GitHub

uqlm: Uncertainty Quantification for Language Models

UQLM provides a suite of response-level scorers for quantifying the uncertainty of Large Language Model (LLM) outputs. Each scorer returns a confidence score between 0 and 1, where higher scores ...
IEEE

DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models

Abstract: Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context ...
GitHub

GitHub - anthropics/claude-code-action

A general-purpose Claude Code action for GitHub PRs and issues that can answer questions and implement code changes. This action intelligently detects when to activate based on your workflow ...
IEEE

Advancing Large Language Models in Code Generation: Usaco Benchmark and Bug Mitigation Insights

Abstract: Recently, Large Language Models (LLMs) have made substantial progress in code generation, but they still frequently generate code containing logic errors or syntax bugs. While research has ...