Efficient Continual Pre-training by Mitigating the Stability Gap
Continual pre-training has increasingly become the predominant approach for
adapting Large Language Models (LLMs) to new domains. This process involves
updating the pre-trained LLM with a corpus from a new domain, resulting in a
shift in the training distribution. To study the behavior of LLMs during this
shift, we measured the model's performance throughout the continual
pre-training process. we observed a temporary performance drop at the
beginning, followed by a recovery phase, a phenomenon known as the "stability
gap," previously noted in vision models classifying new classes. To address
this issue and enhance LLM performance within a fixed compute budget, we
propose three effective strategies: (1) Continually pre-training the LLM on a
subset with a proper size for multiple epochs, resulting in faster performance
recovery than pre-training the LLM on a large corpus in a single epoch; (2)
Pre-training the LLM only on high-quality sub-corpus, which rapidly boosts
domain performance; and (3) Using a data mixture similar to the pre-training
data to reduce distribution gap. We conduct various experiments on Llama-family
models to validate the effectiveness of our strategies in both medical
continual pre-training and instruction tuning. For example, our strategies
improve the average medical task performance of the OpenLlama-3B model from
36.2% to 40.7% with only 40% of the original training budget and enhance the
average general task performance without causing forgetting. Furthermore, we
apply our strategies to the Llama-3-8B model. The resulting model,
Llama-3-Physician, achieves the best medical performance among current
open-source models, and performs comparably to or even better than GPT-4 on
several medical benchmarks. We release our models at
https://huggingface.co/YiDuo1999/Llama-3-Physician-8B-Instruct.
667a35b89f78b2d01bd1b5b7
Yiduo Guo
admin_assigned
2024-06-25T08:28:43.723Z
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Guo
user
YiDuo1999
667a35b89f78b2d01bd1b5b8
Jie Fu
claimed_verified
2024-06-25T08:58:55.208Z
641a6895fb5ffff5ac79d593
https://cdn-avatars.huggingface.co/v1/production/uploads/641a6895fb5ffff5ac79d593/vxvwsto3llOEWGqQKGMYx.jpeg
Jie Fu
user
bigaidream
667a35b89f78b2d01bd1b5b9
Huishuai Zhang
667a35b89f78b2d01bd1b5ba
Dongyan Zhao
667a35b89f78b2d01bd1b5bb
Yikang Shen
extracted_confirmed
2024-06-25T18:40:38.691Z
6463995939359568c63d740c
/avatars/11caacefc68c1259dbc8764cc4340481.svg
Yikang Shen
user
YikangS
Efficient Continual Pre-training by Mitigating the Stability Gap
Continual pre-training has increasingly become the predominant approach for
adapting Large Language Models (LLMs) to new domains. This process involves
updating the pre-trained LLM with a corpus from a new domain, resulting in a
shift in the training distribution. To study the behavior of LLMs during this
shift, we measured the model's performance throughout the continual
pre-training process. we observed a temporary performance drop at the
beginning, followed by a recovery phase, a phenomenon known as the "stability
gap," previously noted in vision models classifying new classes. To address
this issue and enhance LLM performance within a fixed compute budget, we
propose three effective strategies: (1) Continually pre-training the LLM on a
subset with a proper size for multiple epochs, resulting in faster performance
recovery than pre-training the LLM on a large corpus in a single epoch; (2)
Pre-training the LLM only on high-quality sub-corpus, which rapidly boosts
domain performance; and (3) Using a data mixture similar to the pre-training
data to reduce distribution gap. We conduct various experiments on Llama-family
models to validate the effectiveness of our strategies in both medical
continual pre-training and instruction tuning. For example, our strategies
improve the average medical task performance of the OpenLlama-3B model from
36.2% to 40.7% with only 40% of the original training budget and enhance the
average general task performance without causing forgetting. Furthermore, we
apply our strategies to the Llama-3-8B model. The resulting model,
Llama-3-Physician, achieves the best medical performance among current
open-source models, and performs comparably to or even better than GPT-4 on
several medical benchmarks. We release our models at
https://huggingface.co/YiDuo1999/Llama-3-Physician-8B-Instruct.
Strategies for continual pre-training of large language models reduce performance drops and computational costs during adaptation to new domains.
Large Language Models continual pre-training stability gap vision models medical task performance OpenLlama-3B Llama-3-8B Llama-3-Physician GPT-4 medical benchmarks