A lightweight classifier method for hallucination detection conditions on input hidden states before text generation and intervenes in these states to steer Large Language Models toward factual outputs, resulting in consistent improvements in factual accuracy with minimal computational overhead. This method uses Accuracy as a metric and is evaluated on the NQ-Open, MMLU, MedMCQA, and GSM8K datasets.

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 (3)

• Large Language Models concept
• NQ-Open concept
• MEDMCQA concept