Training Compute-Optimal Large Language Models
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.
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Colab
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
MMLU
Natural Questions
WikiText-2
HellaSwag
TriviaQA
PIQA
BoolQ
WinoGrande
WikiText-103
TruthfulQA
The Pile
BIG-bench
LAMBADA
SIQA
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| General Knowledge | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 94.3 | # 1 | ||
| Figure Of Speech Detection | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 63.3 | # 1 | ||
| Fantasy Reasoning | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 69 | # 1 | ||
| English Proverbs | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 82.4 | # 1 | ||
| Human Organs Senses Multiple Choice | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 85.7 | # 1 | ||
| Mathematical Induction | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 47.3 | # 2 | ||
| Presuppositions As NLI | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 49.9 | # 1 | ||
| Physical Intuition | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 79 | # 1 | ||
| Metaphor Boolean | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 93.1 | # 1 | ||
| Logical Args | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 56.2 | # 2 | ||
| Evaluating Information Essentiality | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 17.6 | # 1 | ||
| Epistemic Reasoning | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 60.6 | # 1 | ||
| Entailed Polarity | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 94 | # 1 | ||
| Analytic Entailment | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 67.1 | # 1 | ||
| Similarities Abstraction | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 87 | # 1 | ||
| Sentence Ambiguity | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 71.7 | # 1 | ||
| Misconceptions | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 65.3 | # 1 | ||
| Moral Permissibility | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 57.3 | # 1 | ||
| Dark Humor Detection | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 66.2 | # 2 | ||
| Understanding Fables | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 60.3 | # 1 | ||
| Timedial | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 68.8 | # 1 | ||
| Riddle Sense | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 85.7 | # 1 | ||
| Irony Identification | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 73.0 | # 1 | ||
| Empirical Judgments | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 67.7 | # 1 | ||
| Discourse Marker Prediction | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 13.1 | # 1 | ||
| Crass AI | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 75.0 | # 3 | ||
| Crash Blossom | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 47.6 | # 2 | ||
| Odd One Out | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 70.9 | # 1 | ||
| Analogical Similarity | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 38.1 | # 1 | ||
| Identify Odd Metapor | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 68.8 | # 1 | ||
| Physics MC | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 65.5 | # 1 | ||
| Question Selection | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 52.6 | # 1 | ||
| Phrase Relatedness | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 94 | # 1 | ||
| Nonsense Words Grammar | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 78 | # 1 | ||
| Movie Dialog Same Or Different | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 54.5 | # 1 | ||
| LAMBADA | BIG-bench | Chinchilla-70B (zero-shot) | Accuracy | 77.4 | # 1 | ||
| Intent Recognition | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 92.8 | # 1 | ||
| Implicit Relations | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 49.4 | # 1 | ||
| Implicatures | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 75 | # 1 | ||
| GRE Reading Comprehension | BIG-bench | Chinchilla-70B (few-shot, k=5) | Accuracy | 53.1 | # 1 | ||
| Word Sense Disambiguation | BIG-bench (Anachronisms) | Chinchilla-70B (few-shot, k=5) | Accuracy | 69.1 | # 1 | ||
| Common Sense Reasoning | BIG-bench (Causal Judgment) | Chinchilla-70B (few-shot, k=5) | Accuracy | 57.4 | # 4 | ||
| Common Sense Reasoning | BIG-bench (Date Understanding) | Chinchilla-70B (few-shot, k=5) | Accuracy | 52.3 | # 5 | ||
| Common Sense Reasoning | BIG-bench (Disambiguation QA) | Chinchilla-70B (few-shot, k=5) | Accuracy | 54.7 | # 4 | ||
| Logical Reasoning | BIG-bench (Formal Fallacies Syllogisms Negation) | Chinchilla-70B (few-shot, k=5) | Accuracy | 52.1 | # 7 | ||
| Multiple Choice Question Answering (MCQA) | BIG-bench (Hyperbaton) | Chinchilla-70B (few-shot, k=5) | Accuracy | 54.2 | # 8 | ||
| Common Sense Reasoning | BIG-bench (Known Unknowns) | Chinchilla-70B (few-shot, k=5) | Accuracy | 65.2 | # 2 | ||
| Logical Reasoning | BIG-bench (Logical Fallacy Detection) | Chinchilla-70B (few-shot, k=5) | Accuracy | 72.1 | # 1 | ||
| Common Sense Reasoning | BIG-bench (Logical Sequence) | Chinchilla-70B (few-shot, k=5) | Accuracy | 64.1 | # 1 | ||
| Logical Reasoning | BIG-bench (Logic Grid Puzzle) | Chinchilla-70B (few-shot, k=5) | Accuracy | 44 | # 1 | ||
| Multiple Choice Question Answering (MCQA) | BIG-bench (Movie Recommendation) | Chinchilla-70B (few-shot, k=5) | Accuracy | 75.6 | # 8 | ||
| Multiple Choice Question Answering (MCQA) | BIG-bench (Navigate) | Chinchilla-70B (few-shot, k=5) | Accuracy | 52.6 | # 4 | ||
| Multiple Choice Question Answering (MCQA) | BIG-bench (Novel Concepts) | Chinchilla-70B (few-shot, k=5) | Accuracy | 65.6 | # 2 | ||
| Logical Reasoning | BIG-bench (Penguins In A Table) | Chinchilla-70B (few-shot, k=5) | Accuracy | 48.7 | # 3 | ||
| Logical Reasoning | BIG-bench (Reasoning About Colored Objects) | Chinchilla-70B (few-shot, k=5) | Accuracy | 59.7 | # 3 | ||
| Multiple Choice Question Answering (MCQA) | BIG-bench (Ruin Names) | Chinchilla-70B (few-shot, k=5) | Accuracy | 47.1 | # 8 | ||
| Sarcasm Detection | BIG-bench (SNARKS) | Chinchilla-70B (few-shot, k=5) | Accuracy | 58.6 | # 7 | ||
| Common Sense Reasoning | BIG-bench (Sports Understanding) | Chinchilla-70B (few-shot, k=5) | Accuracy | 71 | # 4 | ||
| Logical Reasoning | BIG-bench (StrategyQA) | Chinchilla-70B (few-shot, k=5) | Accuracy | 68.3 | # 2 | ||
| Logical Reasoning | BIG-bench (Temporal Sequences) | Chinchilla-70B (few-shot, k=5) | Accuracy | 32.0 | # 5 | ||
| Common Sense Reasoning | BIG-bench (Winowhy) | Chinchilla-70B (few-shot, k=5) | Accuracy | 62.5 | # 2 | ||
| Question Answering | BoolQ | Chinchilla 70B (0-shot) | Accuracy | 83.7 | # 21 | ||
| Sentence Completion | HellaSwag | Chinchilla 70B (0-shot) | Accuracy | 80.8 | # 42 | ||
| Language Modelling | LAMBADA | Chinchilla (Zero-Shot) | Accuracy | 77.7 | # 16 | ||
| Multi-task Language Understanding | MMLU | chatgpt/gpt3.5(20B) | Average (%) | 67.5 | # 22 | ||
| Question Answering | Natural Questions | Chinchilla (few-shot, k=64) | EM | 35.5 | # 28 | ||
| Question Answering | PIQA | Chinchilla 70B (0-shot) | Accuracy | 81.8 | # 25 | ||
| Question Answering | SIQA | Chinchilla (zero-shot) | Accuracy | 51.3 | # 19 | ||
| Common Sense Reasoning | WinoGrande | Chinchilla 70B (0-shot) | Accuracy | 74.9 | # 28 |
Methods
Adam •
Attention Dropout •
BPE •
Chinchilla •
Cosine Annealing •
Dense Connections •
Dropout •
Fixed Factorized Attention •
GELU •
GPT-3 •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Strided Attention •
Weight Decay
