Summary of Code Generation with Alphacodium: From Prompt Engineering to Flow Engineering, by Tal Ridnik et al.
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
by Tal Ridnik, Dedy Kredo, Itamar Friedman
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a new approach to code generation by Large Language Models (LLMs), called AlphaCodium, which improves the performance of LLMs on code problems. Unlike natural language generation, code generation requires matching exact syntax, identifying happy paths and edge cases, and paying attention to numerous small details. The proposed flow is a test-based, multi-stage, code-oriented iterative process that consistently and significantly improves results. For example, GPT-4 accuracy increased from 19% with a single prompt to 44% with AlphaCodium on the CodeContests dataset, which includes competitive programming problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers write code better! It’s like when you’re trying to solve a puzzle and need to follow specific rules. The computer tries different approaches to write the correct code, and this new method, called AlphaCodium, makes it more successful. The authors tested it on a big dataset of programming problems and showed that it works much better than before. This is important because it can help make computers smarter and solve more complex tasks. |
Keywords
* Artificial intelligence * Attention * Gpt * Prompt * Syntax
