Papers by Bin Sun

Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)

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

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Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui

Programming Languages in NLP

Challenge:	Structured Query Language (SQL) is the cornerstone for data-driven decision-making.
Approach:	They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework.
Outcome:	The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework.

AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)

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Bin Wang, Xunlong Zou, Geyu Lin, Shuo Sun, Zhuohan Liu, Wenyu Zhang, Zhengyuan Liu, AiTi Aw, Nancy F. Chen

Formally Specifying the Intended Behavior of the Program: LLM-Driven Neuro-Symbolic Program Specification Synthesis (2026.acl-demo)

Challenge:	Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases.
Approach:	They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks .
Outcome:	The proposed evaluation targets speech understanding, audio scene understanding, and voice understanding (paralinguistic) . no single model excels consistently across all tasks, the paper found .

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Cheng Wen, Hu Junjie, YiKun Hu, Jie Su, Bin Yu, Dugang Liu, Zhiwu Xu, Weidi Sun, Shengchao Qin, Cong Tian

Multimodality

Challenge:	Formal verification typically requires developers to write detailed formal specifications . a formal verification system that generates candidate specifications is costly and error-prone .
Approach:	They propose an LLM-driven neuro-symbolic demonstration system that reframes specification writing as constrained structured synthesis.
Outcome:	The proposed system reduces hallucinations and produces proof-ready annotations.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT - A Text-to-SQL Parsing Comparison (2023.findings-emnlp)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Shuo Sun, Yuchen Zhang, Jiahuan Yan, Yuze Gao, Donovan Ong, Bin Chen, Jian Su

Programming Languages in NLP

Challenge:	a number of open-source large language models claim to be performing better than commercial ones . however, these models fall short of the performance achieved by closed-source models like GPT-3.5 .
Approach:	They evaluate six popular large language models against each other to evaluate their performance . authors say open-source models are not as effective as those built by commercial models .
Outcome:	a new set of models claim to match or surpass the language understanding abilities of commercial models . the results show that the models performed far below the performance of closed-source models compared to open-source ones .

A Multi-persona Framework for Argument Quality Assessment (2025.acl-long)

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Bojun Jin, Jianzhu Bao, Yufang Hou, Yang Sun, Yice Zhang, Huajie Wang, Bin Liang, Ruifeng Xu

Challenge:	Existing methods for argument quality assessment do not consider multi-perspective evaluation due to subjective nature of arguments.
Approach:	They propose a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models.
Outcome:	The proposed framework outperforms baselines while providing comprehensive multi-perspective rationales on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets.

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)

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Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

Information Retrieval

Challenge:	Existing paper search systems lack detailed information to support finer-grained queries.
Approach:	They propose a paper-based index that transforms abstract-based corpus index into hierarchical index tree and offline can support paper search queries.
Outcome:	The proposed system achieves the SOTA performance and excels in fine-grained scenarios.

PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)

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Yang Sun, Muyi Wang, Jianzhu Bao, Bin Liang, Xiaoyan Zhao, Caihua Yang, Min Yang, Ruifeng Xu

Text Classification

Challenge:	Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions.
Approach:	They propose a method to model the inter-relationships among three subtasks within a generative framework.
Outcome:	The proposed method achieves state-of-the-art performance on two AM benchmarks.

Exploring Conditional Variational Mechanism to Pinyin Input Method for Addressing One-to-Many Mappings in Low-Resource Scenarios (2024.acl-short)

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Bin Sun, Jianfeng Li, Hao Zhou, Fandong Meng, Kan Li, Jie Zhou

ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)

Challenge:	Experimental results demonstrate the superior performance of our method.
Approach:	They propose to leverage conditional variational mechanism to simplify pinyin IME . they employ a strategy that facilitates interaction between pinyan and Chinese character information .
Outcome:	The proposed method improves the performance of pinyin input method engine (IME) under low-resource conditions.

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Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong

Natural Language Interfaces

Challenge:	acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) .
Approach:	They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions.
Outcome:	The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets.

Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder (2022.findings-emnlp)

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Bin Sun, Shaoxiong Feng, Yiwei Li, Weichao Wang, Fei Mi, Yitong Li, Kan Li

Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)

Challenge:	Existing efforts to identify and avoid CDM to facilitate dialogue learning failed to solve the problem.
Approach:	They propose a Sentence Semantic Segmentation guided Conditional Variational Auto-Encoder which can model and take advantage of the CDM data.
Outcome:	The proposed method can model and take advantages of the CDM data.

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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)

Challenge:	Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities.
Approach:	They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion .
Outcome:	The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT .

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)

Challenge:	Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem.
Approach:	They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules.
Outcome:	The results show that pre-trained language models are forgetful and pre-training leads to retentive models .

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Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, Ruifeng Xu

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)

Challenge:	Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks.
Approach:	They propose a generative framework where expected outputs of AM are framed as a simple target sequence.
Outcome:	The proposed framework achieves state-of-the-art on two AM benchmarks.

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Jianzhu Bao, Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu

Structured Data in NLP

Challenge:	Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation.
Approach:	They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences .
Outcome:	The proposed method outperforms the current state-of-the-art model.

Backdooring Neural Code Search (2023.acl-long)

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Weisong Sun, Yuchen Chen, Guanhong Tao, Chunrong Fang, Xiangyu Zhang, Quanjun Zhang, Bin Luo

Inducing Argument Facets for Faithful Opinion Summarization (2025.findings-emnlp)

Challenge:	Neural code search models are used to find code snippets from online repositories . however, their security aspect is rarely studied .
Approach:	They propose to use off-the-shelf code snippets from online repositories to find desired code . they propose to inject a backdoor into neural code search models which return buggy code if attacker modifies one variable/function name .
Outcome:	The proposed attack outperforms baselines on two neural code search models by 60%.

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Jian Wang, Yanjie Liang, Yuqing Sun, Bin Gong

Text Generation

Challenge:	Faithful opinion summarization task involves generating a summary that covers the majority and minority opinions in documents.
Approach:	They propose a facets-guided opinion summarization method that induces facets and partitions documents into multiple facet-specific sets.
Outcome:	The proposed method outperforms state-of-the-art methods and multiple LLMs on two representative datasets and shows it can be used in specialty domains.

SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration (2026.findings-acl)

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Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen

Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)

Challenge:	Existing benchmarks emphasize general-domain retrieval or static scientific question answering . SciExplore focuses on scientific database navigation, ambiguous literature retrieval, missing reference completion, and cross-source structured knowledge synthesis tasks.
Approach:	They propose a benchmark to evaluate scientific information-seeking and reasoning capabilities of LLMs and agents.
Outcome:	The new benchmark assesses the capabilities of state-of-the-art LLMs and agents in scientific research workflows.

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Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie

StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation.
Approach:	They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models.
Outcome:	The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility.

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Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021.naacl-main)

Challenge:	Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures.
Approach:	They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization.
Outcome:	The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives.

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Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, Bin He

Representation Learning

Challenge:	Recent studies reveal a security threat to natural language processing models, called the Backdoor Attack.
Approach:	They propose to hack a model by modifying one single word embedding vector without sacrificing accuracy on clean samples.
Outcome:	The proposed method is more efficient and stealthier on sentiment analysis and sentence-pair classification tasks.

A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models (2021.naacl-main)

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Kaiyuan Liao, Yi Zhang, Xuancheng Ren, Qi Su, Xu Sun, Bin He

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

Challenge:	Existing methods to accelerate inference speed of pre-trained language models are limited to local representations of exit layer . current models are associated with large memory requirement and high computational cost, which slow down inference and further encumber the application of PLMs.
Approach:	They propose a method to exit early without passing through all inference layers . they take into consideration all the linguistic information embedded in the past layers a global perspective .
Outcome:	The proposed method outperforms existing methods by a large margin . it uses linguistic information embedded in the past layers and future features . the proposed method is scalable and cost-effective .

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan

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

Challenge:	Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Approach:	They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy.
Outcome:	The proposed model surpasses all baselines on various logical reasoning benchmarks.

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

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

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

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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

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

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

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

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)

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

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Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao

Natural Language Interfaces

Challenge:	Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability.
Approach:	They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability.
Outcome:	The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset.

Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)

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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)

Challenge:	Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs).
Approach:	They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts.
Outcome:	The proposed framework outperforms strong baselines while significantly improving explainability.

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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie

Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)

Challenge:	Existing typography solutions lack adaptability, creativity, and computational efficiency.
Approach:	They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs.
Outcome:	The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO.

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Yuhang He, Jianzhu Bao, Yang Sun, Bin Liang, Min Yang, Bing Qin, Ruifeng Xu

Towards Fewer Hallucinations in Knowledge-Grounded Dialogue Generation via Augmentative and Contrastive Knowledge-Dialogue (2023.acl-short)

Challenge:	Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.
Approach:	They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning.
Outcome:	The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines.

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Bin Sun, Yitong Li, Fei Mi, Fanhu Bie, Yiwei Li, Kan Li

Diversifying Neural Dialogue Generation via Negative Distillation (2022.naacl-main)

Challenge:	Existing knowledge-grounded dialogue generation models face the hallucination problem . Existing models generate inappropriate knowledge and generate inconsistent responses .
Approach:	They propose an Augmentative and Contrastive Knowledge Dialogue Expansion Framework to enhance existing knowledge dialogue models by polarizing optimization objectives and weak knowledge generation ability.
Outcome:	The proposed framework expands existing training sets and smooths the optimization objective that enables models to generate ground-truth with or without gold knowledge.

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Yiwei Li, Shaoxiong Feng, Bin Sun, Kan Li

An Exploratory Study on Model Compression for Text-to-SQL (2023.findings-acl)

Challenge:	Existing approaches to generate generic responses are ignoring low-frequency but generic responses and bringing low- frequency but meaningless responses.
Approach:	They propose a negative training paradigm that reminds dialogue models not to generate high-frequency responses during training.
Outcome:	The proposed method outperforms previous methods in the generic response problem while minimizing low-frequency but meaningless responses.

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Shuo Sun, Yuze Gao, Yuchen Zhang, Jian Su, Bin Chen, Yingzhan Lin, Shuqi Sun

Programming Languages in NLP

Challenge:	Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases.
Approach:	They propose to apply model compression techniques to sketch-based and sequence-to-sequence Text-toSQL models.
Outcome:	The proposed models have higher inference efficiency and respond better to model compression than sequence-to-sequence models.

Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)

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Zhen Ke, Liang Shi, Songtao Sun, Erli Meng, Bin Wang, Xipeng Qiu

Towards Generalized Open Information Extraction (2022.findings-emnlp)

Challenge:	Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge.
Approach:	They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task.
Outcome:	Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

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Bowen Yu, Zhenyu Zhang, Jingyang Li, Haiyang Yu, Tingwen Sun, Jian Liu, Yongbin Li, Bin Wang

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)

Challenge:	Open Information Extraction (OpenIE) models are evaluated on in-domain test sets aside from the training corpus, which violates the initial task principle of domain-independence.
Approach:	They propose to generalize OpenIE over unseen target domains with different data distributions from source training domains.
Outcome:	The proposed method beats the previous methods in both in- and out-of-domain settings by 6.0% in F1 score absolutely.

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Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu Sun, Bin He

Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects (2021.naacl-main)

Challenge:	Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations.
Approach:	They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models.
Outcome:	Extensive experiments show that the proposed framework can improve results over existing models.

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Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu Sun, Bin He

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

Challenge:	Existing neural network tuning methods cause instance-wise side effects . et al., 2018: a new approach to perform neural network surgery .
Approach:	They propose to perform neural network surgery by only changing 10-5 parameters . they propose to use a dynamic selecting method to achieve the best overall performance .
Outcome:	The proposed method achieves the best overall performance and induces fewer instance-wise side effects by changing only 10-5 of the parameters.

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

Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining (2024.findings-acl)

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

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Yang Sun, Guanrong Chen, Caihua Yang, Jianzhu Bao, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)

Challenge:	Recent advances in AM models overlook the integration of supplementary discourse structure information, resulting in suboptimal outcomes.
Approach:	They propose a framework which generates discourse structure-aware prefixes for each layer of the generation model.
Outcome:	The proposed framework achieves state-of-the-art performance on two AM benchmarks.

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Hao Sun, Zhexin Zhang, Fei Mi, Yasheng Wang, Wei Liu, Jianwei Cui, Bin Wang, Qun Liu, Minlie Huang

Natural Language Interfaces

Challenge:	A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections.
Approach:	They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system.
Outcome:	The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values .

Probing Structural Knowledge from Pre-trained Language Model for Argumentation Relation Classification (2022.findings-emnlp)

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Yang Sun, Bin Liang, Jianzhu Bao, Min Yang, Ruifeng Xu

Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders (2021.acl-long)

Challenge:	Argumentation relation classification (ARC) is the most challenging subtask of argumentation mining.
Approach:	They propose a dual prior graph neural network to explore probing knowledge and syntactical information for comprehensively modeling the relationship between AC pairs.
Outcome:	The proposed model outperforms the state-of-the-art models on three public datasets.

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Bin Sun, Shaoxiong Feng, Yiwei Li, Jiamou Liu, Kan Li

Probing Graph Decomposition for Argument Pair Extraction (2023.findings-acl)

Challenge:	Conditional Variational AutoEncoders (CVAE) can enhance the diversity and informativeness of responses in open-domain dialogue generation tasks.
Approach:	They propose a Conditional Variational AutoEncoder (CVAE) that regularizes latent variables and introduces group information to regularize them.
Outcome:	Empirical results show that the proposed model can significantly boost responses in well-established open-domain dialogue datasets.

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Yang Sun, Bin Liang, Jianzhu Bao, Yice Zhang, Geng Tu, Min Yang, Ruifeng Xu

Structured Data in NLP

Challenge:	Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion.
Approach:	They propose a method to extract interactive argument pairs from two passages . they propose to decompose the probing graph into four sub-graphs based on inter- and intra-passage perspectives .
Outcome:	The proposed method improves on strong baselines on two benchmark datasets.

Regularizing Dialogue Generation by Imitating Implicit Scenarios (2020.emnlp-main)

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Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, Xu Sun

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

Challenge:	Existing models for dialogue generation lack the flexibility to handle such freedoms.
Approach:	They propose to take into account dialogue history and future conversation to implicitly reconstruct the scenario knowledge.
Outcome:	The proposed approach outperforms state-of-the-art models on diversity and relevance and expresses scenario-specific knowledge.

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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.

Maximal Clique Based Non-Autoregressive Open Information Extraction (2021.emnlp-main)

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Bowen Yu, Yucheng Wang, Tingwen Liu, Hongsong Zhu, Limin Sun, Bin Wang

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)

Challenge:	Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence.
Approach:	They propose a non-autoregressive framework that generates a fact graph and a graph with an edge linking two nodes that belong to the same fact.
Outcome:	The proposed framework outperforms current state-of-the-art methods on two benchmark datasets and significantly outperformed the existing ones.

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Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang

Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs (2024.lrec-main)

Challenge:	Named entity recognition (NER) is a fundamental NLP task.
Approach:	They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers .
Outcome:	The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark.

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Chenxi Sun, Hongzhi Zhang, Zijia Lin, Jingyuan Zhang, Fuzheng Zhang, Zhongyuan Wang, Bin Chen, Chengru Song, Di Zhang, Kun Gai, Deyi Xiong

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)

Challenge:	Large language models have demonstrated exceptional capability in natural language understanding and generation, but their generation speed is limited by the inherently sequential nature of their decoding process.
Approach:	They propose a method that accelerates decoding process without sacrificing quality . they propose lexical unit decoding, which can be integrated with other methods .
Outcome:	The proposed method significantly reduces decoding time while maintaining quality while maintaining output quality.

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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya

Multimodality

Challenge:	Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA .
Approach:	They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities.
Outcome:	a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region .

Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)

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Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao

Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations (2024.lrec-main)

Challenge:	Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers.
Approach:	They propose to use chain of thought prompting to solve reasoning tasks with large language models.
Outcome:	The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets.

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Donovan Ong, Shuo Sun, Jian Su, Bin Chen

Sentiment Analysis

Challenge:	Existing studies on Emotion Recognition in Conversations (ERC) focus on training and testing models on the same datasets and there is no prior work on adaptability.
Approach:	They propose to use contrastive learning to prioritize emotional features over a linguistic style and refining emotion predictions with pseudo-emotion intensity score to improve model's robustness and accuracy in diverse conversational contexts.
Outcome:	The proposed techniques reduce reliance on linguistic artifacts found in TV transcripts and improve model’s robustness and accuracy in diverse conversational contexts.

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation (2024.findings-acl)

Challenge:	Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness .
Approach:	They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image.
Outcome:	The proposed model outperforms existing models on key downstream tasks.

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Yiwei Li, Fei Mi, Yitong Li, Yasheng Wang, Bin Sun, Shaoxiong Feng, Kan Li