29 citations · 53 across the 11 of their papers we have counts for
12 papers
Type-Constrained Code Generation with Language Models
Niels Mündler, Jingxuan He, Hao Wang +3
Large language models (LLMs) have achieved notable success in code generation. However, they still frequently produce uncompilable output because their next-token inference procedu…
GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Manish Shetty, Naman Jain, Jinjian Liu +3
Developing high-performance software is a complex task that requires specialized expertise. We introduce GSO, a benchmark for evaluating language models' capabilities in developing…
R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents
Naman Jain, Jaskirat Singh, Manish Shetty +3
Improving open-source models on real-world SWE tasks (solving GITHUB issues) faces two key challenges: 1) scalable curation of execution environments to train these models, and, 2)…
Challenges and Paths Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li +7
AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be ad…
Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis
Manish Shetty, Naman Jain, Adwait Godbole +2
Despite extensive usage in high-performance, low-level systems programming applications, C is susceptible to vulnerabilities due to manual memory management and unsafe pointer oper…
Specifications: The missing link to making the development of LLM systems an engineering discipline
Ion Stoica, Matei Zaharia, Joseph Gonzalez +8
Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This ca…
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Naman Jain, King Han, Alex Gu +7
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as ne…
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?
Alex Gu, Wen-Ding Li, Naman Jain +4
While language models are increasingly more proficient at code generation, they still frequently generate incorrect programs. Many of these programs are obviously wrong, but others…
CodeScholar: Growing Idiomatic Code Examples
Manish Shetty, Koushik Sen, Ion Stoica
Programmers often search for usage examples for API methods. A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely…
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Arnav Singhvi, Manish Shetty, Shangyin Tan +4
Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic "prompt engineerin…
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Naman Jain, Tianjun Zhang, Wei-Lin Chiang +3
Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively c…
Multiversion Hindsight Logging for Continuous Training
Rolando Garcia, Anusha Dandamudi, Gabriel Matute +4
Production Machine Learning involves continuous training: hosting multiple versions of models over time, often with many model versions running at once. When model performance does…