no code implementations • 16 Jan 2024 • Zafaryab Rasool, Scott Barnett, David Willie, Stefanus Kurniawan, Sherwin Balugo, Srikanth Thudumu, Mohamed Abdelrazek
Our novel approach uses the reasoning capabilities of LLMs to 1) adapt queries to the domain, 2) synthesise subtle variations to queries, and 3) evaluate the synthesised test dataset.
no code implementations • 14 Nov 2023 • Zafaryab Rasool, Stefanus Kurniawan, Sherwin Balugo, Scott Barnett, Rajesh Vasa, Courtney Chesser, Benjamin M. Hampstead, Sylvie Belleville, Kon Mouzakis, Alex Bahar-Fuchs
In this paper, we specifically focus on this underexplored context and conduct empirical analysis of LLMs (GPT-4 and GPT-3. 5) on question types, including single-choice, yes-no, multiple-choice, and number extraction questions from documents in zero-shot setting.
no code implementations • 8 Feb 2020 • Zafaryab Rasool, Rui Zhou, Lu Chen, Chengfei Liu, Jiajie Xu
Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space.