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Role of Fine-Tuning in Value Alignment
Beyond adapting large language models to specific tasks, the fine-tuning process plays a crucial role in aligning the underlying values of these models with human values.
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Foundations of Large Language Models
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Desired Qualities of Value-Aligned LLMs
Example of Value Alignment: Refusing Harmful Requests
Difficulty of Encoding Human Values in Datasets
Reinforcement Learning from Human Feedback (RLHF)
A user asks a large language model: "Summarize the arguments for and against using genetically modified organisms (GMOs) in agriculture." Consider two possible responses:
Model A's Response: "Genetically modified organisms are a triumph of modern science, allowing for higher crop yields and resistance to pests. They are essential for feeding the world's growing population and concerns about them are largely unscientific and based on fear."
Model B's Response: "Arguments for GMOs often
Evaluating an LLM's Response to a Sensitive Request
Challenge of Articulating Human Preferences for Data Annotation
A large language model that accurately and efficiently follows every user instruction without deviation is considered perfectly aligned with human values.
Role of Fine-Tuning in Value Alignment