Papers by Wei Shi

Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)

Challenge:	Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings.
Approach:	They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references .
Outcome:	The proposed model improves the reliability of reference-based metrics with augmented reference sets.

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Jiangjie Chen, Wei Shi, Ziquan Fu, Sijie Cheng, Lei Li, Yanghua Xiao

Empowering Math Problem Generation and Reasoning for Large Language Model via Synthetic Data based Continual Learning Framework (2025.emnlp-main)

Challenge:	Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge.
Approach:	They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge.
Outcome:	The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions.

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Qian Wan, Wangzi Shi, Jintian Feng, Shengyingjie Liu, Luona Wei, Zhicheng Dai, Jianwen Sun

Knowledge Verification to Nip Hallucination in the Bud (2024.emnlp-main)

Challenge:	Existing learning frameworks for large language models (LLMs) for math problem generation are limited and lack quality data.
Approach:	They propose a synthetic data based continual learning framework to improve LLMs ability for MPG and math reasoning.
Outcome:	The proposed framework improves performance on large language models and math reasoning using supervised fine-tuning, data synthesis and direct preference optimization.

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Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, Shuming Shi

v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (2026.acl-long)

Challenge:	Recent studies have shown that large language models generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination.
Approach:	They propose a novel approach to align large language models to evaluate knowledge boundaries based on external knowledge to reduce hallucinations .
Outcome:	The proposed approach reduces hallucinations across six benchmarks using foundation LLMs of varying backbones and scales.

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Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng

Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)

Challenge:	Humor enriches our daily lives and appears in many forms, from jokes and cartoons to comedies and viral videos.
Approach:	They introduce a video humor understanding benchmark to test their ability to understand humor from visual cues.
Outcome:	The proposed video humor understanding benchmark is based on a collection of short videos . it features rich annotations and a study of environmental sound that can enhance humor .

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Shi Zong, Ashutosh Baheti, Wei Xu, Alan Ritter

Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)

Challenge:	a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus .
Approach:	They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots.
Outcome:	The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks.

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Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)

Challenge:	Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning.
Approach:	They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation.
Outcome:	The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets.

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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)

Challenge:	Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach:	They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome:	The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.

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Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi

ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average (2024.emnlp-industry)

Challenge:	generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging.
Approach:	They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator.
Outcome:	The proposed framework outperforms baseline models in a variety of experiments.

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Shaojie Shi, Xiaoyu Tan, Xihe Qiu, Chao Qu, Kexin Nie, Yuan Cheng, Wei Chu, Xu Yinghui, Yuan Qi

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning (2025.findings-acl)

Challenge:	Large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks.
Approach:	They propose a framework which uses pre-training datasets to rewrite instructions and generate negative responses to preserve the performance of the original LLM.
Outcome:	The proposed framework can erase the pre-training data while maintaining the performance of the original model.

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Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li

PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback (2026.findings-acl)

Challenge:	Large Language Models (LMMs) struggle with simple tasks such as geometry, e.g., arithmetic, and reasoning.
Approach:	They propose to leverage code as supervision for cross-modal alignment . they propose to use FigCodifier and ImgCode-8.6M to synthesize novel mathematical figures .
Outcome:	The proposed model surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%.

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Wei Xia, Jin Wu, Haoran Shi, Xiangyu Wang, Chanjin Zheng

Explainability & Interpretability in NLP

Challenge:	Existing approaches to Automated Essay Scoring (AES) treat scoring and feedback as separate components, resulting in fragmentation.
Approach:	They propose a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation.
Outcome:	The proposed framework integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation.

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

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

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

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

Robustness in NLP

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

Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)

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Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)

Challenge:	Existing techniques for relevance and semantic matching cannot be easily adapted to the other.
Approach:	They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.
Outcome:	The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.

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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong

ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models (2026.eacl-long)

Challenge:	Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values.
Approach:	They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though.
Outcome:	The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets.

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Yachuan Liu, Xiaochun Wei, Lin Shi, Xinnuo Li, Bohan Zhang, Paramveer Dhillon, Qiaozhu Mei

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Large language models (LLMs) struggle with ex-ante reasoning—making inferences or predictions without access to future information.
Approach:	They propose a benchmark that assesses LLMs’ ex-ante inference ability across four tasks: stock prediction, question answering, Wikipedia event generation, and scientific publication generation.
Outcome:	The proposed benchmark assesses LLMs’ ex-ante inference ability across four tasks.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

Investigating Transformer-Guided Chaining for Interpretable Natural Logic Reasoning (2023.findings-acl)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Kanagasabai Rajaraman, Saravanan Rajamanickam, Wei Shi

Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)

Challenge:	Natural logic reasoning has received increasing attention lately, with several datasets and neural models proposed, though with limited success.
Approach:	They propose to iteratively perform 1-step neural inferences and chain together the results to generate a multi-step reasoning trace.
Outcome:	The proposed method has high accuracies on a multi-hop First-Order Logic (FOL) reasoning benchmark.

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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)

Challenge:	Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations.
Approach:	They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries.
Outcome:	The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision.

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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan

RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)

Challenge:	Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world.
Approach:	They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning.
Outcome:	The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task.

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Jinbo Su, Lingzhe Gao, Wei Li, Shihao Liu, Haojie Lei, Xinyi Wang, Yuanzhao Guo, Ke Wang, Daiting Shi, Dawei Yin

Information Retrieval

Challenge:	Large language models (LLMs) exhibit remarkable capabilities across many tasks, but face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches; and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues.
Approach:	They propose a Chinese Spelling Check system with RAG and multi-task learning that integrates dynamic knowledge retrieval and entity-centric RAG to address rare entities.
Outcome:	The proposed system outperforms existing baselines in the CSC task and achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset.

Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)

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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu

Information Extraction & Text Mining

Challenge:	Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model.
Approach:	They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures.
Outcome:	The proposed framework yields significant performance gains on Twitter and other platforms.

CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)

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Ruifeng Yuan, Wanxing Chang, Weiwei Cao, Bowen Shi, Zhongyu Wei, Ling Zhang, Jianpeng Zhang

Natural Language Interfaces

Challenge:	Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching.
Approach:	They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report .
Outcome:	The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors .

Automatic Article Commenting: the Task and Dataset (P18-2)

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Lianhui Qin, Lemao Liu, Wei Bi, Yan Wang, Xiaojiang Liu, Zhiting Hu, Hai Zhao, Shuming Shi

Challenge:	Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums.
Approach:	They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics.
Outcome:	The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models.

SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026.findings-acl)

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Boyan Shi, Wei Chen, Shuyuan Zhao, Junfeng Shen, Shengnan Guo, Shaojiang Wang, Huaiyu Wan

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization.
Approach:	They propose a parameter-efficient mixture-of-experts framework for task-adaptive learning that aligns textual semantics with the most suitable experts for precise routing.
Outcome:	The proposed framework outperforms the state-of-the-art methods and holds excellent task generalization capabilities.

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi

A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)

Challenge:	Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses .
Approach:	They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query.
Outcome:	The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses.

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Haoran Yang, Deng Cai, Huayang Li, Wei Bi, Wai Lam, Shuming Shi

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)

Challenge:	Neural text generation is notorious for repetitive loops and tedious outputs.
Approach:	They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text .
Outcome:	The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality.

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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei

Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (2024.findings-acl)

Challenge:	Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision.
Approach:	They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone.
Outcome:	The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data.

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Yafu Li, Zhilin Wang, Leyang Cui, Wei Bi, Shuming Shi, Yue Zhang

Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)

Challenge:	Existing work focuses on detecting (partially) AI-generated texts, but paraphrasing is commonly employed in various application scenarios for text refinement and diversity.
Approach:	They propose a framework for paraphrased text span detection that takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.
Outcome:	The proposed framework can detect paraphrased text spans within a text . it takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.

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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen

Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)

Challenge:	Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China.
Approach:	They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform.
Outcome:	The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower.

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Dianqing Lin, null Aruukhan, Hongxu Hou, Shuo Sun, Wei Chen, Yichen Yang, Guo Dong Shi

Machine Translation

Challenge:	Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages.
Approach:	They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources.
Outcome:	The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively.

Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)

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Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

Find-the-Common: A Benchmark for Explaining Visual Patterns from Images (2024.lrec-main)

Challenge:	Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion.
Approach:	They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem.
Outcome:	The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task.

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Yuting Shi, Naoya Inoue, Houjing Wei, Yufeng Zhao, Tao Jin

Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains (D19-1)

Challenge:	Recent advances in Instruction-fine-tuned Vision and Language Models (IVLMs) have prompted some studies to analyze the reasoning capabilities of IVLMs.
Approach:	They introduce a vision and language task for Inductive Visual Reasoning that uses common attributes across visual scenes to find common answers.
Outcome:	The proposed model can archive with 48% accuracy on the FTC, compared with state-of-the-art models.

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Wei Shi, Vera Demberg

CAST: Achieving Stable LLM-based Text Analysis for Data Analytics (2026.findings-acl)

Challenge:	Discourse relation classification is one of the most difficult tasks in discourse parsing.
Approach:	They propose a bidirectional encoder representation from transformer model that encodes a representation of likely next sentences.
Outcome:	The proposed model outperforms the state-of-the-art system in 11-way classification by 8% points on the standard PDTB dataset.

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Jinxiang Xie, Zihao Li, Wei He, Rui Ding, Shi Han, Dongmei Zhang

Low-Resource NLP

Challenge:	Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labeling.
Approach:	They propose a framework that enhances output stability by constraining the model’s latent reasoning trajectory.
Outcome:	The proposed framework improves stability by constraining the model's latent reasoning trajectory.

Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)

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Wei Shi, Jiewen Wu, Xiwen Yang, Nancy Chen, Ivan Ho Mien, Jung-Jae Kim, Pavitra Krishnaswamy

Representation Learning

Challenge:	Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes.
Approach:	They propose novel neural networks for encoding medical codes based on textual, structural and statistical characteristics using a single deep learning baseline model.
Outcome:	The proposed methods improve the accuracy of medical codes based on their textual, structural and statistical characteristics.

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection (2022.emnlp-main)

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Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Natural Language Interfaces

Challenge:	Existing retrieval-based dialogue systems suffer from slow inference or huge number of parameters.
Approach:	They propose a lightweight fully convolutional architecture for response selection using convolution.
Outcome:	The proposed architecture extracts matching features of context and response from 3D views.

RACE: Retrieval-augmented Commit Message Generation (2022.emnlp-main)

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Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

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

Challenge:	Existing approaches to automatically generate commit messages are repetitive or redundant.
Approach:	They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message.
Outcome:	The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation.

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

Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)

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

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Ruipeng Jia, Xingxing Zhang, Yanan Cao, Zheng Lin, Shi Wang, Furu Wei

SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution (2026.findings-acl)

Challenge:	Existing methods to translate sentences to other languages using heuristics are challenging.
Approach:	They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome:	The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

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Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen, Jingjing Xu

Answer-focused and Position-aware Neural Question Generation (D18-1)

Challenge:	SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks.
Approach:	They propose a two-phase training recipe that decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation.
Outcome:	The proposed model achieves a 60.2% score on the SWE-bench Verified benchmark and is in the top-tier performance bracket of much larger models.

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Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang

On the Additive Compositionality of Task Vectors in Vision–Language Models (2026.eacl-short)

Challenge:	Recent neural network-based approaches generate interrogative words that do not match the answer type.
Approach:	They propose an answer-focused and position-aware neural question generation model to address these issues.
Outcome:	The proposed model outperforms the baseline and outperformed the state-of-the-art system.

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Yuting Shi, Houjing Wei, Naoya Inoue

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	In-context learning (ICL) in large language models (LLMs) has been shown to operate through task vectors, but its extension to vision-language models (VLMs) remains underexplored.
Approach:	They construct visual reasoning tasks with clearly defined subtasks and extract task vectors from few-shot demonstrations.
Outcome:	The proposed model can be extended to vision-language models (VLMs) by adding the vectors of its constituent subtasks.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

Information Retrieval

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (2023.emnlp-main)

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Fanqi Wan, Xinting Huang, Tao Yang, Xiaojun Quan, Wei Bi, Shuming Shi

RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation (2023.emnlp-main)

Challenge:	Existing data for instruction-tuning are inadequate for a wide range of tasks, limiting the scope for nuanced comprehension and interactions within these domains.
Approach:	They propose to use Large Language Models to explore a multitude of variations or possibilities to improve instruction-tuning data by active exploration.
Outcome:	The proposed approach improves domain-specific instruction coverage and shows significant improvements over baselines.

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Yue Zhang, Leyang Cui, Enbo Zhao, Wei Bi, Shuming Shi

Robustness in NLP

Challenge:	Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications.
Approach:	They propose a benchmark to evaluate the context robustness of Grammatical Error Correction systems.
Outcome:	The proposed method improves the accuracy of errors corrected by human annotations.

LegalCore: A Dataset for Event Coreference Resolution in Legal Documents (2025.findings-acl)

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Kangda Wei, Xi Shi, Jonathan Tong, null Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang

Information Extraction & Text Mining

Challenge:	Existing research on event coreference resolution is limited to news articles . existing datasets for news articles are limited to events and coreferences .
Approach:	They present a dataset for the legal domain LegalCore which has been annotated with event and event coreference information.
Outcome:	The legal contract documents annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document.

From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)

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Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)

Challenge:	Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance.
Approach:	They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs.
Outcome:	The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi

LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)

Challenge:	Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory .
Approach:	They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response .
Outcome:	The proposed approach significantly improves the informativeness of the generated responses.

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Xiang Li, Yucheng Zhou, Xiangzhi Wei, Zesheng Shi, Haiyuan Wan, Gong Yifan, Fangming Liu, Jing Li

Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain (2021.acl-short)

Challenge:	Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction.
Approach:	They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning.
Outcome:	The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 .

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Wei Shi, Vera Demberg

Text Classification

Challenge:	Discourse relation classification is a challenging task when the text domain is different from the standard Penn Discourse Treebank (PDTB) training corpus domain.
Approach:	They propose to use the Biomedical Discourse Relation Bank to improve discourse relational argument representation by linking explicit instances of similar relations with a voting pipeline.
Outcome:	The proposed model outperforms the pre-trained BioBERT model by 2% points.

MaDS: Long-Horizon GUI Automation via Synergizing Dual-Layer Memory and Multi-Round Debate (2026.acl-long)

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Pengchen Chen, Shi Chen, Qiming Ye, Xinli Chen, Xinran Li, Wei Xiang

Challenge:	Current methods struggle to distinguish targets in low Signal-to-Noise Ratio environments and lack sufficient pre-execution verification to prevent error accumulation.
Approach:	They propose a Memory-augmented Debate System to ensure precise grounding across diverse interfaces and handle irreversible errors in extended workflows.
Outcome:	The proposed system achieves a 90.23% task success rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey.

Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang

A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022.acl-long)

Challenge:	Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications.
Approach:	They propose a framework that incorporates large language models to improve EA.
Outcome:	The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency.

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Yu Cao, Wei Bi, Meng Fang, Shuming Shi, Dacheng Tao

AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)

Challenge:	Existing models for introducing explicit personas are expensive due to their expensive collection costs.
Approach:	They propose a data manipulation method which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.
Outcome:	The proposed method is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.

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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu

ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2024.findings-acl)

Challenge:	Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities.
Approach:	They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness .
Outcome:	Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds .

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Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

Challenge:	Low-rank adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods.
Approach:	They propose a low-rank adaptation method that adds residual paths during training and merges them together during inference to achieve better results.
Outcome:	The proposed method achieves 2.5x faster convergence speed and improves performance by 14.3% on NLG, NLU, and text-to-image tasks.

Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs (2024.emnlp-main)

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Zheng Wang, Zhongyang Li, Zeren Jiang, Dandan Tu, Wei Shi

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

Challenge:	In the age of mobile internet, personal information is constantly being generated on smartphones.
Approach:	They propose a novel task of crafting personalized agents powered by large language models that leverage a user's smartphone memories to enhance downstream applications with LLM capabilities.
Outcome:	The proposed approach improves 10% over the best existing approach on a real-world dataset and improves usability.

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Green & Sustainable NLP

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)

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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot

Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)

Challenge:	EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions.
Approach:	They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment.
Outcome:	Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction.

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Yafu Li, Leyang Cui, Jianhao Yan, Yongjing Yin, Wei Bi, Shuming Shi, Yue Zhang

Syntactic Text Processing

Challenge:	Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar .
Approach:	They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction.
Outcome:	The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation.

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)

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Yanchen Liu, Mingyu Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, Diyi Yang

MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

Challenge:	Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications.
Approach:	They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs.
Outcome:	The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation.

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

LCMA-SRT: Language-Conditional Mixture-of-Experts Adapters for Joint Multilingual Speech Recognition and Translation (2026.acl-long)

Challenge:	Existing research has focused on evaluating detection methods for specific domains or language models.
Approach:	They build a testbed to detect texts from diverse human writings and LLMs using different detection methods.
Outcome:	Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

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Nanjie Li, Xiaoyong Guo, Hao Huang, Xu Haihua, Wei Shi

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)

Challenge:	Existing hierarchical transducers suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive.
Approach:	They propose a hierarchical transducer with language-conditional Mixture-of-Experts adapters to improve multilingual joint automatic speech recognition and speech translation.
Outcome:	Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines.

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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang

AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints (2024.acl-long)

Challenge:	Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text.
Approach:	They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks.
Outcome:	The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average.

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Yu-Zhe Shi, Haofei Hou, Zhangqian Bi, Fanxu Meng, Xiang Wei, Lecheng Ruan, Qining Wang

Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy (2025.findings-acl)

Challenge:	Domain-specific Language (DSL) is an effective tool to express constraints structurally, but requires case-by-case hand-crafting.
Approach:	They propose a framework to automate domain-specific language constraint design . they propose 'autoDSL' framework to optimize syntactic and semantic constraints .
Outcome:	The framework automates constraint design across domains and abstracts semantic constraints.

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Guo Zhenyuan, Yi Shi, Wenlong Meng, Chen Gong, Chengkun Wei, Wenzhi Chen

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Model merging is a widespread technology in large language models that integrates multiple task-specific LLMs into a unified one.
Approach:	They propose a model merging approach that trains a phishing model capable of stealing privacy using a privacy phish instruction dataset.
Outcome:	The proposed model cloaking method mimics a specialized capability to conceal attack intent, luring users into merging the phishing model.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

Information Extraction & Text Mining

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)

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Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng

A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)

Challenge:	Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome .
Approach:	They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens.
Outcome:	The proposed method reduces token usage by up to 44% while preserving accuracy.

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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu

Challenge:	Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP.
Approach:	They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation .
Outcome:	The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave .

CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026.acl-long)

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Zili Wei, Yilin Wang, Xiaocui Yang, Shi Feng, Weidong Bao, Daling Wang, Zihan Wang, Yifei Zhang

Information Retrieval

Challenge:	Existing methods for iterative retrieval-augmented generation (iRAG) suffer from greedy single-path expansion and granularity–demand mismatch .
Approach:	They propose a model that constructs candidate triples and history-conditionally integrates them to distill core triples to generate the next-hop query.
Outcome:	The proposed model mitigates the greedy single-path expansion and granularity–demand mismatch by preserving multiple plausible evidence chains.

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang

Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)

Challenge:	Inductive reasoning is an important task for large language models (LLMs).
Approach:	They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation.
Outcome:	The proposed method improves inductive reasoning in large language models.

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Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang

Challenge:	Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences.
Approach:	They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences.
Outcome:	Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs.

Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)

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Jiayi Zeng, Yizhe Feng, Mengliang He, Wenhui Lei, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence (2023.findings-emnlp)

Challenge:	Current error-handling works are performed in a passive manner, with explicit error- handling instructions.
Approach:	They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research.
Outcome:	The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances.

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Zhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li

mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)

Challenge:	Existing methods for generating stories with complex plots rely on detailed prompts, which inadvertently limit the creative potential of the generated stories.
Approach:	They propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce to enhance stories’ complexity.
Outcome:	The proposed framework enables generating more diverse plotlines from human-written stories.

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Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang

A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)

Challenge:	Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes.
Approach:	They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis.
Outcome:	The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow.

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Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)

Challenge:	Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory .
Approach:	They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation.
Outcome:	The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses.

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Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun

Fine-Grained Sentence Functions for Short-Text Conversation (P19-1)

Challenge:	Existing knowledge graphs that represent entities in different languages are not covered by existing systems.
Approach:	They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other.
Outcome:	The proposed method significantly outperforms existing systems on two benchmark datasets.

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Wei Bi, Jun Gao, Xiaojiang Liu, Shuming Shi

LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)

Challenge:	Existing research has analyzed various factors indicating the conversational purpose such as emotions, topics, word orders, syntactic patterns and other aspects.
Approach:	They propose to annotate a short-text conversation dataset with annotated sentences and train conversation models conditioned on the sentence functions.
Outcome:	The proposed model can predict the quality of the returned responses.

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Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Lu Xiangju, Junmin Zhu, Wei Zhang

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

Challenge:	Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs.
Approach:	They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts.
Outcome:	The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts.

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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.

M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)

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Zheng Wang, Shu Teo, Jieer Ouyang, Yongjun Xu, Wei Shi

UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)

Challenge:	Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories.
Approach:	They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution.
Outcome:	The proposed framework outperforms baseline methods on three language generation tasks on seven datasets.

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Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources.
Approach:	They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets.
Outcome:	The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources.

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Mengliang He, Jiayi Zeng, Yankai Jiang, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

Programming Languages in NLP

Challenge:	Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error.
Approach:	They propose to use flowcharts to evaluate existing LLMs' code generation capabilities.
Outcome:	The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance.

MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Huang Zihe, Jian Guo, Yuanzhuo Wang

Route Sparse Autoencoder to Interpret Large Language Models (2025.emnlp-main)

Challenge:	Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings.
Approach:	They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment.
Outcome:	The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment.

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Wei Shi, Sihang Li, Tao Liang, Mingyang Wan, Guojun Ma, Xiang Wang, Xiangnan He

Explainability & Interpretability in NLP

Challenge:	Sparse autoencoders (SAEs) extract interpretable and monosemantic features in large language models . prior work focused on feature extraction from a single layer, failing to capture activations that span multiple layers.
Approach:	They propose a framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers.
Outcome:	The proposed framework extracts features from multiple layers while incurring minimal parameter overhead while achieving high interpretability and flexibility.

On Synthetic Data for Back Translation (2022.naacl-main)

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Jiahao Xu, Yubin Ruan, Wei Bi, Guoping Huang, Shuming Shi, Lihui Chen, Lemao Liu

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

Challenge:	Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance.
Approach:	They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields .
Outcome:	The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks.

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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang