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Found 4 papers, 3 papers with code
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
For Lua, our fine-tuned model achieves the same performance as StarCoderBase as Python -- a very high-resource language -- on the MultiPL-E benchmarks.
Type Prediction With Program Decomposition and Fill-in-the-Type Training
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain.
Reflexion: Language Agents with Verbal Reinforcement Learning
Large language models (LLMs) have been increasingly used to interact with external environments (e. g., games, compilers, APIs) as goal-driven agents.
Ranked #1 on
Code Generation
on HumanEval
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder.
