arXiv:2502.15835v5 Announce Type: replace Abstract: Pragmatic reasoning helps interlocutors infer intended meaning from ambiguous or underspecified messages by considering shared context and counterfactual alternatives. Similar challenges arise in natural language-to-code generation, where user instructions often admit multiple plausible candidate programs. However, direct RSA-style inference is difficult because it requires probability estimation over large spaces of programs and alternative instructions. We propose CodeRSA, an RSA-motivated reranking method that makes pragmatic reasoning tra
The rapid advancement in Large Language Models (LLMs) has amplified the need for more robust and pragmatic approaches to natural language-to-code generation, and this research addresses a core challenge within current model capabilities.
Improving LLM code generation through pragmatic reasoning can significantly enhance the reliability and efficiency of AI development, automating more complex programming tasks and reducing human intervention.
The ability of LLMs to infer intended meaning from ambiguous instructions will improve, leading to more accurate and useful code suggestions, ultimately advancing the practical application of AI in software development.
- · AI developers
- · Software companies
- · AI infrastructure providers
- · Monotonous coding tasks
- · Companies slow to adopt AI-assisted development
More efficient and accurate code generation by LLMs.
Accelerated software development cycles and reduction in junior developer tasks.
New paradigms for human-computer interaction in programming, potentially requiring fewer highly specialized developers for routine tasks.
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Read at arXiv cs.CL