Summary of Jumpcoder: Go Beyond Autoregressive Coder Via Online Modification, by Mouxiang Chen et al.
JumpCoder: Go Beyond Autoregressive Coder via Online Modification
by Mouxiang Chen, Hao Tian, Zhongxin Liu, Xiaoxue Ren, Jianling Sun
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces JumpCoder, a novel framework that enables online modification and non-sequential code generation for large language models (LLMs). The traditional autoregressive sequential generation in LLMs lacks reversibility, leading to error propagation. JumpCoder addresses this limitation by inserting new code into the generated code when necessary. This is achieved through an auxiliary infilling model that works with the LLM. The framework uses an “infill-first, judge-later” strategy to identify the best position for insertion and relies on Abstract Syntax Tree (AST) parsing and Generation Model Scoring to validate the inserted code. Experimental results show significant improvements over baselines using six state-of-the-art LLMs across multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary JumpCoder is a new way to generate code that’s more like how humans write code. Right now, big language models can only generate code one step at a time, which makes it hard to fix mistakes they make along the way. JumpCoder fixes this by letting the model add new code to what’s already been written, kind of like how we might go back and correct something in our own writing. This helps prevent errors from adding up and makes the generated code better. |
Keywords
» Artificial intelligence » Autoregressive » Parsing » Syntax
