3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass
Tong Chen, Hao Fang, Patrick Xia +5
Large language models (LMs) are typically adapted to improve performance on new contexts (\eg text prompts that define new tasks or domains) through fine-tuning or prompting. Howev…
cs.CL2024★ 1 cited
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
Boshi Wang, Hao Fang, Jason Eisner +2
Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs…
cs.HC2023★ 3 cited
Co-audit: tools to help humans double-check AI-generated content
Andrew D. Gordon, Carina Negreanu, José Cambronero +9
Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tab…