Quantifying Self-Awareness of Knowledge in Large Language Models

• URL: http://arxiv.org/abs/2509.15339v1
• Date: Thu, 18 Sep 2025 18:29:14 GMT
• Title: Quantifying Self-Awareness of Knowledge in Large Language Models
• Abstract summary: Hallucination prediction in large language models is often interpreted as a sign of self-awareness.<n>We argue that such performance can arise from question-side shortcuts rather than true model-side introspection.<n>We introduce SCAO, a method that enhances the use of model-side signals.
• Score: 19.886013407039517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucination prediction in large language models (LLMs) is often interpreted as a sign of self-awareness. However, we argue that such performance can arise from question-side shortcuts rather than true model-side introspection. To disentangle these factors, we propose the Approximate Question-side Effect (AQE), which quantifies the contribution of question-awareness. Our analysis across multiple datasets reveals that much of the reported success stems from exploiting superficial patterns in questions. We further introduce SCAO (Semantic Compression by Answering in One word), a method that enhances the use of model-side signals. Experiments show that SCAO achieves strong and consistent performance, particularly in settings with reduced question-side cues, highlighting its effectiveness in fostering genuine self-awareness in LLMs.

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