3 papers
cs.LG2026
Reference-Based Distillation Detection in LLMs
Rajat Rawat, Sizhe Chen, Akshay Anand +3
Model distillation -- training on outputs from stronger third-party models -- is widely used to boost performance, but raises concerns about unfair advantages and policy violations…
cs.CL2024
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…
cs.CL2024
DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response
Rajat Rawat
Disasters can result in the deaths of many, making quick response times vital. Large Language Models (LLMs) have emerged as valuable in the field. LLMs can be used to process vast…