Prompt Exploration with Prompt Regression
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910577475977216 |
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| author | Feffer, Michael Xu, Ronald Sun, Yuekai Yurochkin, Mikhail |
| author_facet | Feffer, Michael Xu, Ronald Sun, Yuekai Yurochkin, Mikhail |
| contents | In the advent of democratized usage of large language models (LLMs), there is a growing desire to systematize LLM prompt creation and selection processes beyond iterative trial-and-error. Prior works majorly focus on searching the space of prompts without accounting for relations between prompt variations. Here we propose a framework, Prompt Exploration with Prompt Regression (PEPR), to predict the effect of prompt combinations given results for individual prompt elements as well as a simple method to select an effective prompt for a given use-case. We evaluate our approach with open-source LLMs of different sizes on several different tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11083 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt Exploration with Prompt Regression Feffer, Michael Xu, Ronald Sun, Yuekai Yurochkin, Mikhail Computation and Language Machine Learning In the advent of democratized usage of large language models (LLMs), there is a growing desire to systematize LLM prompt creation and selection processes beyond iterative trial-and-error. Prior works majorly focus on searching the space of prompts without accounting for relations between prompt variations. Here we propose a framework, Prompt Exploration with Prompt Regression (PEPR), to predict the effect of prompt combinations given results for individual prompt elements as well as a simple method to select an effective prompt for a given use-case. We evaluate our approach with open-source LLMs of different sizes on several different tasks. |
| title | Prompt Exploration with Prompt Regression |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.11083 |