Summary of Guiding Enumerative Program Synthesis with Large Language Models, by Yixuan Li et al.
Guiding Enumerative Program Synthesis with Large Language Models
by Yixuan Li, Julian Parsert, Elizabeth Polgreen
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers explore the potential of pre-trained Large Language Models (LLMs) in formal synthesis with precise logical specifications. While LLMs have dominated automatic code generation with natural language specifications, they are outperformed by enumerative algorithms in formal synthesis. The authors carefully craft a library of prompts to evaluate LLMs on formal synthesis benchmarks and propose an innovative algorithm that integrates calls to the LLM into a weighted probabilistic search. This approach combines the strengths of LLMs and state-of-the-art formal synthesis algorithms, yielding significant performance gains over individual models. The evaluation is conducted on Syntax-Guided Synthesis (SyGuS) competition benchmarks, showcasing the potential for LLMs in formal synthesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Formal synthesis with precise logical specifications uses computers to generate code from specific rules. Researchers are trying new ways to make this process better. They tested a powerful tool called GPT-3.5 and found it’s not as good as current methods. Instead, they created an algorithm that combines GPT-3.5 with another approach, making the process faster and more accurate. |
Keywords
» Artificial intelligence » Gpt » Syntax
