5 citations · 6 across the 4 of their papers we have counts for
4 papers
Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition
Danielle Cohen, Yoni Halpern, Noam Kahlon +5
Understanding user intents from UI interaction trajectories remains a challenging, yet crucial, frontier in intelligent agent development. While massive, datacenter-based, multi-mo…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
In-Context Example Ordering Guided by Label Distributions
Zhichao Xu, Daniel Cohen, Bei Wang +1
By allowing models to predict without task-specific training, in-context learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a number of problems persist in…
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
Puxuan Yu, Daniel Cohen, Hemank Lamba +2
In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthine…