7 citations · 7 across the 5 of their papers we have counts for
6 papers
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph
Jiaxin Fang, Runyuan He, Arnav Gupta +3
High-quality coding problems are expensive to collect. Benchmarks such as SWE-bench rely on substantial human curation, while synthetic alternatives typically rely on costly model…
FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
Runyuan He, Qiuyang Mang, Shang Zhou +14
Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implement…
Optimism in Equality Saturation
Russel Arbore, Alvin Cheung, Max Willsey
Equality saturation is a program optimization technique based on non-destructive rewriting and a form of abstract interpretation called e-class analysis. Existing e-class analyses…
Structure-Aware Fill-in-the-Middle Pretraining for Code
Linyuan Gong, Alvin Cheung, Mostafa Elhoushi +1
Fill-in-the-Middle (FIM) is a common pretraining method for code LLMs, where models complete code segments given surrounding context. However, existing LLMs treat code as plain tex…
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
Linyuan Gong, Sida Wang, Mostafa Elhoushi +1
We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. This benchmark focuses…
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
Linyuan Gong, Mostafa Elhoushi, Alvin Cheung
Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature. We introduce…