RESEARCH

NLP

Autoregressive Search Engines: Generating Substrings as Document Identifiers

October 31, 2022

Abstract

Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive language models are emerging as the de-facto standard for generating answers, with newer and more powerful systems emerging at an astonishing pace. In this paper we argue that all this (and future) progress can be directly applied to the retrieval problem with minimal intervention to the models’ architecture. Previous work has explored ways to partition the search space into hierarchical structures and retrieve documents by autoregressively generating their unique identifier. In this work we propose an alternative that doesn’t force any structure in the search space: using all ngrams in a passage as its possible identifiers. This setup allows us to use an autoregressive model to generate and score distinctive ngrams, that are then mapped to full passages through an efficient data structure. Empirically, we show this not only outperforms prior autoregressive approaches but also leads to an average improvement of at least 10 points over more established retrieval solutions for passage-level retrieval on the KILT benchmark, establishing new state-of-the-art downstream performance on some datasets, while using a considerably lighter memory footprint than competing systems. Code and pre-trained models are available at https://github.com/facebookresearch/SEAL.

Download the Paper

AUTHORS

Written by

Fabio Petroni

Giuseppe Ottaviano

Michele Bevilacqua

Patrick Lewis

Scott Yih

Sebastian Riedel

Publisher

ARR or NeurIPS

Related Publications

December 12, 2024

NLP

CORE MACHINE LEARNING

Memory Layers at Scale

Vincent-Pierre Berges, Barlas Oguz

December 12, 2024

December 12, 2024

NLP

Byte Latent Transformer: Patches Scale Better Than Tokens

Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer

December 12, 2024

December 12, 2024

HUMAN & MACHINE INTELLIGENCE

NLP

Explore Theory-of-Mind: Program-Guided Adversarial Data Generation for Theory of Mind Reasoning

Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi, Yulia Tsvetkov, Yonatan Bisk, Yejin Choi, Asli Celikyilmaz

December 12, 2024

December 11, 2024

NLP

Large Concept Models: Language Modeling in a Sentence Representation Space

The LCM team, Loic Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R. Costa-jussa, David Dale, Hady Elsahar, Kevin Heffernan, João Maria Janeiro, Tuan Tran, Christophe Ropers, Eduardo Sánchez, Robin San Roman, Alexandre Mourachko, Safiyyah Saleem, Holger Schwenk

December 11, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.