68 citations · 139 across the 5 of their papers we have counts for
3 papers · 1 filter
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen +9
Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing ap…
Reimagining Retrieval Augmented Language Models for Answering Queries
Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez +4
We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where mo…
REPLUG: Retrieval-Augmented Black-Box Language Models
Weijia Shi, Sewon Min, Michihiro Yasunaga +5
We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike pr…