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Ryan Shar

2 accepted papers

2026

EditBench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits

ICLR 2026oral

Instructed code editing, where LLMs directly modify a developer's existing code based on a user instruction, is becoming a widely used interaction mode in AI coding assistants. However, few benchmarks directly evaluate this capability and current datasets often rely on artificial sources. We introdu…

Cited by 0SourcecodeScholar
2025

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback

ACL 2025finding

Programming with a coding assistant is a fundamentally interactive process, yet existing static benchmarks fail to capture key features of model-user collaboration. We introduce an interactive evaluation pipeline to examine how LLMs incorporate different types of feedback in a collaborative setting,…