Papers by Libo Qin

Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)

Challenge:	Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance.
Approach:	They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations.
Outcome:	The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets.

Copied to clipboard

Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals (2025.acl-long)

Challenge:	Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process.
Approach:	They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks.
Outcome:	The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT.

Copied to clipboard

Yangyang Zhao, Ben Niu, Libo Qin, Shihan Wang

GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)

Challenge:	Evolutionary Algorithms (EAs) have been proven to effectively explore the solution space of neural networks by maintaining population diversity.
Approach:	They propose an elite individual injection mechanism to enhance EA’s search efficiency by adaptively introducing best-performing individuals into the population.
Outcome:	Experiments on four datasets show that the proposed approach significantly improves the balance between exploration and exploitation, boosting performance.

Copied to clipboard

Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Text Generation

Challenge:	Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem.
Approach:	They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task.
Outcome:	The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis.

Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network (2020.acl-main)

Copied to clipboard

Yangming Li, Kaisheng Yao, Libo Qin, Wanxiang Che, Xiaolong Li, Ting Liu

Natural Language Interfaces

Challenge:	Existing approaches to natural language generation are prone to errors, such as neglecting input slot values and generating redundant slot values.
Approach:	They propose an iterative rectification network to improve general NLG systems . they apply bootstrapping algorithms to sample training candidates and incorporate reward .
Outcome:	The proposed methods significantly reduce the slot error rate for strong baselines.

Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)

Copied to clipboard

Wenzhen Zheng, Wenbo Pan, Xu Xu, Libo Qin, Li Yue, Ming Zhou

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)

Challenge:	Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive.
Approach:	They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them.
Outcome:	The proposed approach saves significant resources and accelerates convergence and performance.

Copied to clipboard

Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu

CSTree-SRI: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts (2025.acl-long)

Challenge:	Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other.
Approach:	They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history .
Outcome:	The proposed framework outperforms baseline models and produces entity-consistent responses.

Copied to clipboard

Zhaowen Wang, Xiang Wei, Kangshao Du, Yiting Zhang, Libo Qin, Yingjie Xia, Li Kuang

Question Answering

Challenge:	Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text.
Approach:	They propose a framework that captures logical correlations across chunks of ELC and maintains coherence of multi-turn Questions.
Outcome:	The proposed framework is able to capture logical correlations across chunks of ELC and maintain coherence of multi-turn Questions.

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (D19-1)

Copied to clipboard

Libo Qin, Wanxiang Che, Yangming Li, Haoyang Wen, Ting Liu

Sentiment Analysis

Challenge:	Intent detection and slot filling are two main tasks for building a spoken language understanding system.
Approach:	They propose a framework to incorporate intent information into slot filling tasks . they use a joint model with Stack-Propagation to capture intent semantic knowledge .
Outcome:	The proposed model outperforms existing models on two publicly available datasets and outperformed existing models by a large margin.

Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration (2025.findings-emnlp)

Copied to clipboard

Yangyang Zhao, Ben Niu, Yuxuan Tan, Shihan Wang, Libo Qin

A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification (2024.lrec-main)

Challenge:	Pre-trained large language models (LLMs) with world knowledge and semantic understanding are promising for task-oriented dialogue systems.
Approach:	a framework that synergizes pre-trained large language models with DRL is proposed . a lightweight action pruning mechanism is employed to eliminate implausible actions .
Outcome:	a new framework synergizes pre-trained large language models with DRL to guide decision-making . the proposed framework eliminates semantically implausible or low-potential actions from multi-turn dialogue context .

Copied to clipboard

Yunlong Feng, Bohan Li, Libo Qin, Xiao Xu, Wanxiang Che

M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought (2024.acl-long)

Challenge:	Existing work on cross-domain text classification relies on domain-invariant features or task-agnostic features.
Approach:	They propose a two-stage framework for cross-domain text classification that leverages or reuses rich labeled data from the source domain and unlabeled data in the target domain.
Outcome:	The proposed framework achieves state-of-the-art on a public cross-domain text classification benchmark.

Copied to clipboard

Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, Wanxiang Che

Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (2024.acl-long)

Challenge:	MCoT requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning.
Approach:	They propose a benchmark to address the challenges of MCoT, and evaluate it using vision large language models.
Outcome:	The proposed benchmark addresses the above challenges and shows that current models still struggle to reason in M3CoT.

Copied to clipboard

Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)

Challenge:	Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations.
Approach:	They propose an iterative expansion framework to prompt large teacher model to curate an expansive emotion support dialogue dataset.
Outcome:	The proposed model outperforms the teacher model in some cases . the proposed model is based on an iterative expansion framework and is available on github.com/pandazzh2020/ExTES.

Copied to clipboard

Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin

Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)

Challenge:	Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools.
Approach:	They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools.
Outcome:	The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili.

Copied to clipboard

Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (2023.findings-acl)

Challenge:	Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base.
Approach:	They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models.
Outcome:	The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset.

Copied to clipboard

Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang

Robustness in NLP

Challenge:	Pre-trained language models (PLMs) have improved generalization performance but the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks.
Approach:	They propose to create a benchmark for evaluating out-of-distribution (OOD) generalization in NLP models.
Outcome:	The proposed benchmarks highlight the importance of OOD robustness and provide insights on how to measure it and improve it.

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Copied to clipboard

Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

Copied to clipboard

Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng, Wanxiang Che

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Challenge:	Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU .
Approach:	They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding.
Outcome:	The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios.

Copied to clipboard

Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che

MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)

Challenge:	Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process .
Approach:	They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation.
Outcome:	The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench.

Copied to clipboard

JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin

Programming Languages in NLP

Challenge:	MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code.
Approach:	They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries.
Outcome:	The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues.

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction (2025.acl-industry)

Copied to clipboard

Yinghui Li, Shang Qin, Jingheng Ye, Haojing Huang, Yangning Li, Shu-Yu Guo, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020.acl-main)

Challenge:	Recent studies have shown that Large Language Models’ performance as correctors on Chinese Grammatical Error Correction (CGEC) remains unsatisfactory due to the challenging nature of the task.
Approach:	They propose a training framework EXAM that uses LLMs as explainers to enhance CGEC small models and a novel evaluation method SEE that utilizes LLM as evaluators to bring more reasonable evaluations.
Outcome:	The proposed methods improve the performance of LLMs on Chinese Grammatical Error Correction (CGEC) task.

Copied to clipboard

Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)

Challenge:	Recent studies show remarkable success in end-to-end task-oriented dialog systems . however, most models rely on large training data, which is difficult to scalable for new domains with limited labeled data.
Approach:	They propose a shared-private network which exploits the relevance between the target domain and each domain.
Outcome:	The proposed model outperforms existing methods on multi-domain dialogue by 13.9% on average.

Copied to clipboard

Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che

CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)

Challenge:	Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images.
Approach:	They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images.
Outcome:	The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks.

Copied to clipboard

Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu

N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)

Challenge:	Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm .
Approach:	They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks.
Outcome:	The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement)

Copied to clipboard

Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)

Challenge:	Existing tools that teach an independent model for each task are not supported in Chinese.
Approach:	They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp .
Outcome:	The proposed platform supports six Chinese NLP tasks.

Copied to clipboard

Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models.
Approach:	They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification.
Outcome:	The proposed framework surpasses all baselines on 8 datasets and 5 LLMs.

Copied to clipboard

Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

Copied to clipboard

Yunlong Feng, Dechuan Teng, Yang Xu, Honglin Mu, Xiao Xu, Libo Qin, Qingfu Zhu, Wanxiang Che

What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices (2025.acl-long)

Challenge:	Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable.
Approach:	They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements.
Outcome:	The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%.

Copied to clipboard

Zhi Chen, Qiguang Chen, Libo Qin, Qipeng Guo, Haijun Lv, Yicheng Zou, Hang Yan, Kai Chen, Dahua Lin

Challenge:	Existing methods to generate long-context instruction-tuning data are limited by poor quality and fewer than 35% of samples are multi-hop .
Approach:	They propose a framework that integrates a quality verification agent, a single-hop question generation agent, and a multi-hop questions merger agent to enhance model performance.
Outcome:	The proposed framework significantly improves data quality with high-quality, multi-hop, and diverse data.

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

Copied to clipboard

Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

Copied to clipboard

Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang, Wanxiang Che

Low-Resource NLP

Challenge:	Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development.
Approach:	They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages.
Outcome:	The proposed method outperforms existing prompting methods on several benchmarks.

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Copied to clipboard

Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

Copied to clipboard

Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu

Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)

Challenge:	Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection .
Approach:	They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples.
Outcome:	The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection.

Copied to clipboard

Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

Challenge:	Large-scale high-quality training data is important for improving the performance of models.
Approach:	They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning.
Outcome:	The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets.

Copied to clipboard

Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue (2026.findings-acl)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

Copied to clipboard

Yangyang Zhao, Linfan Dai, Li Cai, Bowen Xing, Libo Qin

CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)

Challenge:	Existing methods to solve cross-domain task-oriented dialogues are brittle when cross- domain constraints are not directly grounded in surface text or require commonsense inference.
Approach:	They propose a framework that makes LLM-derived constraint reasoning usable for RL.
Outcome:	Experiments show that the proposed framework outperforms single-model baselines on long-horizon tasks.

Copied to clipboard

Libo Qin, Weiyun Wang, Qiguang Chen, Wanxiang Che

Information Extraction & Text Mining

Challenge:	Experimental results show that CLIP can be applied to zero-shot text classification tasks.
Approach:	They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP.
Outcome:	The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks.

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)

Copied to clipboard

Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling (2021.acl-long)

Challenge:	Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools.
Approach:	They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators .
Outcome:	The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance.

Copied to clipboard

Libo Qin, Fuxuan Wei, Tianbao Xie, Xiao Xu, Wanxiang Che, Ting Liu

Sentiment Analysis

Challenge:	Existing joint models for multi-intent SLU only consider intent detection while ignoring slot filling task.
Approach:	They propose a non-autoregressive model for joint multiple intent detection and slot filling . their framework is 11.5 times faster than existing joint models .
Outcome:	The proposed model is 11.5 times faster than existing models and is faster than current models.

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)

Copied to clipboard

Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, Libo Qin

Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (2024.findings-emnlp)

Challenge:	Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal.
Approach:	They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities .
Outcome:	The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs.

Copied to clipboard

Guobiao Zhang, Xueping Peng, Tao Shen, Guodong Long, Jiasheng Si, Libo Qin, Wenpeng Lu

Challenge:	Existing methods focus on local optimal while ignoring sole-mention disambiguation boosted by richer context from other mentions’ disambiguating processes.
Approach:	They propose an approach to extracting medical entity disambiguation using memory mechanism and memorized entity information (M3E) they use a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual to aid the disambiguations of remaining mentions.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System (2025.findings-acl)

Copied to clipboard

Peng Wang, Ruihan Tao, Qiguang Chen, Mengkang Hu, Libo Qin

Multilinguality

Challenge:	X-WebAgentBench evaluates the planning and interaction performance of language agents across multiple languages.
Approach:	They propose a multilingual agent benchmark that evaluates the interaction performance of language agents across multiple languages.
Outcome:	The proposed benchmark evaluates the planning and interaction performance of language agents across multiple languages.

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)

Copied to clipboard

Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong

AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024.findings-acl)

Challenge:	Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes.
Approach:	They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer.
Outcome:	The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks.

Copied to clipboard

Yongheng Zhang, Qiguang Chen, Min Li, Wanxiang Che, Libo Qin

Low-Resource NLP

Challenge:	Existing approaches to cross-lingual chain-of-thought integrate reasoning knowledge from different languages, but they still rely on manual language specification and weight allocation.
Approach:	They propose an automatic cross-lingual alignment planning framework that integrates reasoning knowledge from different languages.
Outcome:	The proposed framework surpasses existing methods that require manual effort to integrate languages.

LESA: Learnable LLM Layer Scaling-Up (2025.acl-long)

Copied to clipboard

Yifei Yang, Zouying Cao, Xinbei Ma, Yao Yao, Zhi Chen, Libo Qin, Hai Zhao

Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)

Challenge:	Existing methods for depth scaling-up rely on empirical heuristic rules for layer duplication, resulting in poor initialization and slower convergence during continual pre-training.
Approach:	They propose a method for learning latent parameters between layers by concatenating parameters from each layer and applying Singular Value Decomposition.
Outcome:	Experiments show that LESA outperforms baseline models with less than half the cost of existing methods.

Copied to clipboard

Libo Qin, Tianbao Xie, Shijue Huang, Qiguang Chen, Xiao Xu, Wanxiang Che

Natural Language Interfaces

Challenge:	Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue.
Approach:	They propose a dataset for Consistency Identification in task-oriented dialog system.
Outcome:	The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it.

CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)

Copied to clipboard

Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin

Multilinguality

Challenge:	Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions.
Approach:	They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level.
Outcome:	The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods.

GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)

Copied to clipboard

Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan

AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling (2020.findings-emnlp)

Challenge:	Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages.
Approach:	They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance .
Outcome:	Experiments on MultiATIS++ show that GL-CLeF achieves the best performance . GL is based on dictionaries and encourages representations to be more similar than negative example pairs .

Copied to clipboard

Libo Qin, Xiao Xu, Wanxiang Che, Ting Liu

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)

Challenge:	Existing models focus on the single intent scenario, ignoring the fine-grained multiple intents information integration for token-level slot prediction.
Approach:	They propose an Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling . they propose an intent-slot graph interaction layer to model the strong correlation between the slot and intents .
Outcome:	The proposed framework improves on three multi-intent datasets and new state-of-the-art performance on single-intention datasets.

Copied to clipboard

Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong

Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)

Challenge:	Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding.
Approach:	They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality.
Outcome:	The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet.

Copied to clipboard

Jie Ou, Jinyu Guo, Shuaihong Jiang, Zhaokun Wang, Libo Qin, Shunyu Yao, Wenhong Tian