Papers by Bing He

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning.
Approach:	They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task.
Outcome:	The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning (2026.acl-long)

Challenge:	Existing approaches to cross-domain text classification focus on one-to-one domain adaptation.
Approach:	They propose a framework for domain generalization that uses contrastive learning with a memory-saving queue.
Outcome:	The proposed framework outperforms state-of-the-art methods on Amazon review sentiment datasets and rumour detection datasets.

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Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang, Vipul Gupta, Jaehwan Jeong, Anisha Gunjal, Tahseen Rabbani, Maria Mazzone, David Randolph IV, Mohammad Mahmoudi Meymand, Gurshaan Chattha, Paula Rodriguez, Diego A. Mares Buendia, Pavit Singh, Michael Liu, Subodh Chawla, Peter Cline, Lucy Ogaz, Ernesto Gabriel Hernández Montoya, Zihao Wang, Pavi Bhatter, Marcos Ayestaran, Bing Liu, Yunzhong He

Efficient Shapley Values Estimation by Amortization for Text Classification (2023.acl-long)

Challenge:	Frontier models often lack a view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most.
Approach:	They introduce a professional reasoning benchmark that recruits 182 qualified professionals to contribute questions inspired by their workflows.
Outcome:	The proposed model outperforms other models in 114 countries and 47 US jurisdictions on hard subsets.

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Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang

Information Retrieval

Challenge:	Shapley Values are often estimated with a small number of stochastic model evaluations, but this can only be mitigated by aggregating thousands of model evaluation.
Approach:	They propose to combine a model with thousands of model evaluations to estimate Shapley Values without additional model evaluation.
Outcome:	The proposed model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods and does not suffer from stability issues as inference is deterministic.

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (2022.findings-acl)

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Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)

Challenge:	Document-level relation extraction (DocRE) is a more challenging task than sentence-level one.
Approach:	They propose a semi-supervised framework for document-level relation extraction with three components . they use an axial attention module for learning the interdependency among entity-pairs .
Outcome:	The proposed model outperforms baseline models on two DocRE datasets and outperformed previous models on human annotated data and distantly supervised data.

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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

Challenge:	Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization.
Approach:	They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach.
Outcome:	The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks.

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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu

Challenge:	Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
Approach:	They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
Outcome:	The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)

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Yuhang He, Jihai Zhang, Jianzhu Bao, Fangquan Lin, Cheng Yang, Bing Qin, Ruifeng Xu, Wotao Yin

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

Challenge:	Existing methods for interactive theorem proving in formal logic lack robustness and robustness.
Approach:	They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps.
Outcome:	The proposed framework improves on the miniF2F benchmark.

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

Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)

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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Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Ming Liu, Zerui Chen, Bing Qin

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance.
Approach:	They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics.
Outcome:	The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency.

When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents (2026.acl-long)

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Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui, Xuda Zhi, Yongbo Huang, Hao He, Weixiang Zhao, Yanyan Zhao, Bing Qin

Dialogue Systems & Conversational Agents

Challenge:	Existing research on personalized LLM agents focuses on the effectiveness of personalized responses.
Approach:	They propose a benchmark to quantify intent legitimation in personalized interactions . they propose 'detection-reflection' method that detects intent legititimation from internal representation space .
Outcome:	The proposed method reduces safety degradation by using internal representation space.

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He

Bootstrapped Unsupervised Sentence Representation Learning (2021.acl-long)

Challenge:	End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored.
Approach:	They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns.
Outcome:	The proposed model fails on the highest-performing model with 54.65% pass rate.

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Yan Zhang, Ruidan He, Zuozhu Liu, Lidong Bing, Haizhou Li

Representation Learning

Challenge:	Existing approaches to learn sentence representations rely on quality labeled data.
Approach:	They propose a Siamese Network which maximizes similarity between two augmented views of each sentence.
Outcome:	The proposed method outperforms state-of-the-art methods on STS and classification tasks.

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)

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Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao

MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)

Challenge:	Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application.
Approach:	They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model.
Outcome:	The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity.

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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora.
Approach:	They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks .
Outcome:	The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR).

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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

Information Extraction & Text Mining

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

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

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

Automatic Construction of Enterprise Knowledge Base (2021.emnlp-demo)

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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Junyi Chai, Yujie He, Homa Hashemi, Bing Li, Daraksha Parveen, Ranganath Kondapally, Wenjin Xu

Knowledge Representation

Challenge:	Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia.
Approach:	They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques.
Outcome:	The proposed system is currently serving as part of a Microsoft 365 service.

Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)

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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)

Challenge:	OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs.
Approach:	They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5.
Outcome:	The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL).

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Zerui Chen, Huiming Fan, Qianyu Wang, Tao He, Ming Liu, Heng Chang, Weijiang Yu, Ze Li, Bing Qin

An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)

Challenge:	Entity alignment (EA) is critical for knowledge graph (KG) integration.
Approach:	They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment.
Outcome:	The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment.

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Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing

Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching (2021.emnlp-main)

Challenge:	Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming.
Approach:	They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner.
Outcome:	The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks.

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Wenxuan Zhang, Ruidan He, Haiyun Peng, Lidong Bing, Wai Lam

Multilinguality

Challenge:	Existing studies on Aspect-based sentiment analysis (ABSA) focus on English texts, but handling it in resource-poor languages remains a challenge.
Approach:	They propose an unsupervised cross-lingual transfer method for the Aspect-based sentiment analysis task . they propose an aspect code-switching mechanism to augment training data with code-linked bilingual sentences .
Outcome:	The proposed method preserves task-specific knowledge in the target language.

Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models (2021.eacl-main)

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Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, Fuchun Peng

Dialogue Systems & Conversational Agents

Challenge:	a large-scale unsupervised pretraining has been shown to greatly boost the performance of natural language processing models.
Approach:	They propose an intuitive finetuning strategy to regularize the finetune process . they propose a mix-review strategy to alleviate the forgetting problem .
Outcome:	The proposed strategy regularizes the finetuning process, and the forgetting problem is alleviated . the proposed strategy also improves the performance of the resulting model .

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu

UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)

Challenge:	Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.
Approach:	They propose to summarize advanced methods through a taxonomy that offers novel perspectives.
Outcome:	The proposed method delineates the challenges and future directions, thereby shedding light on future research.

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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li

Variational Autoregressive Decoder for Neural Response Generation (D18-1)

Challenge:	Existing methods overlook the challenge of effectively transforming structure information from NL to SQL.
Approach:	They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL.
Outcome:	The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes.

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Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Text Generation

Challenge:	Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses.
Approach:	They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence.
Outcome:	Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets.

A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning (2026.findings-acl)

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Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin

Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training (2020.emnlp-main)

Challenge:	Large Language Models (LLMs) have made strong progress in reasoning.
Approach:	They propose a dual-phase test-time scaling framework that separates planning and execution and performs search over each phase independently.
Outcome:	Experiments on math reasoning and code generation benchmarks show that the proposed approach improves accuracy while reducing redundant computation.

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Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

Challenge:	Adapting pre-trained language models (PrLMs) to new domains has gained much attention . Adaptation of PrLMs to newdomains is important, but requires fine-tuning .
Approach:	They propose to use PrLMs to adapt to new domains without fine-tuning . they use class-aware feature self-distillation to learn discriminative features .
Outcome:	The proposed model can learn discriminative features from pre-trained language models without fine-tuning.

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Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si