Showing 2024Show all
3 papers · 1 filter
cs.SE2024
Commit0: Library Generation from Scratch
Wenting Zhao, Nan Jiang, Celine Lee +4
With the goal of benchmarking generative systems beyond expert software development ability, we introduce Commit0, a benchmark that challenges AI agents to write libraries from scr…
cs.CL2024
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian +6
Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging i…
cs.CL2024
On Leakage of Code Generation Evaluation Datasets
Alexandre Matton, Tom Sherborne, Dennis Aumiller +7
In this paper, we consider contamination by code generation test sets, in particular in their use in modern large language models. We discuss three possible sources of such contami…