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What challenges do language model systems face?
Language model systems face challenges such as data quality, tokenization limitations, resource-intensive fine-tuning, potential biases, and ensuring outputs align with human values. These challenges require careful consideration and mitigation to ensure the effectiveness and reliability of language models in various applications.
How long do Google and Meta refine their models?
Google and Meta spend twelve to eighteen months refining their language model systems, iteratively improving them through supervised fine-tuning, alignment with human tastes, and periodic re-tuning to address data drift and other issues, striving to reach a specific quality threshold.
What steps are involved in refining language models?
Refining language models involves several steps, including supervised fine-tuning, alignment with human preferences through techniques like Reinforcement Learning from Human Feedback (RLHF), weight pruning and quantization, and iterative refinement for continuous improvement2. These steps help in enhancing the model's performance, reducing its size, and making it more aligned with human-like responses.