activity
20192026
most citedMARG: Multi-Agent Review Generation for Scientific Papers

8 citations · 12 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

ExperienceIndex: Artifact-Grounded Memory

Peter Baile Chen, Geoffrey X. Yu, Xinming Liu +5

Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature). As humans interact with th…

cs.CL2026

Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks

Jena D. Hwang, Varsha Kishore, Amanpreet Singh +9

Recent advances have made long-form report-generating systems widely available. This has prompted evaluation frameworks that use LLM-as-judge protocols and claim verification, alon…

cs.HC2026

Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset

Dany Haddad, Dan Bareket, Joseph Chee Chang +19

AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world set…

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…

cs.CL2019★ 4 cited

CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense

Michael Chen, Mike D'Arcy, Alisa Liu +2

Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on…