Summary of Simple and Provable Scaling Laws For the Test-time Compute Of Large Language Models, by Yanxi Chen et al.
Simple and Provable Scaling Laws for the Test-Time Compute of Large Language Models
by Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed algorithms enjoy provable scaling laws for the test-time compute of large language models (LLMs). The first algorithm is a two-stage knockout-style approach that generates multiple candidate solutions, aggregates them, and eliminates candidates based on pairwise comparisons. The second algorithm is a two-stage league-style approach that evaluates each candidate solution by its average win rate against multiple opponents. Both algorithms are theoretically proven to have exponentially decaying failure probabilities as test-time compute grows. Extensive experiments with GPQA and MMLU-Pro benchmarks validate the theories, demonstrating outstanding scaling properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) need simple and efficient ways to work accurately. Two new methods can do this: a knockout-style algorithm and a league-style algorithm. Both algorithms generate many possible answers and then pick the best one based on how good it is compared to others. The first method gets rid of bad answers by comparing them, while the second method looks at how well each answer does against other answers. These methods are proven to work better as they use more computer power. They were tested with two difficult tasks and showed great results. |
Keywords
» Artificial intelligence » Scaling laws
