Summary of Supervisory Prompt Training, by Jean Ghislain Billa et al.
Supervisory Prompt Training
by Jean Ghislain Billa, Min Oh, Liang Du
First submitted to arxiv on: 26 Mar 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 A novel approach to automating the generation of effective prompts for Large Language Models (LLMs) is proposed in this paper. The method, called Supervisory Prompt Training (SPT), uses a dual LLM system where one LLM generates text while the other provides feedback and improves prompts over time. This collaborative process allows SPT to refine prompts continuously, enhancing the performance of LLMs and reducing hallucinations. The paper presents experimental results on four benchmarks, showing significant improvements in accuracy, including a 28.3% increase for GPT-4 on GSM8K from 65.8% to 94.1%. This advance offers an efficient and scalable alternative to traditional model fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. But they need help getting better, and one way is with special instructions called prompts. These prompts are usually made by humans, but it’s hard work and not very efficient. This paper shows a new way to make these prompts using two computer systems that work together. One system makes the prompt, and the other checks if it’s good or not, then makes it better. This helps the LLM get really good at understanding text and reduces mistakes. The researchers tested this new method on four different tests and showed that it worked much better than before. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Prompt
