Training Compute-Optimal Large Language Models
Authors: Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent SIfre
Date: 2022-03-29 00:00:00
Tasks: Anachronisms, Analogical Similarity, Analytic Entailment, Causal Judgment, Common Sense Reasoning, Crash Blossom, Crass AI, Dark Humor Detection, Date Understanding, Disambiguation QA, Discourse Marker Prediction, Empirical Judgments, English Proverbs, Entailed Polarity, Epistemic Reasoning, Evaluating Information Essentiality, Fantasy Reasoning, Figure Of Speech Detection, Formal Fallacies Syllogisms Negation, General Knowledge, GRE Reading Comprehension, HellaSwag, Human Organs Senses Multiple Choice, Hyperbaton, Identify Odd Metapor, Implicatures, Implicit Relations, Intent Recognition, Irony Identification, Known Unknowns, LAMBADA, Language Modelling, Logical Args, Logical Fallacy Detection, Logical Reasoning, Logical Sequence, Logic Grid Puzzle, Mathematical Induction, Mathematical Reasoning, Metaphor Boolean, Misconceptions, MMLU, Moral Permissibility, Movie Dialog Same Or Different, Movie Recommendation, Multiple Choice Question Answering (MCQA), Multi-task Language Understanding, Navigate, Nonsense Words Grammar, Novel Concepts, Odd One Out, Penguins In A Table, Phrase Relatedness, Physical Intuition, Physics MC, Presuppositions As NLI, Question Answering, Question Selection, Reasoning About Colored Objects, Riddle Sense, Ruin Names, Sarcasm Detection, Sentence Ambiguity, Sentence Completion, Similarities Abstraction, SNARKS, Sports Understanding, StrategyQA, Temporal Sequences, Timedial, Understanding Fables, Winowhy, Word Sense Disambiguation

We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over \nummodels language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, \chinchilla, that uses the same compute budget as \gopher but with 70B parameters and 4$\times$ more more data. \chinchilla uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that \chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, \chinchilla reaches a state-of-the-art average accuracy of 67.5\% on the MMLU benchmark, greater than a 7\% improvement over \gopher.