Paper Review: LLaMA: Open and Efficient Foundation Language Models
LLaMA is a collection of large foundation language models, ranging from 7B to 65B parameters, that have been trained on trillions of tokens using publicly available datasets. The LLaMA-13B model outperforms GPT-3 (175B) on most benchmarks, and the LLaMA-65B model is competitive with other state-of-the-art models, such as Chinchilla70B and PaLM-540B. This suggests that it is possible to achieve excellent performance in language modeling without relying on proprietary or inaccessible datasets.
The authors use only publicly available data, so the following datasets are used: English CommonCrawl, C4, Github, Wikipedia, Gutenberg and Books3, ArXiv, Stack Exchange.
They use BPE as a tokenizer. The whole training dataset has ~1.4T tokens. Most of the tokens are used only once, with the exception of Wikipedia and books, which are used twice.
The authors use the original transformer architecture from the paper “Attention is All you Need” with the following changes:
- pre-normalization with RMSNorm instead of output normalization;
- SwiGLU activation function from PaLM. The dimension is
2/3 * 4dinstead of
4das in PaLM;
- Rotary Embeddings from GPTNeo instead of positional embeddings
- AdamW, cosine learning scheduler.
- Efficient implementation of the causal multi-head attention;
- Reducing the number of activations that are recomputed during the backward pass with checkpointing;
They trained the model on 2048 A100 for 21 days.
- Common Sense Reasoning: LLaMA-65B outperforms Chinchilla-70B on all reported benchmarks but BoolQ. LLaMA-13B model also outperforms GPT-3 on most benchmarks despite being 10× smaller;
- Closed-book Question Answering: LLaMA-65B achieves state-of-the-art performance in the zero-shot and few-shot settings. LLaMA-13B is also competitive with GPT-3 and Chinchilla, despite being 5-10× smaller;
- Reading Comprehension: LLaMA-65B is competitive with PaLM-540B, LLaMA-13B outperforms GPT-3;
- Mathematical reasoning: On GSM8k, LLaMA65B outperforms Minerva-62B, although it has not been fine-tuned on mathematical data;
- Code generation: LLaMA with 13B parameters and more outperforms LaMDA 137B. LLaMA 65B outperforms PaLM 62B;
- Massive Multitask Language Understanding: LLaMA-65B is behind both Chinchilla70B and PaLM-540B by a few percent in average, and across most domains;
- briefly finetuning on instructions data leads to improvements on MMLU;