Summary of Efficient Multi-prompt Evaluation Of Llms, by Felipe Maia Polo et al.
Efficient multi-prompt evaluation of LLMs
by Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 introduces PromptEval, a method for estimating the performance distribution of Large Language Models (LLMs) across various prompts. The current benchmarks often rely on limited prompt templates, which can lead to biased results and affect reproducibility. By borrowing strength across prompts and examples, PromptEval produces accurate estimates under practical evaluation budgets. The resulting distribution enables the construction of robust performance metrics, such as top 95% quantile or median. The authors demonstrate PromptEval’s efficacy on three prominent LLM benchmarks: MMLU, BIG-bench Hard, and LMentry. For instance, PromptEval can accurately estimate performance quantiles across 100 prompt templates on MMLU with a budget equivalent to two single-prompt evaluations. This method has implications for LLM-as-a-judge and best prompt identification applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it fairer to compare different language models. Right now, we use limited prompts to test these models, which can give biased results. The authors of this paper have come up with a new way to estimate how well a model will do across many different prompts. This helps us get a more accurate picture of how good the model is. They tested their method on three different language model benchmarks and found that it works well. This has important implications for using language models as judges or finding the best prompt for a task. |
Keywords
» Artificial intelligence » Language model » Prompt
