Papers by Jia Chen

Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping (2026.findings-acl)

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

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Miao Peng, Weizhou Shen, Nuo Chen, Chenliang Li, Ming Yan, Jia Li

How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization (2020.findings-emnlp)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning.
Approach:	They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck.
Outcome:	The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters.

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Jian Liu, Yubo Chen, Kang Liu, Yantao Jia, Zhicheng Sheng

Robustness in NLP

Challenge:	Existing studies focus on improving the overall performance of an ED model, but few consider the robustness of an existing model.
Approach:	They propose a new training mechanism that can effectively mine context-specific patterns for learning and robustify an ED model.
Outcome:	The proposed model can learn a complementary predictive bias with most ED models that use full context for feature learning.

Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)

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Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)

Challenge:	Existing fashion recommendation systems struggle with the unique challenges of the fashion domain.
Approach:	They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts.
Outcome:	The proposed framework significantly improves fashion recommendation performance on Amazon fashion.

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Chengyu Wang, Zhongjie Duan, Bingyan Liu, Xinyi Zou, Cen Chen, Kui Jia, Jun Huang

Learning In-context Learning for Named Entity Recognition (2023.acl-long)

Challenge:	Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships.
Approach:	They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images.
Outcome:	The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions.

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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

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Guanhua Chen, Shuming Ma, Yun Chen, Li Dong, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

ExpertGenQA: Open-ended QA generation in Specialized Domains (2025.findings-emnlp)

Challenge:	Existing work on improving cross-lingual transferability of NMT model is under-explored.
Approach:	They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability.
Outcome:	The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task.

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Haz Sameen Shahgir, Chansong Lim, Jia Chen, Evangelos E. Papalexakis, Yue Dong

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

Challenge:	Existing methods for generating high-quality question–answer (QA) pairs yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples.
Approach:	They propose a question-answer generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs.
Outcome:	The proposed protocol achieves twice the efficiency of standard few-shot methods while maintaining 94.4% topic coverage.

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

mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)

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

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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

Challenge:	Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs.
Approach:	They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method.
Outcome:	Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks.

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Zeyuan Chen, Haiyan Wu, Kaixin Wu, Wei Chen, Mingjie Zhong, Jia Xu, Zhongyi Liu, Wei Zhang

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

Challenge:	Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items .
Approach:	They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance.
Outcome:	The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency.

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

SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)

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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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma

Programming Languages in NLP

Challenge:	Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments.
Approach:	They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods.
Outcome:	Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG.

SynET: Synonym Expansion using Transitivity (2020.findings-emnlp)

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Jiale Yu, Yongliang Shen, Xinyin Ma, Chenghao Jia, Chen Chen, Weiming Lu

Challenge:	Existing approaches to find synonyms from text corpora are distributed and pattern based, but they suffer from low precision and low recall.
Approach:	They propose a task of synonym expansion using transitivity and propose auxiliary task to reduce the impact of noisy sentences.
Outcome:	The proposed approach reduces the impact of noisy sentences and reduces noise in a real-world dataset.

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

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

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)

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

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Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

LLM Jailbreak Detection for (Almost) Free! (2025.findings-emnlp)

Challenge:	a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora.
Approach:	They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker.
Outcome:	The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences.

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Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li, Philip Torr, Jindong Gu

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)

Challenge:	Existing methods for detecting jailbreak prompts entail significant computational costs .
Approach:	They propose a free jailbreak detection method which scales logits by temperature to detect jailbreak prompts .
Outcome:	The proposed method detects jailbreak prompts with no additional computational costs.

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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)

Challenge:	Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Approach:	They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.
Outcome:	The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.

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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng

Linguistics & Cognitive NLP

Challenge:	Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions.
Approach:	They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators .
Outcome:	The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models.

Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)

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Chen Zhao, Yatu Ji, Ren Qing-Dao-Er-Ji, Nier Wu, Lei Shi, Fu Liu, Yepai Jia

Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)

Challenge:	Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora.
Approach:	They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages.
Outcome:	The proposed framework improves on four low-resource agglutinative language tasks.

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Chen Jia, Yuefeng Shi, Qinrong Yang, Yue Zhang

FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction (2020.emnlp-main)

Challenge:	Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER.
Approach:	They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT .
Outcome:	The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors.

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Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, Weijian Sun

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

Challenge:	Existing relation extraction methods require centralizing training data from different medical platforms while holding the privacy-sensitive data puts patients' privacy at risk.
Approach:	They propose a federated relation extraction model that trains a central model without sharing or exchange of private local data.
Outcome:	The proposed model trains a central model without uploading local parameters, and it performs well on three publicly available datasets.

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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

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.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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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation.
Approach:	They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows.
Outcome:	The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases.

AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms? (2026.findings-acl)

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Henan Sun, Kaichi Yu, Yuyao Wang, Bowen Liu, Xunkai Li, Rong-Hua Li, Nuo Chen, Jia Li

Natural Response Generation for Chinese Reading Comprehension (2023.findings-emnlp)

Challenge:	Existing benchmarks for algorithmic reasoning fail to answer a critical question: do LRMs master algorithmic thinking? Empirical evaluations on leading LRM models reveal substantial performance heterogeneity, while models perform well on non-optimized tasks, accuracy drops sharply to around 49% on globally optimized algorithms.
Approach:	They propose an algorithm-centric benchmark that evaluates large reasoning models under an algorithmic paradigm.
Outcome:	Empirical evaluations on leading LRMs reveal substantial performance heterogeneity . models perform well on non-optimized tasks, accuracy drops sharply to around 49% .

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Nuo Chen, Hongguang Li, Yinan Bao, Baoyuan Wang, Jia Li

Text Generation

Challenge:	MRC models trained on labeled answers are limited in generating human-like responses in real QA scenarios.
Approach:	They construct a dataset called Penguin to promote machine reading comprehension . they use 200k training data with fluent, well-informed responses to train models .
Outcome:	The proposed dataset is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale.

Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition (2026.acl-long)

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Tiejin Chen, Huaiyuan Yao, Jia Chen, Evangelos E. Papalexakis, Hua Wei

Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (D18-1)

Challenge:	Existing methods for MAS fail to address the unique complexities of multi-step reasoning . Existing uncertainty quantification methods struggle with cascading uncertainty .
Approach:	They propose a framework that quantifies uncertainty through tensor decomposition . they show that MATU effectively estimates holistic and robust uncertainty .
Outcome:	The proposed framework disentangles and quantifies distinct sources of uncertainty . it is generalizable across different agent structures and can be used for scientific discovery, education, healthcare and transportation.

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Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia

Syntactic Text Processing

Challenge:	Existing approaches to ACE event detection treat multiple events in one sentence as independent ones and recognize them separately.
Approach:	They propose a hierarchical and bias tagging network framework to detect multiple events in one sentence collectively and a gated multi-level attention mechanism to automatically extract and fuse the sentence-level and document-level information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a 2005 ACE dataset.

RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)

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Santosh T.y.s.s, Chen Jia, Patrick Goroncy, Matthias Grabmair

Text Generation

Challenge:	Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document.
Approach:	They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model.
Outcome:	The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection.

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

Memory-Based Invariance Learning for Out-of-Domain Text Classification (2023.emnlp-main)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Chen Jia, Yue Zhang

Information Extraction & Text Mining

Challenge:	Existing approaches to learning invariant representations rely on the assumption that training and test sets come from the same domain.
Approach:	They propose to extend a classification model trained on multiple source domains to an unseen target domain by using key-value memory.
Outcome:	The proposed method improves on sentiment analysis and natural language inference tasks.

RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)

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Yifan Niu, Miao Peng, Nuo Chen, Yatao Bian, Tingyang Xu, Jia Li

VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)

Challenge:	Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning.
Approach:	They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models.
Outcome:	The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing.

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Yuyan Chen, Jiyuan Jia, Jiaxin Lu, Siyue Li, Yu Guan, Ming Yang, Qingpei Guo

LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)

Challenge:	Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent.
Approach:	They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer.
Outcome:	The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets.

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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)

Challenge:	Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity.
Approach:	They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples .
Outcome:	The proposed method significantly reduces the size of training data while maximizing the submodular gain.

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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu

Challenge:	Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization.
Approach:	They propose a memory guideline optimization framework that learns how memory should be organized and what information to update.
Outcome:	The proposed framework learns how memory should be organized and what information to update.

ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)

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Wentong Chen, Yankai Lin, ZhenHao Zhou, HongYun Huang, YanTao Jia, Zhao Cao, Ji-Rong Wen

Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation (2024.findings-emnlp)

Challenge:	Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability.
Approach:	They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark.
Outcome:	The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy.

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G Shahariar, Jia Chen, Jiachen Li, Yue Dong

Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)

Challenge:	Recent studies show that text-to-image models are vulnerable to adversarial perturbations .
Approach:	They investigate the impact of adversarial attacks on different POS tags within text prompts on T2I models.
Outcome:	The proposed model is vulnerable to adversarial perturbations with noun perturbations in text prompts.

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Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025.acl-long)

Challenge:	Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users.
Approach:	They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives.
Outcome:	The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives.

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Ruotian Ma, Peisong Wang, Cheng Liu, Xingyan Liu, Jiaqi Chen, Bang Zhang, Xin Zhou, Nan Du, Jia Li

Prompt-based Distribution Alignment for Domain Generalization in Text Classification (2022.emnlp-main)

Challenge:	Existing approaches to incentivize LLMs’ deep thinking abilities require large-scale data or significant training efforts.
Approach:	They introduce an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Outcome:	The proposed framework outperforms models trained on long-CoT distilled data with 3.1k initialization samples and achieves an accuracy improvement of 51.0% to 81.6%.

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Chen Jia, Yue Zhang

Language Resource Efficient Learning for Captioning (2021.findings-emnlp)

Challenge:	Pretrained language models (PLMs) have achieved competitive performance on a range of NLP tasks.
Approach:	They propose to learn distributional invariance across source domains via alignment regularization loss functions to improve domain generalization by prompting.
Outcome:	Experiments on sentiment analysis and natural language inference show the effectiveness of the proposed method and achieve state-of-the-art results.

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Jia Chen, Yike Wu, Shiwan Zhao, Qin Jin

MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)

Challenge:	XE loss and SC loss are both considered to be performance degradations for captioning tasks.
Approach:	They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline.
Outcome:	The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources.

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Shuyi Zhang, Zhenbin Chen, Shuting Li, Kewei Tu, Li Jing, Zixia Jia, Zilong Zheng

Information Extraction & Text Mining

Challenge:	Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints.
Approach:	They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts.
Outcome:	The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains.

Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)

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Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Zejun Li, Yun Song, Zhongyu Wei

Challenge:	Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios.
Approach:	They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents.
Outcome:	The proposed framework assesses task performance and procedural compliance across legal proficiency levels.

How does Misinformation Affect Large Language Model Behaviors and Preferences? (2025.acl-long)

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Miao Peng, Nuo Chen, Jianheng Tang, Jia Li

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)

Challenge:	Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation.
Approach:	They propose to use a benchmark to evaluate LLMs' behavior and knowledge preference toward misinformation to identify their models.
Outcome:	The proposed approach is based on 10,346,712 pieces of misinformation and examines knowledge conflicts and stylistic variations.

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Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	MRQA datasets have been used to benchmark progress in general-purpose language understanding.
Approach:	They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities.
Outcome:	The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025.findings-naacl)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Jiazhou Chen, Xu Jia, RuiQiang Guo

Sentiment Analysis

Challenge:	Existing models confuse implicit and explicit sentiment, making it difficult to extract quadruples effectively.
Approach:	They propose a framework that leverages distinct labeled features from diverse reviews and incorporates pseudo-token prompts to harness the semantic knowledge of pre-trained models.
Outcome:	The proposed framework improves over four public datasets, averaging 1.99% F1 improvement, particularly in instances involving implicit sentiment.

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

Information Extraction & Text Mining

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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

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

DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space (2024.lrec-main)

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

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Jianxiang Xiang, Zhenhua Liu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited.
Approach:	They propose a diffusion model to enhance the diversity of dialogue generation by using continuous latent variables instead of discrete ones.
Outcome:	The proposed model greatly enhances diversity of dialog response while keeping the coherence.

HOTVCOM: Generating Buzzworthy Comments for Videos (2024.findings-acl)

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Yuyan Chen, Songzhou Yan, Qingpei Guo, Jiyuan Jia, Zhixu Li, Yanghua Xiao

DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)

Challenge:	Existing research focuses on generating descriptive comments in English . hot-comments are important for video marketing and branding, authors say .
Approach:	They propose a framework to generate hot-comments on a Chinese video dataset . they use a combination of visual, auditory, and textual data to generate them .
Outcome:	The proposed framework shows that it generates hot-comments on both the new and existing datasets.

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Yuhan Liu, Cong Xu, Lu Liu, Yihua Wang, Feiyu Chen, Qi Jia, Yaqian Zhao, Zhichun Wang, Xiang Li

AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties.
Approach:	They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning.
Outcome:	The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation.

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Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

Multi-Cell Compositional LSTM for NER Domain Adaptation (2020.acl-main)

Challenge:	Recent advances in Large Language Models have transformed ML/AI development . a reevaluation of AutoML principles for Retrieval-Augmented Generation (RAG) systems is needed.
Approach:	They propose a framework for hyper-parameter tuning and a hierarchical MAB method for efficient exploration of large search spaces.
Outcome:	The proposed framework outperforms baseline methods in more challenging optimization scenarios.

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Chen Jia, Yue Zhang

UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models (2025.findings-naacl)

Challenge:	Named entity recognition (NER) is a challenging but practical problem.
Approach:	They propose a multi-cell compositional LSTM structure for multi-task learning . they model each entity type using a separate cell state .
Outcome:	Empirical results show that the proposed method outperforms multi-task learning methods and achieves the best results.

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Ruoli Gan, Duanyu Feng, Chen Zhang, Zhihang Lin, Haochen Jia, Hao Wang, Zhenyang Cai, Lei Cui, Qianqian Xie, Jimin Huang, Benyou Wang

Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)

Challenge:	Existing legal benchmarks focusing on knowledge and logic evaluate LLMs on various tasks in legal domain, but few have explored the practical application of LLM by actual users.
Approach:	They propose a Chinese user-centric legal benchmark that aims to assess the practical application of LLMs by real users.
Outcome:	The proposed model outperforms existing models on various tasks in legal domain but does not outperfect ChatGPT.

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Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu

Text Generation

Challenge:	Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods.
Approach:	They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets.
Outcome:	The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable.

Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps (2025.findings-acl)

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Jiashun Cheng, Aochuan Chen, Nuo Chen, Ziqi Gao, Yuhan Li, Jia Li, Fugee Tsung

The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)

Challenge:	Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models.
Approach:	They propose a low-rank Adaptation technique that harnesses the expressiveness of spectral bases to re-parameterize LoRA from a sparse spectral subspace.
Outcome:	The proposed technique achieves greater efficiency with fewer parameters than baselines on various downstream tasks, including commonsense reasoning, math reasoning, and code generation.

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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun

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

Challenge:	Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations.
Approach:	They propose a training data arrangement framework that allows for continual learning and loss reduction.
Outcome:	The proposed framework promotes continual learning and loss reduction on unseen tasks.

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

Can Intelligent Agents Revolutionize Scale Generation? (2026.findings-acl)

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

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Chenghao Jia, Zhitao Yuan, Zhaokang Zong, YiFei Yin, Zhe Chen, Man Lan, Shengjun Wu

Challenge:	Existing measurement scales require extensive manual labor and require extensive validation and validation.
Approach:	They propose a multi-agent framework that automates scale development by leveraging collaborative AI agents.
Outcome:	The proposed framework automates scale development while maintaining rigorous quality standards.

Evaluating the Long-Term Memory of Large Language Models (2025.findings-acl)

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Zixi Jia, Qinghua Liu, Hexiao Li, Yuyan Chen, Jiqiang Liu

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

Challenge:	Recent studies have not thoroughly investigated the memory performance of large language models in long-term tasks.
Approach:	They propose a dataset to evaluate the long-term memory capabilities of large language models.
Outcome:	The proposed model exhibits memory preferences across different categories of information.

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

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)

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

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Huanyu Liu, Jia Li, Yihong Dong, Chang Yu, Taozhi Chen, Lecheng Wang, Yongding Tao, Bin Gu, Ge Li

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

Challenge:	Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration.
Approach:	They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories.
Outcome:	The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods.

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

Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)

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

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Guanhua Chen, Shuming Ma, Yun Chen, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)

Challenge:	Existing unsupervised neural machine translation systems can degrade when labeled data is limited.
Approach:	They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset.
Outcome:	The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU.

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Nuo Chen, Linjun Shou, Jian Pei, Ming Gong, Bowen Cao, Jianhui Chang, Jia Li, Daxin Jiang

Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (2024.lrec-main)

Challenge:	Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs .
Approach:	They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers.
Outcome:	The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets.

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Haiyang Zhang, Qiuyi Chen, Yanjie Zou, Jia Wang, Yushan Pan, Mark Stevenson

Low-Resource NLP

Challenge:	Existing approaches to Document Set Expansion (DSE) rely on the unrealistic assumption of knowing the class prior for positive samples in the collection.
Approach:	They propose a novel method that utilizes intractable density estimation models to learn the class prior for positive samples in the collection.
Outcome:	The proposed method is based on a set of examples from PubMed and Covid datasets in a transductive setting.

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

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

Structured Data in NLP

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

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)

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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li

Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations (2025.coling-main)

Challenge:	Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios.
Approach:	They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings.
Outcome:	The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains.

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Nuo Chen, Hongguang Li, Jianhui Chang, Juhua Huang, Baoyuan Wang, Jia Li

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

Challenge:	Existing retrieval-based methods for long-term conversations face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions.
Approach:	They propose a framework that eschews traditional retrieval modules and memory databases and adopts a “One-for-All” approach to manage memory generation, compression, and response generation.
Outcome:	The proposed framework produces more nuanced and human-like experiences than retrieval-based methods.

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)

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Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, Huaiyu Wan

Green & Sustainable NLP

Challenge:	Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient.
Approach:	They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training.
Outcome:	The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways.

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search (2025.emnlp-main)

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

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Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, Wenhu Chen

A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare (2026.findings-acl)

Challenge:	Existing vision-language models struggle with reasoning-focused tasks due to the lack of high-quality training data.
Approach:	They propose a new approach that leverages search engines to create a multimodal multimodal dataset . they use a set of 30,000 seed images to extract HTML data from 700K unique URLs .
Outcome:	The proposed model achieves the best known performance on MMMU-Pro (40.7), MathVerse (42.6), and DynaMath (55.7).

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Haijie Ruan, Xiaowu Jiang, Zhanpeng LI, Wei Jia, Xuanwu Xu, Xiao-Fen Shan, Shujie Chen, Xindong Ye

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

Challenge:	Existing systems treat roles as static prompts and rely on one-shot safety filters . a self-evolving LLM agent is proposed that learns from role-based social experience .
Approach:	They propose a self-evolving LLM agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature.
Outcome:	The proposed agent learns from role-based social experience and models communicator-level individual traits informed by prior communication questionnaires and clinical literature.

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

VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)

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

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Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao

Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)

Challenge:	Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions.
Approach:	They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset.
Outcome:	The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality.

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Nuo Chen, Linjun Shou, Tengtao Song, Ming Gong, Jian Pei, Jianhui Chang, Daxin Jiang, Jia Li

MURAL: Multimodal, Multitask Representations Across Languages (2021.findings-emnlp)

Challenge:	Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks.
Approach:	They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations.
Outcome:	The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings.

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Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge

MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning (2026.acl-long)

Challenge:	Image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
Approach:	They propose a dual encoder that integrates image-text matching and translation pairs to solve two tasks by learning from billions of pairs.
Outcome:	The proposed encoder outperforms ALIGN's cross-modal retrieval performance on well-resourced languages and significantly improves on under-resource languages.

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Jingping Liu, Xingchen Peng, Yan Zhou, Ziyan Liu, Jie Zhai, Ronghao Chen, Huacan Wang, Xiaofeng Jia

ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts remains underexplored.
Approach:	They propose a benchmark to evaluate MLLMs' ability to distinguish left from right from a subject-centered perspective.
Outcome:	The proposed benchmarks show that even the best performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans.

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Nuo Chen, Ning Wu, Jianhui Chang, Linjun Shou, Jia Li

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)

Challenge:	Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs.
Approach:	They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems.
Outcome:	The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions.

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Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dongmei Zhang, Jia Li

Making Language Models Better Tool Learners with Execution Feedback (2024.naacl-long)

Challenge:	Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages.
Approach:	They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM .
Outcome:	The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios.

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Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia, Huajun Chen, Ningyu Zhang

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

Challenge:	Existing tool learning methodologies induce large language models to utilize tools indiscriminately . Existing frameworks that teach language models when and how to use tools propagate errors rather than enhance performance.
Approach:	They propose a framework that enables large language models to continually learn through feedback derived from tool execution.
Outcome:	The proposed framework can make large language models selectively use tools . it improves accuracy while enhancing insufficient tool learning, it shows .

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

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

Generating Natural Language Proofs with Verifier-Guided Search (2022.emnlp-main)

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Kaiyu Yang, Jia Deng, Danqi Chen

SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning (2025.findings-acl)

Challenge:	Existing stepwise methods struggle to generate valid proof steps based on the hypothesis . instead, they generate invalid steps .
Approach:	They propose a stepwise method which generates relevant steps conditioning on the hypothesis.
Outcome:	The proposed method improves correctness of predicted proofs from 27.7% to 33.3% on EntailmentBank and RuleTaker.

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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, Xuming Hu

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)

Challenge:	Existing methods for MU forget quality and model utility are not fully explored for safety in MLLMs.
Approach:	They propose a safety unlearning benchmark for MLLMs to measure over-forgetting . they propose MU methods to forget quality and model utility .
Outcome:	The proposed method reduces over-forgetting by 79.5% while maintaining forget quality and model utility.

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Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu

Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)

Challenge:	Initial outpatient consultations are costly and difficult to scale to real-time intake.
Approach:	They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control.
Outcome:	The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability.

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Tong Zhang, Junhong Liu, Chen Huang, Jia Liu, Hongru Liang, Zujie Wen, Wenqiang Lei

DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection.
Approach:	They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset.
Outcome:	The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing.

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Hanghui Guo, Jia Zhu, Shimin Di, Weijie Shi, Zhangze Chen, Jiajie Xu

Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)

Challenge:	Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content.
Approach:	They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs.
Outcome:	The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method.

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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang

A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation (2025.acl-srw)

Challenge:	Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity.
Approach:	They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states.
Outcome:	The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters.

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Yepai Jia, Yatu Ji, Xiang Xue, Shilei@imufe.edu.cn Shilei@imufe.edu.cn, Qing-Dao-Er-Ji Ren, Nier Wu, Na Liu, Chen Zhao, Fu Liu

Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)

Challenge:	Back-translation methods rely on large-scale parallel corpora to enhance performance, but ignore the semantic quality of monolingual data.
Approach:	They propose a method which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data.
Outcome:	The proposed method improves translation accuracy and fluency by +1.7 on all three translation tasks.

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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

Robustness in NLP

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.

Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)

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Jie Shi, Xi Cao, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects.
Approach:	They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.
Outcome:	The proposed framework outperforms existing methods that generate SQL queries directly.

Generative Input: Towards Next-Generation Input Methods Paradigm (2024.findings-acl)

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Keyu Ding, Yongcan Wang, Zihang Xu, Zhenzhen Jia, Enhong Chen

Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources (2026.findings-acl)

Challenge:	generative models have been used for various NLP tasks but their application in the field of input methods remains under-explored.
Approach:	They propose a novel Generative Input paradigm that uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback.
Outcome:	The proposed paradigm achieves state-of-the-art in the Full-mode Key-sequence to Characters task and surpasses GPT-4 in the other input methods.

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Tiejin Chen, Xiaoou Liu, Longchao Da, Jia Chen, Evangelos E. Papalexakis, Hua Wei

CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)

Challenge:	Existing methods for uncertainty quantification fail to capture multifaceted nature of natural language generation.
Approach:	They propose a multi-resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure.
Outcome:	The proposed framework outperforms existing methods on CoQA, NQ_Open, and HotpotQA.

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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo

Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization (2026.acl-long)

Challenge:	Relevance modeling between queries and items is a key component of commercial search engines.
Approach:	They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge.
Outcome:	The proposed model achieves convincing performance compared to strong baselines.

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Jia Liu, Jiaxin Luo, Weiwen Xu, Jonathan M. Garibaldi, Xiao-Kun Wu, Yixue Hao, Min Chen

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)

Challenge:	Chain-of-Thought (CoT) prompts elicit multi-step reasoning, yet how reasoning related structure is expressed during training remains poorly understood.
Approach:	They propose a framework that tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities.
Outcome:	The proposed framework tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities.

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Jie Shi, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Chenhao Xie, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL.
Approach:	They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever .
Outcome:	The proposed method improves embedding-based retriever and reduces cost.

SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)

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Junhao Jia, Hanwen Zheng, Yueyi Wu, Huangwei Chen, Haishuai Wang, Jiajun Bu, Lei Wu

Explainability & Interpretability in NLP

Challenge:	Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction.
Approach:	They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments.
Outcome:	The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks.

Large Language Models Meet Harry Potter: A Dataset for Aligning Dialogue Agents with Characters (2023.findings-emnlp)

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Nuo Chen, Yan Wang, Haiyun Jiang, Deng Cai, Yuhan Li, Ziyang Chen, Longyue Wang, Jia Li

Dialogue Systems & Conversational Agents

Challenge:	Existing models that can create open-domain dialogue agents lack character representation and annotations.
Approach:	They propose a dataset to study character alignment and character representation . it includes all dialogue sessions from the Harry Potter series and includes annotations .
Outcome:	The proposed dataset can be used as a universal benchmark for character-driven LLMs.

PIArena: A Platform for Prompt Injection Evaluation (2026.acl-long)

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Runpeng Geng, Chenlong Yin, Yanting Wang, Ying Chen, Jinyuan Jia

Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models (2023.findings-emnlp)

Challenge:	OWASP identifies prompt injection as the top-1 security risk for large language models (LLMs).
Approach:	They propose a unified platform for prompt injection evaluation that integrates state-of-the-art attacks and defenses into a platform.
Outcome:	The proposed attack exploits state-of-the-art defenses and generalizes them on diverse datasets and attacks.

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Yufan Wu, Yinghui He, Yilin Jia, Rada Mihalcea, Yulong Chen, Naihao Deng

FASTTRACK: Reliable Fact Tracing via Clustering and LLM-Powered Evidence Validation (2024.findings-emnlp)

Challenge:	Theory of Mind (ToM) is the ability to reason about one's own and others' mental states.
Approach:	They propose a higher-order theory of mind benchmark and introduce a new deception mechanism to evaluate ToM reasoning.
Outcome:	The proposed benchmarks show that the LLMs are not performing well on higher-order tasks.

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Si Chen, Feiyang Kang, Ning Yu, Ruoxi Jia

Text Clustering

Challenge:	Existing methods to fact tracing rely on assessing the similarity between training samples and the query along a certain dimension, such as lexical similarity, gradient, or embedding space.
Approach:	They propose a new approach that harnesses the capabilities of Large Language Models to validate supportive evidence for queries and clusters the training database towards a reduced extent for LLMs to trace facts.
Outcome:	The proposed approach outperforms existing methods in accuracy and efficiency while being x33 faster than TracIn.

Cross-Domain NER using Cross-Domain Language Modeling (P19-1)

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Chen Jia, Xiaobo Liang, Yue Zhang

Adversarial Self-Supervised Data-Free Distillation for Text Classification (2020.emnlp-main)

Challenge:	Existing methods for named entity recognition (NER) use labeled data for both source and target domains.
Approach:	They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer.
Outcome:	The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters.

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Xinyin Ma, Yongliang Shen, Gongfan Fang, Chen Chen, Chenghao Jia, Weiming Lu

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)

Challenge:	Existing knowledge distillation algorithms rely on the accessibility of the training dataset, which may be unavailable due to privacy issues.
Approach:	They propose a data-free distillation method for a pre-trained transformer-based model that uses plug & play Embedding Guessing to craft pseudo embeddings from the teacher's hidden knowledge.
Outcome:	The proposed method is the first data-free distillation framework designed for NLP tasks.

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Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu

Parameter-Efficient Fine-Tuning via Circular Convolution (2025.findings-acl)

Challenge:	Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models.
Approach:	They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data .
Outcome:	The proposed framework achieves strong utility compared to baselines.

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Aochuan Chen, Jiashun Cheng, Zijing Liu, Ziqi Gao, Fugee Tsung, Yu Li, Jia Li

BiasFilter: An Inference-Time Debiasing Framework for Large Language Models (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, but its intrinsic low-rank characteristic may limit its performance.
Approach:	They propose a low-rank adaptive method that uses low-ranked matrices to represent weight changes.
Outcome:	The proposed method reduces trainable parameters and mitigates heavy memory consumption associated with full delta matrices by sequentially multiplying mathbf A and mathbb B with the activation.

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Xiaoqing Cheng, Ruizhe Chen, Hongying Zan, Yuxiang Jia, Min Peng