Papers by Hong Chen

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models (2025.findings-naacl)

Challenge:	Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored.
Approach:	They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs.
Outcome:	The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs.

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Zizhang Chen, Peizhao Li, Xiaomeng Dong, Pengyu Hong

SP3: Enhancing Structured Pruning via PCA Projection (2024.findings-acl)

Challenge:	Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns .
Approach:	They propose to combine ensembling and multi-tasking approaches to reduce uncertainty in EHRs by using multi-tapping methods.
Outcome:	The proposed framework reduces model uncertainty in white-box and black-box settings, and improves model transparency in both settings.

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Yuxuan Hu, Jing Zhang, Zhe Zhao, Chen Zhao, Xiaodong Chen, Cuiping Li, Hong Chen

Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)

Challenge:	Structured pruning is a widely used technique for reducing the size of pre-trained language models, but current methods overlook the potential of compressing the hidden dimension d in PLMs.
Approach:	They propose a structured pruning approach that projectes features into a space defined by principal components before masking the hidden dimension d in pre-trained language models.
Outcome:	Experiments on benchmarks show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy.

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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang

Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)

Challenge:	Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target.
Approach:	They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success.
Outcome:	The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design.

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Qingting Xu, Yu Hong, Fubang Zhao, Kaisong Song, Yangyang Kang, Jiaxiang Chen, Guodong Zhou

Low-Resource NLP

Challenge:	Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences.
Approach:	They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach .
Outcome:	The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results.

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)

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Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, Juanzi Li

P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion (2021.findings-emnlp)

Challenge:	Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression.
Approach:	They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression.
Outcome:	The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline.

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Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)

Challenge:	Existing methods to encode and match entity pairs have only a few observed reference entity pairs.
Approach:	They propose a model that infers and leverages paths that can expressively encode the relation of two entities.
Outcome:	The proposed model outperforms the state-of-the-art models by 11.2– 14.2% in terms of Hits@1.

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Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen

Taming "Zombie" Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution (2026.acl-long)

Challenge:	Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs.
Approach:	They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning.
Outcome:	The proposed framework improves retrieval and QA performance over existing methods.

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Taolin Zhang, Pukun Zhao, Qizhou Chen, Jiuheng Wan, Chen Chen, Xiaofeng He, Chengyu Wang, Richang Hong

Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning (2025.findings-emnlp)

Challenge:	Existing approaches to improve efficiency of multi-agent systems rely on aggressive graph topology evolution . however, such hard pruning overlooks the potential for "zombie" agents to recover and contribute in subsequent discussion rounds.
Approach:	They propose a Markov state-aware framework for resilient multi-agent evolution that manages agent collaboration through soft state transitions.
Outcome:	The proposed framework outperforms baselines and significantly reduces token consumption through state-aware agent scheduling.

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Yuhan Chen, Bowei Zou, Yifan Fan, Yuchong Chen, Shujun Cao, Yu Hong

Question Answering

Challenge:	Attributed Question Answering models are not yet leveraged to enhance their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning.
Approach:	They propose a progressive progressive curriculum learning approach that optimizes both encoder-decoder and decoder-only AQA models.
Outcome:	The proposed approach improves both encoder-decoder and decoder-only AQA models on the quotesum benchmark.

Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022.findings-emnlp)

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Xirui Ke, Jing Zhang, Xin Lv, Yiqi Xu, Shulin Cao, Cuiping Li, Hong Chen, Juanzi Li

Natural Language Interfaces

Challenge:	Recent rise of conversational applications has promoted the development of conversation KBQA (ConvKBQA).
Approach:	They propose a framework to produce a full-fledged rewritten question based on conversation history and then reason the answer by existing single-turn KBQA models.
Outcome:	The proposed framework produces a full-fledged rewritten question based on the conversation history and reasoned the answer by existing single-turn KBQA models.

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

Challenge:	a number of information extraction tasks require task-specific training.
Approach:	They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model .
Outcome:	The proposed framework outperforms the existing methods on open information extraction tasks.

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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

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

Challenge:	Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
Approach:	They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure .
Outcome:	The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains.

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

Sentiment Analysis

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

E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)

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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang

Explainability & Interpretability in NLP

Challenge:	Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models.
Approach:	They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words.
Outcome:	Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model.

DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)

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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao

PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)

Challenge:	Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models .
Approach:	They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently .
Outcome:	The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width.

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Hong Chen, Zhenhua Fan, Hao Lu, Alan Yuille, Shu Rong

Information Extraction & Text Mining

Challenge:	Existing methods for coreference resolution are based on word2vec-like representations of entities.
Approach:	They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers .
Outcome:	The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo .

SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)

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Han Liu, Shuqin Li, Xiaotong Zhang, Yuanyuan Wang, Feng Zhang, Hongyang Chen, Hong Yu

Information Retrieval

Challenge:	Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance.
Approach:	They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories.
Outcome:	The proposed method significantly outperforms state-of-the-art models.

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

PCQPR: Proactive Conversational Question Planning with Reflection (2024.emnlp-main)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Chen

Natural Language Interfaces

Challenge:	Current CQG methods focus on immediate context without strategic consideration of the specified conversational outcome.
Approach:	They propose a method that uses a planning algorithm inspired by Monte Carlo Tree Search to generate contextually relevant questions.
Outcome:	The proposed approach surpasses existing methods in e-learning and customer service fields . it generates contextually appropriate questions strategically devised to reach a specified outcome .

Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)

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Shasha Guo, Jing Zhang, Xirui Ke, Cuiping Li, Hong Chen

Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)

Challenge:	Existing methods on knowledge base question generation focus on refining the quality of a single generated question.
Approach:	They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth.
Outcome:	The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.

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Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang

SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation (2024.findings-naacl)

Challenge:	Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality.
Approach:	They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data.
Outcome:	The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data.

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Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen

DSM: Question Generation over Knowledge Base via Modeling Diverse Subgraphs with Meta-learner (2022.emnlp-main)

Challenge:	Existing methods have significantly boosted the performance of Knowledge Base Question Generation (KBQG) through pre-trained language models thanks to the richly endowed semantic knowledge.
Approach:	They propose a framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG by combining skeleton heuristic guidance with a soft prompting approach.
Outcome:	The proposed framework incorporates "skeleton heuristics" which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions.

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Shasha Guo, Jing Zhang, Yanling Wang, Qianyi Zhang, Cuiping Li, Hong Chen

SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)

Challenge:	Existing methods on knowledge base question generation learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraph.
Approach:	They propose a graph contrastive learning-based retriever to model diverse subgraphs with meta-learner to learn semantics-specific and semantics agnostic knowledge on and across these tasks.
Outcome:	The proposed approach reduces learning difficulty and improves performance on two widely-adopted benchmarks on KBQG.

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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni

QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)

Challenge:	Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging .
Approach:	They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks.
Outcome:	The proposed framework bridges the domain gap between LLMs and recommendation tasks.

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Taolin Zhang, Haidong Kang, Dongyang Li, Qizhou Chen, Xiaofeng He, Chengyu Wang, Richang Hong

Dual-Reasoner: Bridging Interleaved Atomicity and Streaming Latency via Thinking-while-Talking (2026.findings-acl)

Challenge:	Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations.
Approach:	They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing.
Outcome:	The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing.

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Yangzhuo Li, Shengpeng Ji, Yifu Chen, Tianle Liang, Haoyu Yang, null Junboli, Jun Fang, Lin Li, Qingyang Hong

The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research (2025.acl-long)

Challenge:	Existing methods to integrate Chain-of-Thought into spoken dialogue models incur prohibitive latency.
Approach:	They propose a Streaming Masking Mechanism to ensure uninterrupted audio streaming . they use a quadruple-constraint system to reconstruct logical atomicity .
Outcome:	Experimental results show that Dual-Reasoner improves speech generation performance with low latency.

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Hong Chen, Misha Teplitskiy, David Jurgens

Challenge:	Academic citations are widely used for evaluating research and tracing knowledge flows.
Approach:	They propose a computational pipeline to quantify citation fidelity at the sentence level by identifying citations in citing papers and corresponding claims in cited papers.
Outcome:	The proposed pipeline identifies citations in citing papers and the corresponding claims in cited papers and applies supervised models to measure fidelity at the sentence level.

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning (2022.emnlp-main)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Hong Chen, Duc Vo, Hiroya Takamura, Yusuke Miyao, Hideki Nakayama

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)

Challenge:	Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.
Approach:	They propose a novel Story Evaluation method that mimics human preference when judging a story . the model is based on a well-annotated dataset and a longformer-encoder-decoder .
Outcome:	The proposed method is applicable to machine-generated and human-written stories.

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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu

Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)

Challenge:	Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains.
Approach:	They propose a training framework that operationalizes this principle through coarse-to-fine budgeting.
Outcome:	Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines.

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Xinyu Li, Kexi Chen, Jiajie Shen, Ying Zheng, Hong Lu, Jin Zhao, Xin Wang

Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023.findings-emnlp)

Challenge:	In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix.
Approach:	They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance.
Outcome:	The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance.

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Jiaxiang Chen, Yu Hong, Qingting Xu, Jianmin Yao

Information Extraction & Text Mining

Challenge:	Aspect Term Extraction (ATE) is a task of automatically extracting aspect terms from sentences.
Approach:	They propose to automatically rewrite sentences from virtual experts with different roles . they leverage ChatGPT to determine virtual experts in the considered domains .
Outcome:	The proposed method can be used to expand the predictions obtained on the original sentences without retraining or fine-tuning the baseline extractors.

SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2021.findings-emnlp)

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Hong Chen, Hiroya Takamura, Hideki Nakayama

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

Challenge:	Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context.
Approach:	They propose a task of context-aware text generation in the scientific domain to exploit the contributions of context in generated texts.
Outcome:	The proposed dataset comprehensively benchmarks the efficacy of the proposed dataset in generating description and paragraph.

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

PATIENT-𝜓: Using Large Language Models to Simulate Patients for Training Mental Health Professionals (2024.emnlp-main)

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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Ruiyi Wang, Stephanie Milani, Jamie Chiu, Jiayin Zhi, Shaun Eack, Travis Labrum, Samuel Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei Fang, Zhiyu Chen

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)

Challenge:	Mental illness remains one of the most critical public health issues.
Approach:	They propose a patient simulation framework for cognitive behavior therapy training that uses large language models to act as a simulated therapy patient.
Outcome:	The proposed framework improves the skill acquisition and confidence of mental health trainees beyond textbooks, videos, and role-play with non-patients.

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Jun Bai, Xiaofeng Zhang, Chen Li, Hanhua Hong, Xi Xu, Chenghua Lin, Wenge Rong

G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026.findings-acl)

Challenge:	Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem .
Approach:	They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation .
Outcome:	The proposed methods perform well with superiorities in effectiveness and efficiency.

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Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Lingling Zhang, Yixin Chen, Xuequn Shang

Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022.naacl-main)

Challenge:	Large language models (LLMs) have good performance in multiple reasoning tasks, but are limited to adapt the rapid knowledge updates in the real-world scenario.
Approach:	They propose an LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel.
Outcome:	The proposed framework achieves superiority in terms of accuracy and interpretability on three benchmarks.

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Rujun Han, Hong Chen, Yufei Tian, Nanyun Peng

AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026.findings-acl)

Challenge:	Existing systems that generate flashbacks are monotonic and lack explicit guidance on how to insert them.
Approach:	They propose to use event temporal orders to encode events as temporal prompts . they leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines .
Outcome:	The proposed method generates more interesting stories with flashbacks while maintaining textual diversity, fluency, and temporal coherence.

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Xuan Luo, Lewei Yao, Lanqing Hong, Kai Chen, Dehua Tao, Daxin Tan, Yukun Deng, Ruifeng Xu, Jing Li

AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments (2024.lrec-main)

Challenge:	Existing benchmarks focus on cognitive abilities, such as knowledge retrieval, complex reasoning, and instruction following, largely overlooking empathy evaluation.
Approach:	They propose to benchmark two core empathetic capabilities of omnimodal large models (OLMs) generating empatries by comprehending affective cues from multi-modal inputs and judging empathy of audio responses without relying on text transcription.
Outcome:	The proposed benchmark outperforms existing models with audio output capabilities but is unreliable for evaluating fine-grained paralinguistic expressiveness.

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Huadai Liu, Xu Wenqiang, Xuan Lin, Jingjing Huo, Hong Chen, Zhou Zhao

Visual Data in NLP

Challenge:	Argument mining is a thriving task in natural language processing, but its generalization is limited by existing datasets.
Approach:	They propose to use a dataset to help model argument mining . the dataset AntCritic supports both argument component detection and argument relation prediction tasks.
Outcome:	The proposed model can detect arguments and identify their relationships automatically.

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

Extracting and Understanding the Superficial Knowledge in Alignment (2025.naacl-long)

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Runjin Chen, Gabriel Jacob Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura

Challenge:	Recent studies have shown that alignment of large language models with human values and preferences requires substantial data and computation resources.
Approach:	They propose a method to extract and isolate superficial knowledge from aligned models by focusing on the shallow modifications to the final token selection process.
Outcome:	The proposed method extracts and isolates superficial knowledge from aligned models, focusing on the shallow modifications to the final token selection process.

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

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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

Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)

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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Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He

CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment (2024.findings-acl)

Challenge:	Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability.
Approach:	They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics.
Outcome:	The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%.

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Jixiang Hong, Quan Tu, Changyu Chen, Gao Xing, Ji Zhang, Rui Yan

Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy (2021.findings-emnlp)

Challenge:	Existing language models that generate harmful responses are constrained by their inherent capability.
Approach:	They propose to align large language models with human preferences from AI feedback.
Outcome:	The proposed framework improves the alignment of large language models with human preferences from AI feedback.

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Tao Huang, Hong Chen

Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025.findings-emnlp)

Challenge:	Latent Dirichlet allocation (LDA) is a widely used topic model to discover the latent semantic of text data.
Approach:	They propose to combine a subsampling method with CGS to improve efficiency while amplifying privacy by using a novel metric, the efficiency–privacy function.
Outcome:	The proposed algorithm improves efficiency while amplifying privacy while subsampling in CGS increases efficiency while preserving privacy.

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Simin Hong, Jun Sun, Hongyang Chen

Controllable Dialogue Simulation with In-context Learning (2022.findings-emnlp)

Challenge:	Emotional reasoning is essential for improving human-AI interactions, especially in mental health support and empathetic systems.
Approach:	They propose a third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisals, and Reappraisal.
Outcome:	The proposed model outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability.

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Zekun Li, Wenhu Chen, Shiyang Li, Hong Wang, Jing Qian, Xifeng Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to generate annotated dialogues require crowdsourcing, which is expensive and time-consuming.
Approach:	They propose a dialogue simulation method based on large language model in-context learning that generates new dialogues and annotations in a controllable way.
Outcome:	The proposed method can expand a small set of dialogue data with minimum or zero human involvement and parameter update.

Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)

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Chih-Te Lai, Yi-Te Hong, Hong-You Chen, Chi-Jen Lu, Shou-De Lin

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)

Challenge:	Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text.
Approach:	They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others.
Outcome:	The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency.

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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng

HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks.
Approach:	They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality.
Outcome:	The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research.

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Hanhua Hong, Yizhi LI, Jiaoyan Chen, Sophia Ananiadou, Xiaoli Li, Jung-jae Kim, Chenghua Lin

User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)

Challenge:	Existing approaches to automate computational research use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance.
Approach:	They propose a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages.
Outcome:	The proposed framework improves the paper2code benchmark and significantly reduces hallucination in the evaluation.

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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu

A Generation-based Deductive Method for Math Word Problems (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops.
Approach:	They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty.
Outcome:	The proposed approach shows significant gains in both user satisfaction and exploration diversity.

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Yuxuan Hu, Jing Zhang, Haoyang Li, Cuiping Li, Hong Chen

From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

Challenge:	Existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations.
Approach:	They propose a multivariate directed acyclic graph (mDAG) which generates the topological ordering of mDAg by equipping a generation model with a re-encoder to keep the deductive property but avoid the expensive enumeration of deductive methods.
Outcome:	The proposed model performs well on the widely used benchmarks and solves multivariate operators on the CMWPA benchmark.

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Jiawei Liu, Xun Gong, Muli Yang, Xingrui Yu, Fen Fang, Xulei Yang, Ivor Tsang, Yunfeng hu, Hong Chen, Qing Guo

Challenge:	Recent large language model-based AD research offers new avenues to address this challenge.
Approach:	They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome:	The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.

ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)

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Huadai Liu, Rongjie Huang, Xuan Lin, Wenqiang Xu, Maozong Zheng, Hong Chen, Jinzheng He, Zhou Zhao

Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)

Challenge:	Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy .
Approach:	They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio.
Outcome:	The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications .

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Zijin Hong, Zheng Yuan, Hao Chen, Qinggang Zhang, Feiran Huang, Xiao Huang

Programming Languages in NLP

Challenge:	Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs.
Approach:	They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models.
Outcome:	The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models.

Adaptive Textual Label Noise Learning based on Pre-trained Models (2023.findings-emnlp)

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Shaohuan Cheng, Wenyu Chen, Fu Mingsheng, Xuanting Xie, Hong Qu

Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking (2024.emnlp-main)

Challenge:	Existing approaches to learning with noisy labels are limited due to the time and labor costs involved.
Approach:	They propose an adaptive warm-up and hybrid training frameworks to learn with noisy labels based on pre-trained models.
Outcome:	The proposed approach performs comparable or even surpasses state-of-the-art methods in various noise scenarios, including scenarios with the mixture of multiple types of noise.

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Jun Bai, Zhuofan Chen, Zhenzi Li, Hanhua Hong, Jianfei Zhang, Chen Li, Chenghua Lin, Wenge Rong

Information Extraction & Text Mining

Challenge:	Existing methods for text ranking are based on intuition, but their estimated transferability may not align well with the objectives of text ranking.
Approach:	They propose to compute expected rank as transferability, explicitly reflecting the model’s ranking capability.
Outcome:	The proposed method shows significant improvements over previous classification-oriented TE methods, human intuition, and ChatGPT with minor time consumption.

BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)

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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models (2025.emnlp-main)

Challenge:	Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors .
Approach:	They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents.
Outcome:	The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries.

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Shrey Pandit, Jiawei Xu, Junyuan Hong, Zhangyang Wang, Tianlong Chen, Kaidi Xu, Ying Ding

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) generate plausible but factually incorrect outputs, posing serious risks to patient safety and clinical decision-making.
Approach:	They propose a benchmark for medical hallucination detection using 10,000 question-answer pairs derived from PubMedQA.
Outcome:	The proposed model achieves an F1 score as low as 0.625 for detecting 'hard' category hallucinations.

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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen

Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding.
Approach:	They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations.
Outcome:	The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks.

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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python.
Approach:	They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability.
Outcome:	The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline.

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

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)

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

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Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, Yaodong Yang

Challenge:	Using large-scale annotation data, large language models can generate noise, errors and biases, leading to unexpected behaviours.
Approach:	They propose a dataset to promote safety alignment in large language models . they separate helpfulness and harmlessness annotations for question-answering pairs .
Outcome:	The proposed dataset provides 44.6k prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels, with answers generated by Llama-family models.

Infinite Babble: Inflating 3D Vision-Language Model Inference Overhead via Adversarial Geometric Perturbation (2026.findings-acl)

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Shuoyang Sun, Jiaxin Hong, Yv Zhang, Kuofeng Gao, Hao Fang, Fan Mo, Bin Chen, Shu-Tao Xia

Robustness in NLP

Challenge:	3D Vision-Language Models (VLMs) are critical cognitive backbone for spatial intelligence, but their reliance on autoregressive decoding introduces a fundamental vulnerability regarding inference efficiency.
Approach:	They propose a framework that triggers computational and economic exhaustion in 3D-VLMs by injecting imperceptible noise that forces the model into a state of pathological verbosity.
Outcome:	The proposed framework amplifies output length and energy consumption by up to 6.45, demonstrating a potent capability to drain system resources.

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)

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Hieu Tran, Junda Wang, Yujan Ting, Hong Yu, Weijing Huang, Terrence Chen

Ethical NLP

Challenge:	Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare.
Approach:	They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components .
Outcome:	Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores.

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Hongyu Zhu, Yan Chen, Jing Yan, Jing Liu, Yu Hong, Ying Chen, Hua Wu, Haifeng Wang

Robustness in NLP

Challenge:	a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs .
Approach:	They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts .
Outcome:	The proposed model is more robust than other models on natural questions with 32 linguistic perturbations.

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions (2026.acl-long)

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Guo Gan, Yuxuan Ding, Cong Chen, Yuwei Ren, Yin Huang, Hong Zhou

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations (2026.findings-acl)

Challenge:	Existing reinforcement learning methods are expensive due to high latency and sample inefficiency . Currently, RL is limited to one-to-one state-action pairs .
Approach:	They propose a framework that shifts the training paradigm to Single State Multiple Actions and introduce a group-wise advantage estimator based on the averaged critic outputs.
Outcome:	The proposed framework achieves 7.5% and 8.3% success rate improvements on AndroidLab and AndroidWorld over UI-TARS-1.5-7B and attains 1.4x higher training efficiency than existing methods.

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Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe, Benjamin Roth, Barbara Plank

Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction (2026.findings-acl)

Challenge:	Natural Language Inference (NLI) datasets often exhibit label variation.
Approach:	They extend LiTEx taxonomy to two NLI datasets and jointly analyze label variation and label variation.
Outcome:	The proposed model combines explanations as a lens to analyze variation in NLI annotations and examine individual differences in reasoning.

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Ming Li, Han Chen, Yunze Xiao, Jian Chen, Hong Jiao, Tianyi Zhou

Cognitive Modeling

Challenge:	Accurate estimation of item (question or task) difficulty suffers from the cold start problem.
Approach:	They propose to use large-scale empirical analysis to examine human-AI Difficulty Alignment . they find that models struggle to simulate the capability limitations of students .
Outcome:	The proposed model size is not reliably helpful for human-AI alignment . high performance often impedes accurate difficulty estimation, the authors say .

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)

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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye

Looking Beyond the One: Operationalizing and Eliciting Visual Ambiguity in VLLMs (2026.acl-long)

Challenge:	Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks.
Approach:	They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs.
Outcome:	The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks.

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Yuchong Chen, Bowei Zou, Yuhan Chen, Yifan Fan, Xinyu Li, Shujun Cao, Yu Hong

Visual Data in NLP

Challenge:	Visual question answering systems typically collapse ambiguity, committing to a single interpretation during decoding and evaluation.
Approach:	They operationalize ambiguity as the existence of multiple answer-supporting regions in an image . they show that ambiguities are already encoded in their internal representations .
Outcome:	The proposed approach makes ambiguity observable without exhaustive annotations . ambiguities are already encoded in models, but not reliably expressed in outputs despite hidden states .

Dialogue Language Model with Large-Scale Persona Data Engineering (2025.naacl-industry)

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Mengze Hong, Chen Jason Zhang, Chaotao Chen, Rongzhong Lian, Di Jiang

Dialogue Systems & Conversational Agents

Challenge:	Existing persona-consistent dialogue models lack robustness due to limited scale and diversity of datasets.
Approach:	They propose an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialog dataset to enhance persona consistency.
Outcome:	The proposed model generates vast persona dialogue datasets and addresses invalid persona bias.

Exploring the Choice Behavior of Large Language Models (2025.findings-acl)

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Weidong Wu, Qinlin Zhao, Hao Chen, Lexin Zhou, Defu Lian, Hong Xie

Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)

Challenge:	Large Language Models (LLMs) are increasingly being adopted across various domains where they help to make choices.
Approach:	They construct a virtual QA platform that includes three different experimental conditions, with four models from GPT and Llama series participating in repeated experiments.
Outcome:	The proposed model includes three experimental conditions and four models from GPT and Llama series.

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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li

Challenge:	Large language models (LLMs) have shown impressive performance in code understanding and generation.
Approach:	They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks.
Outcome:	The proposed models have shown to be highly effective in coding tasks.

Mixture of insighTful Experts (MoTE): The Synergy of Reasoning Chains and Expert Mixtures in Self-Alignment (2025.acl-long)

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Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James Kwok

A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)

Challenge:	Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment.
Approach:	They propose a framework that synergistically combines reasoning chains and expert mixtures to improve self-alignment.
Outcome:	The proposed framework improves model safety, jailbreak resistance, and over-refusal capabilities, achieving performance comparable to OpenAI’s state-of-the-art o1 model.

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Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen

SAM Decoding: Speculative Decoding via Suffix Automaton (2025.acl-long)

Challenge:	Existing knowledge bases (KBs) can explicitly facilitate the QA process.
Approach:	They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models.
Outcome:	Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model.

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Yuxuan Hu, Ke Wang, Xiaokang Zhang, Fanjin Zhang, Cuiping Li, Hong Chen, Jing Zhang

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing (2025.naacl-long)

Challenge:	Speculative decoding (SD) methods are inefficient and rely on single retrieval resources.
Approach:	They propose a retrieval-based speculative decoding method that adapts the suffix automaton for efficient draft generation by utilizing the generating text sequence and static text corpus.
Outcome:	The proposed method can find the longest suffix match and can be integrated with existing methods to generalize to broader domains.

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Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have demonstrated their effectiveness in human-guided dialogues, but tasks in the real world are more complex and require greater autonomy from LLMs.
Approach:	They propose to characterize LLM-guided conversation into three fundamental components: Goal Navigation, Context Management, Empathetic Engagement and implement an interviewing environment for the evaluation of LLMs.
Outcome:	The proposed LLM outperforms baseline LLMs in interviewing quality and autobiography generation quality.

Federated Incremental Named Entity Recognition (2025.coling-main)

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Zesheng Liu, Qiannan Zhu, Cuiping Li, Hong Chen

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)

Challenge:	Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting.
Approach:	They propose a model which allows for catastrophic forgetting of old entity types . they propose adaptive pseudo labeling and a prototypical relation distillation loss .
Outcome:	The proposed model overcomes catastrophic forgetting problem on old entity types with semantic shift.

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Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, Zhicheng Dou

AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026.acl-long)

Challenge:	Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data.
Approach:	They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models.
Outcome:	The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF.

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Taolin Zhang, Dongyang Li, Chen Chen, Qizhou Chen, Jiuheng Wan, Xiaofeng He, Chengyu Wang, Richang Hong

Question Answering

Challenge:	Empirical results show that AMATA outperforms baseline approaches, knowledge-augmented frameworks, and LLMs on knowledge-intensive QA benchmarks.
Approach:	They propose an Adaptive Multi-Agent Trajectory Alignment framework that integrates external knowledge to improve response interpretability and factual grounding.
Outcome:	The proposed framework outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks.

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

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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

Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-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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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens

Challenge:	a key intent behind many emails is to get a reply from the recipient.
Approach:	They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations.
Outcome:	The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates .

Harnessing and Evaluating the Intrinsic Extrapolation Ability of Large Language Models for Vehicle Trajectory Prediction (2025.naacl-long)

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Jiawei Liu, Yanjiao Liu, Xun Gong, Tingting Wang, Hong Chen, Yunfeng Hu

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

Challenge:	Emergent abilities of large language models (LLMs) have advanced their application in autonomous vehicle research.
Approach:	They propose a framework that leverages LLMs’ built-in extrapolation capabilities for vehicle trajectory prediction, enabling them to understand traffic agents' behavior and interactions over time.
Outcome:	The proposed framework enables off-the-shelf, frozen LLMs to achieve competitive trajectory prediction performance with qualitative analyses revealing their enhanced understanding of complex, multi-agent traffic scenarios.

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

Character-centric Story Visualization via Visual Planning and Token Alignment (2022.emnlp-main)

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

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Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, Nanyun Peng

Visual Data in NLP

Challenge:	Story visualization is a task that requires machines to understand long text inputs and produce a globally consistent image sequence that illustrates the contents of the story.
Approach:	They propose to augment VQ-VAE with a text-to-visual-token (transformer) architecture to enable multiple image generation based on a complete story.
Outcome:	The proposed method excels at preserving characters and produces higher quality image sequences compared with baselines.

LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference (2025.emnlp-main)

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Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe, Barbara Plank

Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)

Challenge:	Existing evidence of human label variation in Natural Language Inference (NLI) however, within-label variation is an additional challenge.
Approach:	They propose a linguistically-informed taxonomy for categorizing free-text explanations in English that captures different reasoning strategies behind NLI explanations with a particular focus on within-label variation.
Outcome:	The proposed taxonomy can be used to classify explanations in English using a linguistically-informed taxonomies.

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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang

P2 Law: Scaling Law for Post-Training After Model Pruning (2025.acl-long)

Challenge:	Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult.
Approach:	They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples.
Outcome:	The proposed approach improves preference while preserving utility.

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Xiaodong Chen, Yuxuan Hu, Xiaokang Zhang, Yanling Wang, Cuiping Li, Hong Chen, Jing Zhang

Challenge:	Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs).
Approach:	They propose to use model pruning techniques to maintain high performance while reducing hardware requirements for large language models (LLMs).
Outcome:	The proposed model pruning law can be generalized to larger dataset sizes, larger model sizes, and higher pruning rates, offering valuable insights for resource allocation in pruned LLMs.

Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)

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Zijun Chen, Wenbo Hu, Guande He, Zhijie Deng, ZHeng ZHang, Richang Hong