3 papers
cs.LG2025
Automated Capability Evaluation of Foundation Models
Arash Afkanpour, Omkar Dige, Fatemeh Tavakoli +3
Current evaluation frameworks for foundation models rely heavily on static, manually curated benchmarks, limiting their ability to capture the full breadth of model capabilities. T…
cs.CL2024
On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language
Jacob-Junqi Tian, Omkar Dige, D. B. Emerson +1
Large language models (LLMs) are trained on vast, uncurated datasets that contain various forms of biases and language reinforcing harmful stereotypes that may be subsequently inhe…
cs.CL2024
Mitigating Social Biases in Language Models through Unlearning
Omkar Dige, Diljot Singh, Tsz Fung Yau +4
Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs. Numerous approaches revolve around data pre-processing and fine-tuni…