Papers by Yichen Gao
WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents (2025.findings-naacl)
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| Challenge: | Existing methods focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only brief segments within longer documents. |
| Approach: | They propose a method to detect watermarked segments in large documents using an anomaly extraction method and a local traversal. |
| Outcome: | The proposed method achieves a superior balance between detection accuracy and computational efficiency. |
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)
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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King
| Challenge: | Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data. |
| Approach: | They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations. |
| Outcome: | The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability. |
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization (2021.naacl-main)
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Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao
| Challenge: | Abstractive summarization is the task of generating a concise summary of input documents . a middle-aged man and a young girl died after they were unable to avoid the plane . |
| Approach: | They propose a model that enriches the original Transformer with a Tensor Product Representation for abstractive summarization. |
| Outcome: | The proposed model outperforms the Transformer and the original TP-Transformer significantly on several datasets. |
Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing LLM reliability suffer from inefficient information aggregation and rigid reasoning schemes. |
| Approach: | They propose a method that explicitly models external knowledge integration capabilities by explicitly modeling knowledge relationships. |
| Outcome: | The proposed method outperforms existing methods in multiple graph reasoning tasks. |
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. |