Hugging Face Trending Papers
Talking to Itself While Coding: What Makes Comments Help Code Generation?
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
2d ago
Talking to Itself While Coding: What Makes Comments Help Code Generation?
The study investigates how natural-language comments influence code generation by large language models. Observational and controlled experiments on LiveCodeBench reveal that comment frequency and general intent do not predict success, but comments derived from correct solutions significantly improve recipient model performance by an average of 17.2%. Conversely, comments from failed solutions or unrelated problems either offer no benefit or even reduce performance, and most models cannot fully recover the advantage of well‑crafted comments.
arXiv AI
Jun 12
HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation
arXiv:2601. 19072v3 Announce Type: replace-cross Abstract: Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses a significant challenge to the adoption of LLMs in code review workflows.
Hugging Face Trending Papers
Jul 2
An Exploratory Study on LLM-Generated Code and Comments in Code Repositories
The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization. Reports from large technology companies showed that around 20% to 30% of their code are generated by LLMs.
arXiv AI
Jun 2
Measuring and Mitigating Bias in Code Generated by Large Language Models
arXiv:2606. 00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation tasks.
arXiv AI
Jul 14
Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications
arXiv:2607. 10674v1 Announce Type: cross Abstract: As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification.
Hugging Face Trending Papers
Jul 16
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.