More room for language: Investigating the effect of retrieval on language models
David Samuel, Lucas Charpentier, Sondre Wold
Abstract
Retrieval-augmented language models pose a promising alternative to standard language modeling. During pretraining, these models search in a corpus of documents for contextually relevant information that could aid the language modeling objective. We introduce an ‘ideal retrieval’ methodology to study these models in a fully controllable setting. We conduct an extensive evaluation to examine how retrieval augmentation affects the behavior of the underlying language model. Among other things, we observe that these models: (i) save substantially less world knowledge in their weights, (ii) are better at understanding local context and inter-word dependencies, but (iii) are worse at comprehending global context.
BibTeX
@inproceedings{samuel-etal-2024-room,
title = "More room for language: Investigating the effect of retrieval on language models",
author = "Samuel, David and
Charpentier, Lucas and
Wold, Sondre",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.26/",
doi = "10.18653/v1/2024.naacl-short.26",
pages = "282--305"
}