Adapting language models to human preferences for better alignment. #NLP

Domain Adaptation of Large Language Models and Aligning to Human Preferences | by Mitul Tiwari | Jun, 2024

The content discusses the advancement of open-source large language models (LLMs) like Mistral and the need for domain-specific adaptation to improve task accuracy. Four key techniques for adapting LLMs to specific domains are outlined: prompt tuning, retrieval augmented generation, supervised fine-tuning, and alignment for human preference, with a focus on direct preference optimization (DPO). The post delves into the details of DPO and its implementation, highlighting its effectiveness in reducing toxicity in AI models. Experimental results show a 48% reduction in toxicity with DPO training, emphasizing its potential to align AI models with human preference and enhance safety in AI interactions. The discussion also emphasizes the importance of domain-specific adaptation in improving LLMs’ capabilities and applications in artificial intelligence. The post concludes by highlighting the transformative impact of DPO and its variants, such as IPO and KTO, in creating more responsible and user-friendly AI models and systems.

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