Summary of Selective Prompt Anchoring For Code Generation, by Yuan Tian et al.
Selective Prompt Anchoring for Code Generation
by Yuan Tian, Tianyi Zhang
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 study investigates the challenges in generating correct code from natural language prompts using large language models (LLMs). The authors find that as LLMs generate more code tokens, they tend to pay less attention to user intent, leading to errors. To address this issue, the researchers propose Selective Prompt Anchoring (SPA), a technique to guide LLMs to focus on user intent when generating code. They evaluate SPA using six base LLMs across six benchmarks and demonstrate that it enhances Pass@1 by up to 12.9%, outperforming state-of-the-art code generation methods in all settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code generation from natural language prompts is a crucial task in software development, but current large language models (LLMs) struggle to produce fully correct code that aligns with user intent. This study reveals that LLMs tend to pay less attention to user prompts as they generate more code tokens, leading to errors. To solve this problem, the authors propose a new technique called Selective Prompt Anchoring (SPA) to guide LLMs to focus on user intent when generating code. |
Keywords
» Artificial intelligence » Attention » Prompt
