52 citations · 122 across the 4 of their papers we have counts for
4 papers
Aviary: training language agents on challenging scientific tasks
Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths +8
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and exper…
Language agents achieve superhuman synthesis of scientific knowledge
Michael D. Skarlinski, Sam Cox, Jon M. Laurent +6
Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigo…
LAB-Bench: Measuring Capabilities of Language Models for Biology Research
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo +6
There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines.…
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski +3
Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without grou…