Summary of Token-wise Influential Training Data Retrieval For Large Language Models, by Huawei Lin et al.
Token-wise Influential Training Data Retrieval for Large Language Models
by Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a scalable framework called RapidIn for estimating the influence of each training data on Large Language Model (LLM) generations. The framework, comprising two stages: caching and retrieval, allows for efficient estimation of influence within minutes, achieving a 6,326x speedup compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RapidIn is designed to identify which training data led to a specific LLM generation by compressing gradient vectors by over 200,000x, allowing them to be cached on disk or in memory. The framework also supports multi-GPU parallelization for faster caching and retrieval. This makes RapidIn an effective tool for analyzing the impact of different training data on LLM generations. |
Keywords
» Artificial intelligence » Large language model
