Summary of Inference Scaling Flaws: the Limits Of Llm Resampling with Imperfect Verifiers, by Benedikt Stroebl et al.
Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers
by Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 challenges the idea that weaker language models can match stronger ones through inference scaling by repeatedly sampling solutions until they pass unit tests. The researchers find that there is no free lunch for this approach: even with an infinite compute budget, resampling cannot decrease the probability of false positives – incorrect solutions that pass the unit tests. They show a strong correlation between the model’s single-sample accuracy and its false positive rate on coding benchmarks HumanEval and MBPP. As a result, weaker models can’t match the accuracy of stronger ones, even with repeated sampling (Fig. 1a). The optimal number of samples is often less than 10 under realistic assumptions (Fig. 1b). Additionally, false positives may have other negative qualities, such as poor adherence to coding style conventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models can get better by trying many solutions until they pass certain tests. They find that there’s a limit to how good this approach can make weaker models, even if you use a lot of computer power. Weaker models will always be worse than stronger ones, no matter how many times you try. This is important because it affects not just how accurate the model is, but also other things like whether it follows coding rules correctly. |
Keywords
» Artificial intelligence » Inference » Probability
