45 citations · 136 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Arie Cattan, Tom Hope, Doug Downey +4
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-docu…
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents
Catherine Chen, Zejiang Shen, Dan Klein +3
Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are ofte…
S2abEL: A Dataset for Entity Linking from Scientific Tables
Yuze Lou, Bailey Kuehl, Erin Bransom +3
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When app…
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
Zejiang Shen, Tal August, Pao Siangliulue +6
Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools. Recent work has shown that levera…