Papers by Lingyong Yan
Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) suffer from inherent inabilities to interact with the physical world and access vast, up-to-date knowledge. |
| Approach: | They propose a tool retrieval benchmark for large language models (LLMs) that includes 7.6k diverse retrieval tasks and a corpus of 43k tools. |
| Outcome: | The proposed model performs poorly on the heterogeneous tool retrieval benchmark, resulting in low pass rate and low retrieval quality. |
Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking (2025.emnlp-main)
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| Challenge: | Large Reasoning Models (LLMs) have demonstrated impressive performances across diverse domains, but how their safety benefits from enhanced reasoning capabilities against jailbreak queries remains unexplored. |
| Approach: | They propose a safety-aware reasoning paradigm that integrates a pivot token-based safety-based reasoning mechanism into LLMs’ generation process. |
| Outcome: | The proposed model improves the safety of large language models against jailbreak queries while minimizing attacks and maintaining the original performance. |
DRBO: Mitigating Short Board Effect via Dynamic Reward Balancing in Multi-reward LLM Optimization (2025.findings-emnlp)
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| Challenge: | a new framework to optimize large language models (LLMs) for evaluation metrics is needed to balance weaker metrics. |
| Approach: | They propose a Dynamic Reward Balancing Optimization framework to mitigate the "short-board effect" they apply it to single-task and multi-type task scenarios . |
| Outcome: | The proposed framework improves performance and balances performance across multiple metrics. |
LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts (2026.eacl-long)
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| Challenge: | LM-LEXICON is a definition modeling approach that integrates data clustering, semantic expert learning, and model merging. |
| Approach: | They propose a definition modeling approach that integrates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. |
| Outcome: | The proposed model outperforms existing methods on five widely used benchmarks and achieves a BLEU score of 7%. |
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models . however, such approach can generate inconsistent answer with external references . |
| Approach: | They propose to integrate the verification module into the RAG to improve external retrieval correctness and internal generation consistency. |
| Outcome: | The proposed model can significantly surpass the state-of-the-art baselines using different LLM backbones. |
Learning to Use Tools via Cooperative and Interactive Agents (2024.findings-emnlp)
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Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Pengjie Ren, Suzan Verberne, Zhaochun Ren
| Challenge: | Existing methods for large language models (LLMs) use one agent to iterate and execute tools, but they suffer from performance degradation when addressing practical tasks. |
| Approach: | They propose a tool learning framework that coordinates three specialized agents for tool selection, tool execution, and action calibration separately. |
| Outcome: | The proposed framework outperforms baseline models on three datasets with 14% higher success rate. |
Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)
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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li
| Challenge: | Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process. |
| Approach: | They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor. |
| Outcome: | Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework. |
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)
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| Challenge: | Recent studies show that pre-trained masked language models can be factual knowledge bases. |
| Approach: | They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a . |
| Outcome: | The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases . |
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
| Challenge: | Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking. |
| Approach: | They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge. |
| Outcome: | The proposed model outperforms a 3B supervised model on the BEIR benchmark. |
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization (2025.naacl-long)
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| Challenge: | Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly. |
| Approach: | They propose a method for optimizing the RAG generator from multiple preference perspectives to align with RAG requirements comprehensively. |
| Outcome: | The proposed method improves the performance of RAG generators by incorporating retrieved documents into the prompt. |
Task Knowledge Injection via Interpolations and Reinstatement for Large Language Model Generalization (2025.findings-acl)
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| Challenge: | Pre-trained large language models have been widely adopted to elicit their superior performance on downstream tasks, but instruction tuning may overfit them to specific task formats, compromising their generalization on unseen tasks. |
| Approach: | They propose to inject latent task adaptation and knowledge reinstatement into large language models to mitigate spurious correlations between inputs and targets. |
| Outcome: | The proposed method improves generalization on in-domain and out-of-domain unseen tasks. |
Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal. |
| Approach: | They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations. |
| Outcome: | The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses. |
Element Intervention for Open Relation Extraction (2021.acl-long)
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| Challenge: | Current OpenRE models are often trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. |
| Approach: | They propose to use a causal model to identify relation instances referring to the same relation . they propose to perform Element Interventions on context and entities respectively . |
| Outcome: | The proposed method outperforms existing methods and is robust across datasets. |
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization. |
| Approach: | They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question. |
| Outcome: | The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |
DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators. |
| Approach: | They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality. |
| Outcome: | The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation. |
Learning to Bootstrap for Entity Set Expansion (D19-1)
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| Challenge: | Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision. |
| Approach: | They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals. |
| Outcome: | The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. |
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) due to noisy and fabricating content, it is inevitable that RAG systems are vulnerable to these noises and prone to respond incorrectly. |
| Approach: | They propose to optimize retrieval-augmented generation (RGG) with an Adversarial Tuning Multi-agent system (ATM) ATM steers the Generator to have a robust perspective of useful documents for question answering with the help of an auxiliary Attacker agent. |
| Outcome: | The proposed system improves the retrieval-augmented generator with an auxiliary Attacker agent and can discriminate useful documents amongst fabrications. |
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models (2024.emnlp-main)
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Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten Rijke, Zhaochun Ren
| Challenge: | Large language models (LLMs) are a default solution for many natural language processing tasks. |
| Approach: | They propose a knowledge-aware fine-tuning method to improve LLMs' knowledge awareness . they propose augmentation and comparison stages to improve accuracy and reliability . |
| Outcome: | The proposed method generates more facts with less factual error rate under fine-grained facts evaluation. |
Global Bootstrapping Neural Network for Entity Set Expansion (2020.findings-emnlp)
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| Challenge: | Recent studies have shown that end-to-end bootstrapping methods only leverage local semantics rather than global semantics. |
| Approach: | They propose a global-sighted encoder to capture and encode local and global semantics into entity embedding and an attention-guided decoder to sequentially expand new entities based on these embeddables. |
| Outcome: | The proposed network achieves state-of-the-art on two bootstrapping datasets. |
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021.emnlp-main)
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| Challenge: | Existing methods for entity set expansion define the expansion boundary using seed-based distance metrics, which are hard to adjust due to the extremely sparse supervision. |
| Approach: | They propose a new learning method for bootstrapping which jointly models the bootstraping process and boundary learning process in a GAN framework. |
| Outcome: | The proposed method achieves the new state-of-the-art performance for entity set expansion. |
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval (2024.emnlp-main)
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Weiwei Sun, Zhengliang Shi, Wu Long, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren
| Challenge: | Existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. |
| Approach: | They propose a multi-task instruction-tuned IR benchmark that includes 126 distinct IR tasks across 6 domains. |
| Outcome: | The proposed model performs better on instruction-tuned models than non-instruction-tunned models on MAIR. |
Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) demonstrate remarkable capabilities but their ability to autonomously execute complex real-world tasks remains limited. |
| Approach: | They propose a parallel tool invocation framework that decomposes tasks into parallel tool-using subtasks while aggregating results for subsequent decisions. |
| Outcome: | The proposed method significantly improves task performance while reducing token consumption and inference time. |