47 citations · 85 across the 19 of their papers we have counts for
5 papers · 1 filter
MetaLint: Easy-to-Hard Generalization for Code Linting
Atharva Naik, Lawanya Baghel, Dhakshin Govindarajan +4
Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during traini…
CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose
Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main…
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
Shubham Gandhi, Atharva Naik, Yiqing Xie +1
We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and th…
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Atharva Naik, Prakam, Yash Mathur +6
Although many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs) within domains such as mathematics, coding, or data wrangling, few abstract away from doma…
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction
Atharva Naik, Darsh Agrawal, Hong Sng +8
Historical linguists have long written "programs" that convert reconstructed words in an ancestor language into their attested descendants via ordered string rewrite functions (cal…