Summary of Semcoder: Training Code Language Models with Comprehensive Semantics Reasoning, by Yangruibo Ding et al.
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
by Yangruibo Ding, Jinjun Peng, Marcus J. Min, Gail Kaiser, Junfeng Yang, Baishakhi Ray
First submitted to arxiv on: 3 Jun 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 Code Large Language Models (Code LLMs) have excelled in tasks like code completion, but often struggle with deeper semantics such as execution effects and dynamic states. To bridge this gap, researchers introduced a novel strategy called monologue reasoning to train Code LLMs to reason about comprehensive semantics. This approach links static code text with dynamic execution states, allowing the models to understand code semantics by reasoning about key properties, constraints, and execution behaviors using natural language. The resulting model, SemCoder, shows competitive performance on code generation and execution reasoning tasks, outperforming GPT-3.5-turbo in certain benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code Large Language Models (Code LLMs) are super smart at helping with coding tasks, but sometimes they don’t fully understand what the code does. To fix this, scientists created a new way to train these models called monologue reasoning. This lets the models learn about code by thinking about what it’s supposed to do and how different parts work together. The result is a model called SemCoder that can write code and explain what it means. It even beats other smart models on some tasks! |
Keywords
» Artificial intelligence » Gpt » Semantics
