Summary of Automatic Prompt Selection For Large Language Models, by Viet-tung Do et al.
Automatic Prompt Selection for Large Language Models
by Viet-Tung Do, Van-Khanh Hoang, Duy-Hung Nguyen, Shahab Sabahi, Jeff Yang, Hajime Hotta, Minh-Tien Nguyen, Hung Le
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper proposes an effective method to automatically select the optimal prompt from a set of synthetic candidate prompts for Large Language Models (LLMs) in natural language processing tasks. The approach consists of three steps: clustering training data to generate prompts, synthesizing input-prompt-output tuples for prompt evaluation, and selecting the best prompt based on relevance to the input. This method balances generality-specificity and eliminates the need for extensive training and inference. It demonstrates competitive performance on zero-shot question-answering datasets such as GSM8K, MultiArith, and AQuA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers work better with language. Right now, people have to come up with special instructions (called prompts) to get the computer to do tasks like answering questions. But this can be hard and time-consuming. This research suggests a new way to automatically choose the best prompt for a task. It does this by grouping similar tasks together, generating many possible prompts, and then choosing the one that works best. This approach is good at finding the right balance between being too general or too specific. The results show that it can work well on certain types of question-answering tasks. |
Keywords
» Artificial intelligence » Clustering » Inference » Natural language processing » Prompt » Question answering » Zero shot
