Papers by Bo Han

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds .
Approach:	They propose a framework that only utilizes entity embeddings to decode knowledge graphs.
Outcome:	The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

Generative Text Modeling through Short Run Inference (2021.eacl-main)

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Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu

Challenge:	Latent variable models for text capture global semantic and syntactic features when trained correctly.
Approach:	They propose a short run dynamics for inference that initializes from the prior distribution of the latent variable and runs a small number of Langevin dynamics steps guided by its posterior distribution.
Outcome:	The proposed model is able to generate coherent sentences with smooth transition and shows no sign of posterior collapse.

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs (2026.acl-long)

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Bo Zhang, Jiawei Zhang, Cong Gao, Bingxu Han, Minghao Hu, Jun Zhang, Yunbo Cao, Zhunchen Luo, Wen Yao, Guotong Geng, Zhong Wang

Reasoning

Challenge:	Existing methods for calibration of large reasoning models (LRMs) focus on clean inputs, leaving noise unexplored.
Approach:	They propose a confidence calibration framework for character-level noisy inputs that extracts uncertainty signals from both the empirical answer distribution and the model’s predictive distribution and integrates them via a learned calibrator.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that DisCal outperforms existing calibration methods under noisy inputs, reducing expected calibration error (ECE) by up to 39.21% and improving Area Under the Receiver Operating Characteristic Curve (AUROC) by 31.44%.

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)

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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)

Challenge:	Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Approach:	They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding.
Outcome:	The proposed method can solve the semantic gap and structure gap on multiple datasets.

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Shan Wu, Chunlei Xin, Bo Chen, Xianpei Han, Le Sun

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)

Challenge:	Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses.
Approach:	They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration.
Outcome:	The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods.

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Bo Chen, Le Sun, Xianpei Han

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains.
Approach:	They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process.
Outcome:	The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

Linguistics & Cognitive NLP

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

CaMML: Context-Aware Multimodal Learner for Large Models (2024.acl-long)

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Yixin Chen, Shuai Zhang, Boran Han, Tong He, Bo Li

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

Challenge:	a lightweight module for tuning large multimodal models is introduced . CaMML integrates contextual samples into large models, enabling them to make inferences .
Approach:	They introduce a lightweight module for tuning large multimodal models . they have developed two models that have shown exceptional performance .
Outcome:	The proposed model outperforms LLaVA-1.5 on ten widely recognized datasets with a noticeable margin.

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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

Information Retrieval

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference (2024.emnlp-main)

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Jianxing Yu, Shiqi Wang, Han Yin, Zhenlong Sun, Ruobing Xie, Bo Zhang, Yanghui Rao

Representation Learning

Challenge:	a new method to detect clickbait posts on the Web is needed to detect such posts.
Approach:	They propose a method to detect clickbait posts on the Web using latent factors . they use features in multiple modalities to characterize the posts and causal inference to eliminate noise .
Outcome:	The proposed method can detect clickbait posts on popular social media platforms with good generalization ability.

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

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

Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing (C18-1)

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

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Bo Chen, Bo An, Le Sun, Xianpei Han

Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)

Challenge:	Semantic parsers rely on accurate and high-coverage lexicons, but they often use annotated logical forms to learn the lexic.
Approach:	They propose a semi-supervised learning framework that makes use of large text corpora and lexical resources.
Outcome:	The proposed framework improves on two benchmarks: Webquestions and Free917.

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Shengkun Ma, Jiale Han, Yi Liang, Bo Cheng

Low-Resource NLP

Challenge:	Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting.
Approach:	They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones .
Outcome:	The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios.

Model-Free Context-Aware Word Composition (C18-1)

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Bo An, Xianpei Han, Le Sun

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)

Challenge:	Word composition models do not take ambiguity of words and context into consideration for learning representations, and thus suffer from the inaccurate representation of semantics.
Approach:	They propose a model-free context-aware word composition model which takes the latent semantic information as global context for learning representations.
Outcome:	The proposed model improves on existing composition models at different granularities and shows that it can be used to learn semantics.

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Haowei Fu, Bo Ni, Han Xu, Kunpeng Liu, Dan Lin, Tyler Derr

VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)

Challenge:	Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains.
Approach:	They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks.
Outcome:	The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality.

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Zhixuan Wu, Bo Cheng, Jiale Han, Jiabao Ma, Shuhao Zhang, Yuli Chen, Changbo Li

Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL (2026.findings-acl)

Challenge:	Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts.
Approach:	They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels.
Outcome:	The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels.

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Yang Zerui, Weichuan Wang, Yanwei Xu, Linqi Song, Yudai Matsuda, Wei Han, Bo Bai

Programming Languages in NLP

Challenge:	Existing NL2SQL systems rely on in-context learning with only correct examples . current test-time scaling methods often decompose questions arbitrarily, resulting in poor performance .
Approach:	They propose a structured decomposition and experience-aware self-correction framework for NL2SQL . they build a dynamic memory of successful queries and historical error–fix pairs .
Outcome:	The proposed framework achieves 68.5% execution accuracy on BIRD, setting new state of the art among open, zero-fine-tuning methods.

AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark (2025.acl-long)

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Jianlyu Chen, Nan Wang, Chaofan Li, Bo Wang, Shitao Xiao, Han Xiao, Hao Liao, Defu Lian, Zheng Liu

Information Retrieval

Challenge:	Evaluation benchmarks based on predefined domains and human-labeled data face limitations in addressing evaluation needs for emerging domains.
Approach:	They propose an automated information retrieval benchmark based on predefined domains and human-labeled data . AIR-Bench is automated and Heterogeneous with three key features .
Outcome:	The proposed benchmarks are based on predefined domains and human-labeled data.

EUSP: An Easy-to-Use Semantic Parsing PlatForm (D19-3)

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Bo An, Chen Bo, Xianpei Han, Le Sun

Select Before Use: On the Importance of Reference Model Selection in Preference Alignment (2026.acl-long)

Challenge:	Semantic parsing aims to map natural language utterances into structured meaning representations.
Approach:	They propose a modular platform that allows developers to build semantic parser from scratch.
Outcome:	The proposed platform achieves competitive performance on semantic parsing task and improves performance of a business search engine.

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Muyang Li, Runze Wu, Xiangyu Zhao, Bo Han, Daoyi Dong, Tongliang Liu

Challenge:	Supervised Fine-Tuning (SFT) is used as the initialization and reference model for subsequent preference alignment.
Approach:	They propose to use RewardRank to estimate initial implicit alignment between reference model and preference objective to ensure LLMs generate safe, helpful, and instruction-aligned content.
Outcome:	Empirical evidence shows that using the selected model as reference can gain up to 67.6% relative increase on length-controlled win rate compared to baselines.

Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)

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Wenjuan Han, Bo Pang, Ying Nian Wu

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial.
Approach:	They propose a simple yet effective adapter-based approach to fine-tune language models on downstream tasks.
Outcome:	The proposed approach improves stability and adversarial robustness in transfer learning to various downstream tasks.

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Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang

Language Models

Challenge:	Large language models (LLMs) have many advantages but they also pose significant safety risks.
Approach:	They propose a method to enhance the safety self-evaluation capability of LLMs . they perform semantic mutations on the original safety evaluation questions .
Outcome:	The proposed method improves safety self-evaluation accuracy by 5.86% and 7.79% over baseline methods on Chinese and English datasets.

Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph (2020.coling-main)

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Xu Wang, Shuai Zhao, Jiale Han, Bo Cheng, Hao Yang, Jianchang Ao, Zhenzi Li

Structured Data in NLP

Challenge:	Existing studies rely on deep graph neural networks (GNNs) to capture rich structural information, but they lack the structural information needed for QA.
Approach:	They propose a framework which captures structural information from KBs and models long-distance node relations from two perspectives.
Outcome:	The proposed framework models long-distance node relations from two perspectives . it is based on two widely used multi-hop KBQA datasets .

Extending Context Window of Large Language Models via Semantic Compression (2024.findings-acl)

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Weizhi Fei, Xueyan Niu, Pingyi Zhou, Lu Hou, Bo Bai, Lei Deng, Wei Han

Language Models

Challenge:	Existing models rely on a quadratic computation to generate long texts . current models impose limitations on the length of text inputs .
Approach:	They propose a semantic compression method that extends the context window of large language models . the method reduces the semantic redundancy of long inputs before passing them to the LLMs .
Outcome:	The proposed method extends the context window of large language models across tasks . it exhibits consistent fluency in text generation while reducing associated computational overhead.

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

Robustness in NLP

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

Accurate Text-Enhanced Knowledge Graph Representation Learning (N18-1)

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Bo An, Bo Chen, Xianpei Han, Le Sun

Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models (2026.findings-acl)

Challenge:	Existing representation learning methods for knowledge graph representation do not consider the ambiguity of relations and entities.
Approach:	They propose a text-enhanced knowledge graph representation learning method which exploits the entity descriptions and triple-specific relation mention to enhance representations.
Outcome:	The proposed method outperforms existing representation learning models on link prediction and triple classification tasks and significantly outperformed existing models.

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Shuhao Zhang, Yuli Chen, Jiale Han, Bo Cheng, Jiabao Ma

Robustness in NLP

Challenge:	Existing detection methods for large language models rely on fixed strategies to steal watermarks.
Approach:	They propose a novel steal-based watermark algorithm that derives watermark information from watermarked texts to craft highly targeted adversarial attacks.
Outcome:	The proposed system significantly increases steal efficiency against target watermarks under identical conditions.

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)

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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang

Explain-Analyze-Generate: A Sequential Multi-Agent Collaboration Method for Complex Reasoning (2025.coling-main)

Challenge:	Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application.
Approach:	They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application.
Outcome:	The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.

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WenYuan Gu, JiaLe Han, HaoWen Wang, Xiang Li, Bo Cheng

Exploring Task Difficulty for Few-Shot Relation Extraction (2021.emnlp-main)

Challenge:	Multiagent debate (MAD) is a popular approach for large language models . however, the performance of LLMs is suboptimal in complex reasoning scenarios .
Approach:	They propose a sequential collaboration framework to enable agents to provide constructive assistance to peers by decomposing complex tasks into essential subtasks.
Outcome:	The proposed framework achieves the highest performance on 19 out of 23 tasks and lower costs compared to MAD.

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Jiale Han, Bo Cheng, Wei Lu

CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models (2024.findings-naacl)

Challenge:	Existing models do not distinguish hard tasks from easy ones in the learning process.
Approach:	They propose a novel approach that exploits relation label information to learn better representations by focusing on hard tasks.
Outcome:	Experiments on two standard datasets show the proposed approach performs better than previous methods.

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Xiang Li, FanBu FanBu, Ambuj Mehrish, Yingting Li, Jiale Han, Bo Cheng, Soujanya Poria