LIMA: Less Is More for Alignment
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervis
Open LLM Leaderboard 2 - a Hugging Face Space by open-llm-leaderboard
Track, rank and evaluate open LLMs and chatbots
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GitHub - dair-ai/ML-Papers-of-the-Week: 🔥Highlighting the top ML papers every week.
https://www.mckinsey.com/jp/~/media/mckinsey/locations/asia/japan/our%20insights/generative%20ai%20is%20here/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business-jp.pdf
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Fine-tuning Alpaca and LLaMA: Training on a Custom Dataset | MLExpert - Crush Your Machine Learning interview
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
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