Thinking ahead: prediction in context as a keystone of language in humans and machines
Christopher A Baldassano,
Ken A. Norman,
Posted 03 Dec 2020
bioRxiv DOI: 10.1101/2020.12.02.403477
Posted 03 Dec 2020
Departing from classical rule-based linguistic models, advances in deep learning have led to the development of a new family of self-supervised deep language models (DLMs). These models are trained using a simple self-supervised autoregressive objective, which aims to predict the next word in the context of preceding words in real-life corpora. After training, autoregressive DLMs are able to generate new 'context-aware' sentences with appropriate syntax and convincing semantics and pragmatics. Here we provide empirical evidence for the deep connection between autoregressive DLMs and the human language faculty using a 30-min spoken narrative and electrocorticographic (ECoG) recordings. Behaviorally, we demonstrate that humans have a remarkable capacity for word prediction in natural contexts, and that, given a sufficient context window, DLMs can attain human-level prediction performance. Next, we leverage DLM embeddings to demonstrate that many electrodes spontaneously predict the meaning of upcoming words, even hundreds of milliseconds before they are perceived. Finally, we demonstrate that contextual embeddings derived from autoregressive DLMs capture neural representations of the unique, context-specific meaning of words in the narrative. Our findings suggest that deep language models provide an important step toward creating a biologically feasible computational framework for generative language.
- Downloaded 2,571 times
- Download rankings, all-time:
- Site-wide: 5,562
- In neuroscience: 507
- Year to date:
- Site-wide: 1,495
- Since beginning of last month:
- Site-wide: 4,248
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!