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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…

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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,…