Papers by Yongqi Leng
Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons (2025.coling-main)
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| Challenge: | Despite their superior multitask capabilities, the multitask learning mechanisms of large language models remain as an open question. |
| Approach: | They propose a method that fine-tunes current task-specific neurons during continuous learning by using gradient attribution on task-specified data. |
| Outcome: | The proposed method is highly correlated with the given task and solves two common problems in multi-task learning and continuous learning: Generalization and Catastrophic Forgetting. |
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models (2024.findings-acl)
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Linhao Yu, Yongqi Leng, Yufei Huang, Shang Wu, Haixin Liu, Xinmeng Ji, Jiahui Zhao, Jinwang Song, Tingting Cui, Xiaoqing Cheng, Liutao Liutao, Deyi Xiong
| Challenge: | Recent years have witnessed remarkable progress achieved by large language models in both natural language understanding and generation. |
| Approach: | They propose a large benchmark CMoralEval for moral evaluation of Chinese LLMs . they use a Chinese TV program discussing Chinese moral norms and Chinese moral anomies based on various sources . |
| Outcome: | The proposed dataset is characterized by diversity and authenticity. |
Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on item-level behavioral metrics without capturing how models prioritize competing values as a whole. |
| Approach: | They propose a symmetric human-LLM evaluation framework to measure value-structure alignment . they evaluate 12 LLMs across four model families via 240 replicated Q-sorts . |
| Outcome: | The proposed framework measures value-structure alignment across four model families. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria (2025.acl-long)
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Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong
| Challenge: | Existing evaluation methods are inadequate to evaluate large language models (LLMs). |
| Approach: | They propose a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria that can be used to evaluate large language models. |
| Outcome: | The proposed model outperforms existing LLM evaluators and instruction-tuned LLMs on multiple benchmarks and sets new SOTA results. |
Self-Pluralising Culture Alignment for Large Language Models (2025.naacl-long)
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| Challenge: | Existing approaches to align large language models don't take cultural diversity into account. |
| Approach: | They propose a framework that generates questions on various culture topics and outputs to LLMs under both culture-aware and culture-unaware settings. |
| Outcome: | The proposed framework improves the alignment of large language models to diverse cultures without compromising general abilities. |
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision (2025.emnlp-industry)
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Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Recent advances in outcome-supervised reinforcement learning (RL) have shown strong performance, but this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. |
| Approach: | They propose a model that models RAG as a Markov Decision Process (MDP) and introduces an efficient pruning strategy to optimize data expansion. |
| Outcome: | The proposed model outperforms existing methods and achieves an average performance improvement of 6.2% across six datasets. |
Can Large Language Models Learn Translation Robustness from Noisy-Source In-context Demonstrations? (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have been used for machine translation, but their robustness remains a challenge, as they struggle to translate sentences in the presence of noise even when using similarity-based in-context learning methods. |
| Approach: | They propose a scheme for studying machine translation robustness on LLMs by using noisy-source demonstration examples. |
| Outcome: | The proposed model can learn robustness from noisy-source demonstration examples, thereby improving translation performance on noisy sentences. |