Into the Unknown: Self-Learning Large Language Models
CoRR(2024)
摘要
We address the main problem of self-learning LLM: the question of what to
learn. We propose a self-learning LLM framework that enables an LLM to
independently learn previously unknown knowledge through self-assessment of
their own hallucinations. Using the hallucination score, we introduce a new
concept of Points in The Unknown (PiUs), along with one extrinsic and three
intrinsic methods for automatic PiUs identification. It facilitates the
creation of a self-learning loop that focuses exclusively on the knowledge gap
in Points in The Unknown, resulting in a reduced hallucination score. We also
developed evaluation metrics for gauging an LLM's self-learning capability. Our
experiments revealed that 7B-Mistral models that have been finetuned or aligned
are capable of self-learning considerably well. Our self-learning concept
allows more efficient LLM updates and opens new perspectives for knowledge
exchange. It may also increase public trust in AI.
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