Summary of Llasp: Fine-tuning Large Language Models For Answer Set Programming, by Erica Coppolillo et al.
LLASP: Fine-tuning Large Language Models for Answer Set Programming
by Erica Coppolillo, Francesco Calimeri, Giuseppe Manco, Simona Perri, Francesco Ricca
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates the application of Large Language Models (LLMs) to generate code for Answer Set Programming (ASP), a declarative formalism. While LLMs have shown promise in generating code for imperative programming languages, there is a gap in their adaptation to ASP. The authors begin by evaluating several state-of-the-art LLMs and find that they perform inadequately in generating correct ASP programs. To address this, the authors propose LLASP, a fine-tuned lightweight model specifically trained to encode fundamental ASP program patterns. They create an ad-hoc dataset covering various problem specifications that can be encoded in ASP and demonstrate that LLASP generates remarkable ASP programs, outperforming non-fine-tuned LLMs and many eager candidates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers can generate code for a special kind of programming called Answer Set Programming (ASP). So far, large language models have been good at generating code for some types of programming languages. But they haven’t done well with ASP. The researchers tested different large language models and found that none of them did very well. To fix this, they created a new model just for ASP. They trained it on lots of examples of ASP code and then had it generate its own code. It turned out to be much better than the other models! This is important because it could help people use computers to solve problems in areas like science and medicine. |
