5 citations · 10 across the 6 of their papers we have counts for
6 papers
DiversityMedQA: Assessing Demographic Biases in Medical Diagnosis using Large Language Models
Rajat Rawat, Hudson McBride, Dhiyaan Nirmal +5
As large language models (LLMs) gain traction in healthcare, concerns about their susceptibility to demographic biases are growing. We introduce {DiversityMedQA}, a novel benchmark…
AAVENUE: Detecting LLM Biases on NLU Tasks in AAVE via a Novel Benchmark
Abhay Gupta, Philip Meng, Ece Yurtseven +2
Detecting biases in natural language understanding (NLU) for African American Vernacular English (AAVE) is crucial to developing inclusive natural language processing (NLP) systems…
PathFinder: Guided Search over Multi-Step Reasoning Paths
Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru +4
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However…
Contrastive Decoding Improves Reasoning in Large Language Models
Sean O'Brien, Mike Lewis
We demonstrate that Contrastive Decoding -- a simple, computationally light, and training-free text generation method proposed by Li et al 2022 -- achieves large out-of-the-box imp…
Shepherd: A Critic for Language Model Generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan +7
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shephe…
A Model-Independent Radio Telescope Dark Matter Search in the L and S Bands
Aya Keller, Nicole Wolff, Karl van Bibber
Ultralight bosonic dark matter in its most general form can be detected through its decay or annihilation to a quasimonochromatic radio line. Assuming only that this line is consis…