Papers by Yichen Tang
DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models (2024.acl-long)
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| Challenge: | Existing dynamic RAG methods fail to address the information needs of large language models (LLMs) despite their impressive capabilities, these models often produce text that seems coherent and plausible but factually incorrect, a problem commonly known as hallucination. |
| Approach: | They propose a dynamic retrieval augmented generation paradigm that actively decides when and what to retrieve during the text generation process of Large Language Models. |
| Outcome: | The proposed framework achieves superior performance over 4 knowledge-intensive generation datasets. |
Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)
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| Challenge: | Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks. |
| Approach: | They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another. |
| Outcome: | The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another. |
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)
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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin
| Challenge: | Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. |
| Approach: | They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations. |
| Outcome: | The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images. |
Knowledge Editing through Chain-of-Thought (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods focus on multi-hop QA tasks and require frequent retraining. |
| Approach: | They propose a new knowledge editing framework that updates large language models with new information to maintain their world knowledge without retraining. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods. |