Summary of Horizon-length Prediction: Advancing Fill-in-the-middle Capabilities For Code Generation with Lookahead Planning, by Yifeng Ding et al.
Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning
by Yifeng Ding, Hantian Ding, Shiqi Wang, Qing Sun, Varun Kumar, Zijian Wang
First submitted to arxiv on: 4 Oct 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 addresses a challenge in code language models that generate missing code given both left and right contexts. The current training paradigm for Fill-in-the-Middle (FIM) models often leads to struggles in generating content that aligns smoothly with the surrounding context, which can be detrimental to open-domain code completion tasks. To address this issue, existing works rely on rule-based post-processing methods, but these are not practically usable without restrictive assumptions. The paper’s main contribution is a novel approach that improves FIM model performance and enables more accurate generation of missing code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps code language models do a better job at filling in the middle of code. Right now, these models have trouble creating smooth connections between different parts of the code. To fix this, some people use rules to help the model make better choices, but these rules only work if you know exactly what the correct answer should look like. The problem is that we often don’t know that in real-life coding tasks. This paper shows a new way to train the models that makes them much better at filling in the middle of code without needing those restrictive rules. |
