9 citations · 33 across the 11 of their papers we have counts for
14 papers
Reinforcement Learning over Predictive Distributions for LLM Regression
Jungsoo Park, Hyungjoo Chae, Ethan Mendes +4
Large language models (LLMs) have emerged as flexible regressors capable of predicting real-valued quantities from heterogeneous inputs. Yet most LLM regression objectives optimize…
Improving Attributed Long-form Question Answering with Intent Awareness
Xinran Zhao, Aakanksha Naik, Jay DeYoung +4
Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models are trained on diverse academic papers…
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…
PreScience: A Dataset and Benchmark for Scientific Forecasting
Anirudh Ajith, Amanpreet Singh, Jay DeYoung +7
Can AI systems trained on the existing scientific record forecast the advances that will follow? We introduce PreScience, a dataset and benchmark for scientific forecasting built a…
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung +4
Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contr…
Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs
Somin Wadhwa, Jay DeYoung, Benjamin Nye +2
Results from Randomized Controlled Trials (RCTs) establish the comparative effectiveness of interventions, and are in turn critical inputs for evidence-based care. However, results…