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LLM thought experiment — 4— alignment through interpretability
- LLMs are essentially language pattern statistics that require alignment to specific tasks through human supervision and fine-tuning methods like DPO.
- Fine-tuning, although often effective, is not foolproof and may not always align LLMs with human values as intended.
- Methods like DPO reinforce LLMs to produce specific responses verbatim, akin to teaching a child certain behaviors through repetition.
- Aligning LLMs to human values requires understanding where and how these values are stored in the network, a challenging task.
- Mechanistic interpretability suggests training sparse autoencoders to investigate how changing specific neurons can affect certain concepts within LLMs.
- Finding neurons related to abstract concepts like 'expected human suffering' is possible, but identifying specific ones remains a challenge.
- Proposing a new approach involves identifying neurons with significant influence on specific human values while minimizing influence on contrary values.
- Creating a function f to boost the influence of desired values without affecting undesired ones is crucial for true alignment.
- Deriving the gradient of f enables the creation of an 'injection vector' that can push the network towards aligning with human values.
- Achieving true alignment with human values in LLMs requires a deep exploration of individual and cultural values and injecting these values into the model.
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