3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Evaluating How Fine-tuning on Bimodal Data Effects Code Generation
Gabriel Orlanski, Seonhye Yang, Michael Healy
Despite the increase in popularity of language models for code generation, it is still unknown how training on bimodal coding forums affects a model's code generation performance a…
cs.CL2022★ 3 cited
Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting
Gabriel Orlanski
Large language models have shown that impressive zero-shot performance can be achieved through natural language prompts (Radford et al., 2019; Brown et al., 2020; Sanh et al., 2021…
cs.CL2021
Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation
Gabriel Orlanski, Alex Gittens
Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 Stack…