Summary of Benchmarks and Metrics For Evaluations Of Code Generation: a Critical Review, by Debalina Ghosh Paul et al.
Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review
by Debalina Ghosh Paul, Hong Zhu, Ian Bayley
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 abstract discusses the challenges in evaluating Large Language Models (LLMs) designed for programming tasks, such as generating program code from natural language input. Despite significant research efforts, there is still no unified approach to evaluate and compare these models. The paper reviews existing work on benchmarking and metric selection for LLMs, highlighting key aspects that require attention. Additionally, the authors identify potential research directions for future improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This abstract talks about how to test special computer programs called Large Language Models (LLMs) that can write code from text. Right now, there’s no clear way to compare and evaluate these models, even though many researchers have worked on this problem. The paper looks at what other experts have done so far to see where we need more improvement. It also suggests new ideas for future research. |
Keywords
» Artificial intelligence » Attention
