Summary of The Effect Of Sampling Temperature on Problem Solving in Large Language Models, by Matthew Renze and Erhan Guven
The Effect of Sampling Temperature on Problem Solving in Large Language Models
by Matthew Renze, Erhan Guven
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 study empirically investigates the impact of sampling temperature on Large Language Models (LLMs) performance on various problem-solving tasks. The researchers created a multiple-choice question-and-answer (MCQA) exam by randomly sampling problems from standard LLM benchmarks, and then used nine popular LLMs with five prompt-engineering techniques to solve the MCQA problems while increasing the sampling temperature from 0.0 to 1.6. The findings indicate that changes in temperature from 0.0 to 1.0 do not have a statistically significant impact on LLM performance for problem-solving tasks, and these results appear to generalize across LLMs, prompt-engineering techniques, and problem domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how changing the “temperature” of language models affects their ability to solve problems. They tested different language models and saw that increasing the temperature didn’t make a big difference in how well they did on tasks like answering questions. The researchers used nine different language models and tried five ways to help them understand what was being asked, but the results were the same: changing the temperature didn’t matter. |
Keywords
» Artificial intelligence » Prompt » Temperature
