Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Method 2. Method 3. Experiments on real data 3. Experiments on real data 3.1. Benefits scale with model size and 3.2. Faster inference 3.1. Benefits scale with model size and 3.2. Faster inference 3.3. Learning global patterns with multi-byte prediction and 3.4. Searching for the optimal n 3.3. Learning global patterns with multi-byte prediction and 3.4. Searching for the optimal n n 3.5. Training for multiple epochs and 3.6. Finetuning multi-token predictors 3.5. Training for multiple epochs and 3.6. Finetuning multi-token predictors 3.7. Multi-token prediction on natural language 3.7. Multi-token prediction on natural language 4. Ablations on synthetic data and 4.1. Induction capability 4. Ablations on synthetic data and 4.1. Induction capability 4.2. Algorithmic reasoning 4.2. Algorithmic reasoning 5. Why does it work? Some speculation and 5.1. Lookahead reinforces choice points 5. Why does it work? Some speculation and 5.1. Lookahead reinforces choice points 5.2. Information-theoretic argument 5.2. Information-theoretic argument 6. Related work 6. Related work 7. Conclusion, Impact statement, Environmental impact, Acknowledgements, and References 7. Conclusion, Impact statement, Environmental impact, Acknowledgements, and References A. Additional results on self-speculative decoding A. Additional results on self-speculative decoding B. Alternative architectures B. Alternative architectures C. Training speeds C. Training speeds D. Finetuning D. Finetuning E. Additional results on model scaling behavior E. Additional results on model scaling behavior F. Details on CodeContests finetuning F. Details on CodeContests finetuning G. Additional results on natural language benchmarks G. Additional results on natural language benchmarks H. Additional results on abstractive text summarization H. Additional results on abstractive text summarization I. Additional results on mathematical reasoning in natural language I. Additional results on mathematical reasoning in natural language J. Additional results on induction learning J. Additional results on induction learning K. Additional results on algorithmic reasoning K. Additional results on algorithmic reasoning L. Additional intuitions on multi-token prediction L. Additional intuitions on multi-token prediction M. Training hyperparameters M. Training hyperparameters H. Additional results on abstractive text summarization In this section, we report comprehensive evaluation results on summarization tasks for the 7B parameter models trained on 200B and 500B tokens of natural language from Section 3.7. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech, and contributed equally;
(2) Badr Youbi IdrissiFAIR at Meta, LISN Université Paris-Saclay, and contributed equally;
(3) Baptiste Rozière, FAIR at Meta;
(4) David Lopez-Paz, FAIR at Meta and his the last author;
(5) Gabriel Synnaeve, FAIR at Meta and the last author. Authors: Authors: (1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech, and contributed equally; (2) Badr Youbi IdrissiFAIR at Meta, LISN Université Paris-Saclay, and contributed equally; (3) Baptiste Rozière, FAIR at Meta; (4) David Lopez-Paz, FAIR at Meta and his the last author; (5) Gabriel Synnaeve, FAIR at Meta and the last author.

Multi-Token Prediction for Abstractive Text Summarization: ROUGE Metrics

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Formalization of the SylloBio-NLI Resource Generation Process

The Art of Transformers: How AI Intuitively Summarizes Business Papers Using NLP

Turning Movie Scripts into Short Summaries—Smarter and Faster

Smart Summaries: Teaching AI to Pick Key Scenes from Scripts

This Model Knows Which Movie Scenes Matter Most

How AI Learns to Summarize Movies Like a Human

A Formalization of the SylloBio-NLI Resource Generation Process

The Art of Transformers: How AI Intuitively Summarizes Business Papers Using NLP

Turning Movie Scripts into Short Summaries—Smarter and Faster

Smart Summaries: Teaching AI to Pick Key Scenes from Scripts

This Model Knows Which Movie Scenes Matter Most

How AI Learns to Summarize Movies Like a Human

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps