Summary of Mind the Gap: Examining the Self-improvement Capabilities Of Large Language Models, by Yuda Song et al.
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
by Yuda Song, Hanlin Zhang, Carson Eisenach, Sham Kakade, Dean Foster, Udaya Ghai
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 explores the mechanism of Large Language Model (LLM) self-improvement during pre-training, post-training, and test-time inference. The authors investigate a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the filtered data. Despite empirical successes, there is still a lack of fundamental understanding. This work provides a comprehensive, modular, and controlled study on LLM self-improvement, introducing a mathematical formulation for self-improvement governed by the generation-verification gap. Experiments with various model families and tasks reveal a scaling phenomenon of self-improvement, where the generation-verification gap scales monotonically with pre-training flops. The authors also examine when self-improvement is possible, an iterative procedure, and ways to improve its performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have a special ability called self-improvement that helps them learn from their own mistakes. This paper tries to understand how this works by creating a framework where the model checks its own answers, removes or changes data based on these checks, and makes the best predictions. Even though they have had some success, there is still much we don’t know about LLM self-improvement. The authors do many experiments with different models and tasks to learn more. |
Keywords
» Artificial intelligence » Inference » Large language model
