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Researchers are exploring efficient strategies to continually pre-train large language models (LLMs) to avoid starting the training process from scratch when new data becomes available. These strategies aim to achieve re-training results with less compute and exhibit human-like behaviors in various tasks. The use of scaling laws as guides for developing LLMs is also being studied to bridge the gaps between current scaling studies and the actual training and evaluation processes.
Here is my selection of papers for today (14 Mar) on Hugging Face Scaling Up Dynamic Human-Scene Interaction Modeling Language models scale reliably with over-training and on downstream tasks Simple and Scalable Strategies to Continually Pre-train Large Language Models https://t.co/U6f7sFiiKR
[LG] Simple and Scalable Strategies to Continually Pre-train Large Language Models A Ibrahim, B Thérien, K Gupta, M L. Richter… [Université de Montréal] (2024) https://t.co/NQSdbJhafC - Simple and scalable continual learning strategies can be used to efficiently update large… https://t.co/I8VxINcXj6
[LG] Simple and Scalable Strategies to Continually Pre-train Large Language Models A Ibrahim, B Thérien, K Gupta, M L. Richter… [Université de Montréal] (2024) https://t.co/NQSdbJhafC https://t.co/cQPsf2yrr4