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Less Is More for Alignment (LIMA) is a new research paper from Meta AI, which proposes a new approach to alignment of large language models. The researchers define the Superficial Alignment Hypothesis, which claims that a model's knowledge and capabilities are almost entirely learnt in the pretraining stage. To prove that, they fine-tune a 65B params LLaMa language model with only 1,000 carefully curated prompts and responses, and show it can produce remarkable results without any reinforcement learning. Whether you're a seasoned researcher or just getting started in the field, this video is sure to provide valuable insights into this exciting development in NLP. If you enjoy this video then please subscribe to the channel and hit the like button to support the creation of more similar educational content. LIMA paper on arxiv - https://arxiv.org/abs/2305.11206 Chapters: 0:00 Introducing LIMA 0:32 LLM Training Stages 2:14 Superficial Alignment Hypothesis 3:28 Small Curated Dataset 4:11 LIMA Results Analysis