Papers by Qiyao Peng
DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path (2025.findings-emnlp)
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| Challenge: | Existing knowledge graph methods lack adaptability in knowledge-intensive tasks with multiple entities and implicit multi-hop relations. |
| Approach: | They propose a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. |
| Outcome: | DS-MHP outperforms baselines and state-of-the-art methods on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning. |
Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)
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| Challenge: | Existing approaches to expert finding are effective for a community question answering platform. |
| Approach: | They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations. |
| Outcome: | The proposed framework could learn more comprehensive question representations on six real-world datasets. |
ExpertPLM: Pre-training Expert Representation for Expert Finding (2022.findings-emnlp)
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| Challenge: | Existing methods to learn expert representations based on historical answered questions are inadequate. |
| Approach: | They propose a pre-training and fine-tuning expert finding framework that integrates expert interest and expertise simultaneously. |
| Outcome: | The proposed framework integrates expert interest and expertise simultaneously and integrates historical answers with the vote score information to model the expert ability. |
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
| Approach: | They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs . |
| Outcome: | The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities . |
Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)
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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin
| Challenge: | Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages . |
| Approach: | They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs. |
| Outcome: | Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages. |