Papers by Zhanqiu Zhang
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)
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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored. |
| Approach: | They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations. |
| Outcome: | The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. |
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them. |
| Approach: | They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on benchmark datasets. |
Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities when provided with full information in a single turn, yet they exhibit substantial vulnerability in multi-turn interactions. |
| Approach: | They propose a generalizable training approach to stabilize multi-turn interactions by leveraging the model's intrinsic single-turn capabilities as stable internal anchors. |
| Outcome: | The proposed approach outperforms fine-tuning and abstention-based methods and exhibits strong cross-domain generalization. |
Beyond Dialogue: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model (2025.acl-long)
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| Challenge: | Existing role-playing training methods often lack profile-dialogue alignment at the sentence level. |
| Approach: | They propose a framework that aligns dialogue with profile traits for each scenario, eliminating biases during training. |
| Outcome: | The proposed model outperforms most proprietary role-playing models and is fully automated and low-cost. |
Cultivating Gaming Sense for Yourself: Making VLMs Gaming Experts (2025.acl-long)
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| Challenge: | Recent efforts leverage Vision Language Models (VLMs) as direct controllers, often pausing the game to analyze screens and plan action through language reasoning. |
| Approach: | They propose a paradigm shift in gameplay agent design that uses Vision Language Models as a developer instead of direct control. |
| Outcome: | The proposed framework achieves fluent gameplay in diverse genres, including ACT, FPS, and Flappy Bird, setting a new benchmark for game-playing agents. |