Influential Data Retrieval Explained 

In this session, we welcome Huawei Lin from The Rochester Institute of Technology, who co-authored the paper "Token-wise Influential Training Data Retrieval for Large Language Models".
About the paper:
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​The authors propose RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval.
🔬 Token-wise Influential Training Data Retrieval for Large Language Models: arxiv.org/pdf/2405.11724
📝 Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao
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