1.2k citations · 2.5k across the 21 of their papers we have counts for
6 papers · 1 filter
Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai +5
In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong…
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu +4
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many i…
Multi-Vector Retrieval as Sparse Alignment
Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu +5
Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alig…
RARR: Researching and Revising What Language Models Say, Using Language Models
Luyu Gao, Zhuyun Dai, Panupong Pasupat +8
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading conte…
Promptagator: Few-shot Dense Retrieval From 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7
Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is lim…
Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao +4
Many important questions (e.g. "How to eat healthier?") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems…