67 citations · 86 across the 12 of their papers we have counts for
5 papers · 1 filter
Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations
Rose E. Wang, Pawan Wirawarn, Kenny Lam +2
Many open-ended conversations (e.g., tutoring lessons or business meetings) revolve around pre-defined reference materials, like worksheets or meeting bullets. To provide a framewo…
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Sheryl Hsu, Omar Khattab, Chelsea Finn +1
The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately,…
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles
Li Siyan, Vethavikashini Chithrra Raghuram, Omar Khattab +2
Users can divulge sensitive information to proprietary LLM providers, raising significant privacy concerns. While open-source models, hosted locally on the user's machine, alleviat…
Backtracing: Retrieving the Cause of the Query
Rose E. Wang, Pawan Wirawarn, Omar Khattab +2
Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for suc…
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
Yijia Shao, Yucheng Jiang, Theodore A. Kanell +3
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplore…