Summary of Inference Scaling Laws: An Empirical Analysis Of Compute-optimal Inference For Problem-solving with Language Models, by Yangzhen Wu et al.
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
by Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper investigates the optimal inference configurations for large language models (LLMs) during test time, exploring trade-offs between model sizes and generating additional tokens using different inference strategies. The study computes-optimal inference methods, examining cost-performance trade-offs for various strategies such as greedy search, majority voting, weighted voting, and two tree search algorithms. The results show that scaling inference compute with these strategies can be more computationally efficient than scaling model parameters, while smaller models combined with advanced inference algorithms offer Pareto-optimal trade-offs in cost and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting bigger and better, but what happens when we use them for things like answering questions or generating text? This paper tries to figure out the best way to make these models work well during this “test time” part. They looked at how different ways of doing inference (like using a simple rule or asking multiple models) affect how good the answers are and how much computer power it takes. The results show that sometimes it’s better to use more computing power for the task, rather than just making the model bigger. |
Keywords
» Artificial intelligence » Inference
