and deal with multi-turned conversations.
Outcome:	The proposed solution outperforms all other models in multi-turn conversations and can learn from conversation records.

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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

MSMO: Multimodal Summarization with Multimodal Output (D18-1)

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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Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong

SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction (2026.findings-acl)

Challenge:	Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality.
Approach:	They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs.
Outcome:	The proposed method improves user satisfaction by 12.4% compared to the current system .

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Hangxiao Zhu, Yuyu Zhang, Ping Nie, Yu Zhang

Challenge:	Prior work on scientific impact prediction has focused on citation counts and its variants, leaving limited evaluation of models’ capability to reason about other dimensions.
Approach:	They propose a large-scale, multi-dimensional benchmark for scientific impact prediction spanning 19 fields.
Outcome:	The proposed model outperforms larger models and close-source models in a wide range of fields and measures of scientific impact across 19 fields.

LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)

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Yuxiao Ye, Yiming Zhang, Yiran Rex Ma, Huiyuan Xie, Huining Zhu, Zhiyuan Liu

MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration (2026.acl-long)

Challenge:	Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues.
Approach:	They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies.
Outcome:	The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language.

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Yakun Zhu, Yutong Huang, Shengqian Qin, Zhongzhen Huang, Shaoting Zhang, Xiaofan Zhang

Challenge:	Existing benchmarks focus on static single-step calculations with explicit instructions.
Approach:	They propose a benchmark for evaluating medical calculators in realistic scenarios . they use 118 scenario tasks across 4 clinical domains to evaluate medical calculator performance .
Outcome:	The first benchmark for evaluating medical calculators in realistic scenarios is released . it features 118 scenario tasks across 4 clinical domains and is based on a model context protocol integration.

Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)

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Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang

UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)

Challenge:	Existing federated learning frameworks require substantial data and computational resources to develop large language models.
Approach:	They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs.
Outcome:	The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one.

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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi

Challenge:	Existing alignment paradigms for creative writing use static reward signals and supervised data.
Approach:	They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments.
Outcome:	The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references.

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models (2024.naacl-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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Yue Zhou, Yada Zhu, Diego Antognini, Yoon Kim, Yang Zhang

Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision (2025.findings-emnlp)

Challenge:	Despite the impressive performance of large-scale language models, their ability to reason through complex problems remains a bottleneck.
Approach:	They propose a method which diversifies reasoning paths from specific surface forms of the problem to improve mathematical reasoning performance.
Outcome:	The proposed approach improves mathematical reasoning performance over vanilla self-consistency, especially for problems initially deemed unsolvable.

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Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu, Haosheng Zou, Junfeng Ran, null XWang, Lin Sun, Xiangzheng Zhang, Sujian Li

Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks.
Approach:	They propose a chain-of-thought framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance.
Outcome:	The proposed framework generalizes across most long-context scenarios and amplifys with increasing context length.

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Xianzhen Luo, Yixuan Wang, Qingfu Zhu, Zhiming Zhang, Xuanyu Zhang, Qing Yang, Dongliang Xu

MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency.
Approach:	They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree.
Outcome:	The proposed method outperforms existing train-free methods by 30% and even a training method by 25%.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)

Challenge:	Existing TATQA datasets are limited to English, leading to drawbacks . existing datasets overlook challenges of multilingual TAT-QA and do not reflect real-world multilingual scenarios .
Approach:	They propose a multilingual TATQA dataset that can be translated into 10 languages . they use data from 3 mainstream TATQ datasets and analyze the results .
Outcome:	The proposed dataset outperforms other baselines by an average of 3.3 .

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Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, Lingyao Li

Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation (2026.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP).
Approach:	They investigate the vulnerability of Large Language Models to hidden adversarial prompts . they evaluate two critical attack scenarios: malicious content relay and sensitive data leakage .
Outcome:	The proposed extension could introduce new security vulnerabilities.

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Jianing Zhang, Runan Li, Honglin Pang, Ding Xia, Zhou Zhu, Qian Zhang, Chuntao Li, Xi Yang

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects (2026.findings-eacl)

Challenge:	Existing methods for deciphering ancient Chinese Oracle Bone Script (OBS) treat deciphering as a closed-set image recognition problem, which fails to bridge the "interpretation gap" .
Approach:	They propose a vision-language model framework that integrates a VLM and an LLM to automate a reasoning chain of component identification and knowledge retrieval.
Outcome:	The proposed framework yields more detailed and precise decipherments compared to baseline methods.

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Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang, Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)

Challenge:	a series of paradigm shifts have come with distinct characteristics and challenges associated with table modeling.
Approach:	They propose to replicate four table LLMs by instruction-tuning three foundation models on four existing datasets.
Outcome:	The results show that base model choice plays a more dominant role than training data itself.

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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng

Concise Math Reasoning via Difficulty-Aware Distillation (2026.findings-acl)

Challenge:	Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment.
Approach:	They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion.
Outcome:	The proposed training recipe bridges atomic action execution and strategic task completion.

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Yifan Wu, Jingze Shi, Bingheng Wu, Jiayi Zhang, Xiaotian Lin, Yizhang Zhu, Zhaoyang Yu, Bang Liu, Chenglin Wu, Nan Tang, Yuyu Luo

Numerical Reasoning

Challenge:	Human experts tackle difficult math problems by identifying and executing a few pivotal steps rather than listing every intermediate thought.
Approach:	They propose a method for producing training data that mirrors concise human reasoning by rewriting a problem's solution to retain only the essential steps.
Outcome:	The proposed method outperforms models trained on 800k long CoT and cuts training and inference costs.

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)

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Xu Liu, Yan Chen, Kan Ling, Yichi Zhu, Hengrun Zhang, Guisheng Fan, Huiqun Yu

Proofread: Fixes All Errors with One Tap (2024.acl-demos)

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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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai

Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)

Challenge:	Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio.
Approach:	They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement.
Outcome:	The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets.

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Dejiao Zhang, Wei Xiao, Henghui Zhu, Xiaofei Ma, Andrew Arnold