1 citations · 1 across the 5 of their papers we have counts for
5 papers
Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloys
Satanu Ghosh, Collin Holgate, Neal R. Brodnik +4
We apply preference learning to the task of language model-guided design of novel structural alloys. In contrast to prior work that focuses on generating stable inorganic crystals,…
Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
Alan Li, Yixin Liu, Arpan Sarkar +2
Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through complex reasoning. While automated…
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
David Wadden, Kejian Shi, Jacob Morrison +11
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks.…
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…
CARE: Extracting Experimental Findings From Clinical Literature
Aakanksha Naik, Bailey Kuehl, Erin Bransom +2
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for…