Papers by Bang Wang

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

Challenge:	Recent prompt learning-based approaches have shown promising improvements on the ECI task . however, they are subject to the delicate design of multiple prompts and positive correlations between the main task and derivate tasks.
Approach:	They propose an event causality identification model that uses contrastive learning to enhance both positive and negative demonstrations.
Outcome:	The proposed model improves on the event-related causality identification task . it uses contrastive learning to enhance both positive and negative demonstrations .

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

Responsible & Trustworthy NLP

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

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)

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Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen

Text Generation

Challenge:	Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content .
Approach:	They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating .
Outcome:	The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations.

Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration (2026.acl-long)

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Zhenhua Wang, Chunlei Wang, Yue Geng, Bang Wang

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) improve event synthesis, but most are monolithic and often process overlapping evidence with bursty reporting patterns.
Approach:	They propose a multi-agent framework that casts TLS as a newsroom-like collaboration.
Outcome:	Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency.

Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval (2021.acl-long)

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Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng

Efficient Classification of Long Documents via State-Space Models (2023.emnlp-main)

Challenge:	Existing methods for document hashing combine only one of semantics and neighborhood information, lacking a theoretical principle to guide the integration process.
Approach:	They propose to encode neighborhood information with a graph-induced Gaussian distribution and integrate it with generative models.
Outcome:	The proposed model can be trained as efficiently as state-of-the-art methods on benchmark datasets.

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Peng Lu, Suyuchen Wang, Mehdi Rezagholizadeh, Bang Liu, Ivan Kobyzev

SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)

Challenge:	Existing transformer-based models can only process long documents with limited computational resources due to their quadratic computation time and space.
Approach:	They propose to use state-space models for long document classification tasks instead of using sparse or hierarchical structures to solve this problem.
Outcome:	The proposed model performs comparable to self-attention models while being 36% more efficient.

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Xiaoqiang Wang, Bang Liu, Siliang Tang, Lingfei Wu

Natural Language Interfaces

Challenge:	Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions.
Approach:	They propose a question generation framework with controllable comprehension types for machine reading comprehension models.
Outcome:	Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task.

Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)

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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen

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

Challenge:	Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property.
Approach:	They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness.
Outcome:	The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

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

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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

Reasoning

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

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Dialogue Systems & Conversational Agents

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)

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Wei Xiang, Bang Wang, Lu Dai, Yijun Mo

Challenge:	Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR).
Approach:	They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network.
Outcome:	The proposed model achieves state-of-the-art on the PDTB 3.0 corpus.

INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment (2025.findings-emnlp)

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Dekun Wu, Frederik Brudy, Bang Liu, Yi Wang

Challenge:	Existing virtual environments for LLM agent research focus on task solving or social simulation . existing environments for virtual environments lack physical grounding of social behaviors .
Approach:	They propose a virtual environment that tightly integrates physical and social dynamics . IndoorWorld is a heterogeneous multi-agent environment that integrates social and physical dynamics based on a simulation of physical environments .
Outcome:	The proposed environment integrates physical and social dynamics into a heterogeneous multi-agent environment.

R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression (2025.findings-acl)

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Xiaoqiang Wang, Suyuchen Wang, Yun Zhu, Bang Liu

Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)

Challenge:	Existing memory solutions that store information via parameters struggle with reliable retrieval.
Approach:	They propose a memory network that optimizes both information Retention and Retrieval through Reversible context compression.
Outcome:	The proposed memory network outperforms conventional memory modules in long-horizon interaction tasks like conversational agents and achieves state-of-the-art performance in language modeling and retrieval-augmented generation tasks.

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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions.
Approach:	They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations.
Outcome:	The proposed framework measures the agent's higher-order social cognition in multi-turn conversations.

FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval (2025.findings-naacl)

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Jinlin Wang, Suyuchen Wang, Ziwen Xia, Sirui Hong, Yun Zhu, Bang Liu, Chenglin Wu

Text Generation

Challenge:	Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, but struggle with tasks requiring simultaneous retrieval of multiple facts.
Approach:	They propose a method that refines context through successive rounds of rewriting to address this problem by finding all Crucial Texts (FACT)
Outcome:	The proposed method improves multi-fact retrieval performance across tasks, though improvements are less notable in general-purpose QA scenarios.

On Collaborating Small and Large Models For Few-shot Intent Detection (2025.findings-emnlp)

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Peng Chen, Bang Wang

MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)

Challenge:	Existing methods for few-shot intent detection face high inference cost and label interference.
Approach:	They propose a framework that integrates a small prediction model with a large language model for FSID.
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

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Bang Yang, Fenglin Liu, Xian Wu, Yaowei Wang, Xu Sun, Yuexian Zou

Multimodality

Challenge:	Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language.
Approach:	They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles .
Outcome:	The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs.

Gloss-Free End-to-End Sign Language Translation (2023.acl-long)

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Kezhou Lin, Xiaohan Wang, Linchao Zhu, Ke Sun, Bang Zhang, Yi Yang

Machine Translation

Challenge:	a study of sign language translation without gloss annotations focuses on the problem of gloss annotation . gloss annotation is hard to acquire, especially in large quantities, and limits the domain coverage of translation datasets .
Approach:	They propose a gloss-free end-to-end sign language translation framework to solve this problem . gloss annotations are hard to acquire, especially in large quantities, they argue .
Outcome:	The proposed framework improves sign language translation performance on large-scale datasets . gloss annotations are hard to acquire, especially in large quantities .

Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)

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Lu Dai, Bang Wang, Wei Xiang, Yijun Mo

Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection (2024.findings-acl)

Challenge:	Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments.
Approach:	They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models.
Outcome:	The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset.

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Songtao Liu, Bang Wang, Wei Xiang, Han Xu, Minghua Xu

Challenge:	Existing studies on ideology detection focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies.
Approach:	They propose a concept semantics-enhanced framework for multifaceted ideology detection . it enables concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics.
Outcome:	The proposed framework achieves state-of-the-art in the cross-topic scenario and on the benchmark dataset.

A Framework of Reflective Agents with Adaptive Collaboration for Attributed Summary Generation (2026.findings-acl)

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Yu Chen, Peng Chen, Ziwei Zheng, Bang Wang

Information Extraction & Text Mining

Challenge:	Existing prompt-based summarization approaches face limitations such as positional preference, poor citation quality and sensitivity to uninformative documents.
Approach:	They propose a framework of Reflective Agents with Adaptive Collaboration for attributed summarization that performs iterative summarizing via reflective agents’ collaboration.
Outcome:	The proposed framework outperforms baselines on the ALCE benchmark in factual correctness and citation quality.

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Xiaoqiang Wang, Lingfei Wu, Tengfei Ma, Bang Liu

ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition (2022.coling-1)

Challenge:	Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks.
Approach:	They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones.
Outcome:	The proposed framework dissociates the language-related capabilities from cognition-related ones and breaks down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems.

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Wei Xiang, Zhenglin Wang, Lu Dai, Bang Wang

TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition (2023.findings-acl)

Challenge:	Existing paradigms for Implicit Discourse Relation Recognition (IDRR) do not exploit linguistic evidence embedded in the pre-training process.
Approach:	They propose a new paradigm to detect and classify relation sense between two text segments without an explicit connective.
Outcome:	The proposed method significantly outperforms the state-of-the-art algorithms even with fewer training data.

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Wei Xiang, Chao Liang, Bang Wang

What Would Happen Next? Predicting Consequences from An Event Causality Graph (2024.findings-emnlp)

Challenge:	Existing prompt learning models for IDRR use multiple-prompt decisions from three different yet much similar connective prediction templates.
Approach:	They propose to fuse three related tasks to fuse the learned features of auxiliary tasks to create a prompt learning model that can be used to boost the main task.
Outcome:	The proposed model outperforms the ConnPrompt in the training phase and in the testing phase.

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Chuanhong Zhan, Wei Xiang, Liang Chao, Bang Wang

Structured Data in NLP

Challenge:	Existing script event prediction task forcasts the subsequent event based on an event script chain, but the evolution of historical events is more complicated in real world scenarios.
Approach:	They propose a Causality Graph Event Prediction task that forecasts consequential event based on an Event Causity Graph (ECG).
Outcome:	The proposed model outperforms the advanced competitors for the CGEP task.

Feeding What You Need by Understanding What You Learned (2022.acl-long)

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Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang, Lingfei Wu

Green & Sustainable NLP

Challenge:	Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance.
Approach:	They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner.
Outcome:	The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks.

Improving Context Fidelity via Native Retrieval-Augmented Reasoning (2025.emnlp-main)

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Suyuchen Wang, Jinlin Wang, Xinyu Wang, Shiqi Li, Xiangru Tang, Sirui Hong, Xiao-Wen Chang, Chenglin Wu, Bang Liu

Reasoning

Challenge:	Existing approaches to fidelity to contexts rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without improving utilization of the given context.
Approach:	They propose a native retrieval-augmented reasoning framework that integrates in-context evidence with the model’s own retrieval capabilities.
Outcome:	The proposed approach outperforms supervised fine-tuning, retrieval-augmented generation methods, and external retrieval solutions on multiple real-world and counterfactual QA benchmarks.

Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)

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Songtao Liu, Ziling Luo, Minghua Xu, Lixiao Wei, Ziyao Wei, Han Yu, Wei Xiang, Bang Wang

Challenge:	Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues.
Approach:	They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset.
Outcome:	The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets.

Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification (2025.findings-acl)

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Wei Xiang, Chuanhong Zhan, Qing Zhang, Bang Wang