10 citations · 13 across the 6 of their papers we have counts for
6 papers
Advancing AI Research Assistants with Expert-Involved Learning
Tianyu Liu, Simeng Han, Hanchen Wang +27
Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research A…
HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
Simeng Han, Tianyu Liu, Chuhan Li +2
LLMs approach logical and mathematical reasoning through natural or symbolic languages. While natural language offers human-accessible flexibility but suffers from ambiguity, symbo…
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.…
Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
Hyunji Lee, Luca Soldaini, Arman Cohan +2
Prevailing research practice today often relies on training dense retrievers on existing large datasets such as MSMARCO and then experimenting with ways to improve zero-shot genera…
On Learning to Summarize with Large Language Models as References
Yixin Liu, Kejian Shi, Katherine S He +5
Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarizat…
MultiVerS: Improving scientific claim verification with weak supervision and full-document context
David Wadden, Kyle Lo, Lucy Lu Wang +3
The scientific claim verification task requires an NLP system to label scientific documents which Support or Refute an input claim, and to select evidentiary sentences (or rational…