1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…
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…
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
Omkar Dige, Jacob-Junqi Tian, David Emerson +1
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the lear…