8 citations · 28 across the 10 of their papers we have counts for
Showing 2024 · cs.CLShow all
2 papers · 2 filters
cs.CL2024★ 6 cited
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
Akari Asai, Jacqueline He, Rulin Shao +22
Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenS…
cs.CL2024★ 8 cited
MARG: Multi-Agent Review Generation for Scientific Papers
Mike D'Arcy, Tom Hope, Larry Birnbaum +1
We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discus…