33 citations · 46 across the 4 of their papers we have counts for
4 papers
Zero-Overhead Introspection for Adaptive Test-Time Compute
Rohin Manvi, Joey Hong, Tim Seyde +3
Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real…
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Rohin Manvi, Anikait Singh, Stefano Ermon
Inference-time computation is a powerful paradigm to enhance the performance of large language models (LLMs), with Best-of-N sampling being a widely used technique. However, this m…
Large Language Models are Geographically Biased
Rohin Manvi, Samar Khanna, Marshall Burke +2
Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation…
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
Rohin Manvi, Samar Khanna, Gengchen Mai +3
The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can…