202 citations · 471 across the 35 of their papers we have counts for
7 papers · 1 filter
LaMP: When Large Language Models Meet Personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky +1
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models…
You can't pick your neighbors, or can you? When and how to rely on retrieval in the NN-LM
Andrew Drozdov, Shufan Wang, Razieh Rahimi +3
Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvement…
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an…
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property…
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard, Tim O'Gorman, Rajarshi Das +6
At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text.…
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das +8
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieva…