Summary of Grounding Data Science Code Generation with Input-output Specifications, by Yeming Wen et al.
Grounding Data Science Code Generation with Input-Output Specifications
by Yeming Wen, Pengcheng Yin, Kensen Shi, Henryk Michalewski, Swarat Chaudhuri, Alex Polozov
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Programming Languages (cs.PL); Software Engineering (cs.SE)
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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 addresses a crucial challenge in large language model (LLM) programming: aligning outputs with both natural language prompts and input-output specifications. Current LLMs struggle to generate accurate code when given ambiguous NL prompts, requiring additional I/O specifications. The authors propose GIFT4Code, an instruction fine-tuning approach that utilizes synthetic data produced by the LLM and execution-derived feedback in the form of program I/O specifications. This method facilitates learning signals for the LLM, significantly improving its ability to generate executable code aligned with user specifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate code from natural language prompts, but they struggle when prompts are ambiguous. To fix this, researchers have developed a new way to fine-tune these models using data produced by the model itself and feedback from how the code works. This method helps the model learn what it means to follow instructions correctly. The authors tested their approach on two challenging data science tasks and found that it greatly improved the quality of generated code. |
Keywords
* Artificial intelligence * Fine tuning * Large language model * Synthetic data
