activity
20222025
most citedReflexion: Language Agents with Verbal Reinforcement Learning

288 citations · 380 across the 12 of their papers we have counts for

collaborators

12 papers

cs.AI2025★ 1 cited

ReasoningWeekly: A General Knowledge and Verbal Reasoning Challenge for Large Language Models

Zixuan Wu, Francesca Lucchetti, Aleksander Boruch-Gruszecki +5

Existing benchmarks for frontier models often test specialized, "PhD-level" knowledge that is difficult for non-experts to grasp. In contrast, we present a benchmark with 613 probl…

cs.CL2024★ 2 cited

SelfCodeAlign: Self-Alignment for Code Generation

Yuxiang Wei, Federico Cassano, Jiawei Liu +7

Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAli…

cs.SE2024★ 67 cited

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov, Raymond Li, Loubna Ben Allal +63

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership wi…

cs.SE2023★ 2 cited

Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions

Federico Cassano, Luisa Li, Akul Sethi +8

A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural l…

cs.SE2023

npm-follower: A Complete Dataset Tracking the NPM Ecosystem

Donald Pinckney, Federico Cassano, Arjun Guha +1

Software developers typically rely upon a large network of dependencies to build their applications. For instance, the NPM package repository contains over 3 million packages and s…

cs.PL2023★ 3 cited

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Federico Cassano, John Gouwar, Francesca Lucchetti +7

Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks…

cs.SE2023★ 1 cited

Type Prediction With Program Decomposition and Fill-in-the-Type Training

Federico Cassano, Ming-Ho Yee, Noah Shinn +2

TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated typ…

cs.CR2023★ 2 cited

SafeLLVM: LLVM Without The ROP Gadgets!

Federico Cassano, Charles Bershatsky, Jacob Ginesin +1

Memory safety is a cornerstone of secure and robust software systems, as it prevents a wide range of vulnerabilities and exploitation techniques. Among these, we focus on Return-Or…

cs.SE2023

A Large Scale Analysis of Semantic Versioning in NPM

Donald Pinckney, Federico Cassano, Arjun Guha +1

The NPM package repository contains over two million packages and serves tens of billions of downloads per-week. Nearly every single JavaScript application uses the NPM package man…

cs.AI2023★ 288 cited

Reflexion: Language Agents with Verbal Reinforcement Learning

Noah Shinn, Federico Cassano, Edward Berman +3

Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challengin…

cs.LG2022★ 14 cited

MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation

Federico Cassano, John Gouwar, Daniel Nguyen +10

Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code gene…

cs.SE2022

Flexible and Optimal Dependency Management via Max-SMT

Donald Pinckney, Federico Cassano, Arjun Guha +3

Package managers such as NPM have become essential for software development. The NPM repository hosts over 2 million packages and serves over 43 billion downloads every week. Unfor…