The Self-Feedback framework for improving internal consistency in Large Language Models operates in three steps: (1) Self-Evaluation, which evaluates the model's internal consistency based on language expressions, decoding layer probability distributions, and hidden states; (2) Internal Consistency Signal, which derives numerical, textual, external, or comparative signals from the evaluation; and (3) Self-Update, which uses these signals to update the model's expressions or the model itself.

Authors

• Person: Not available Organization: GitHub
  EdinburghNLP/awesome-hallucination-detection - GitHub

Sources

• EdinburghNLP/awesome-hallucination-detection - GitHub github.com GitHub via serper

Referenced by nodes (2)

• Large Language Models concept
• LLM-as-a-judge concept