no code implementations • 16 Nov 2023 • Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J. Kim, Joo-Kyung Kim
Specifically, we first leverage LLMs to construct a "question/rationale graph" by using knowledge extraction prompting given the initial question and the rationales generated in the previous steps.
no code implementations • 30 Oct 2023 • Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, Abhinav Sethy
To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.
1 code implementation • 21 Feb 2023 • Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Omar Zia Khan, Zeynab Raeesy, Abhinav Sethy
The retrieval system is trained on a dataset which contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates.