Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale
arXiv:2607.07670v1 Announce Type: new Abstract: Large language models hallucinate most about entities they have never seen. We ask whether a model's activations betray entity familiarity before a single answer token is generated, and whether that signal predicts the factual reliability of the answers. On four Polish Bielik models (1.5B-11B parameters), we probe four entity domains (athletes, cities, writers, musicians), each with 42 well-known, 42 obscure-but-real, and 42 fabricated entities addressed by a one-sentence question (504 prompts per model). Two unsupervised, single-forward-pass dis
The proliferation of advanced LLMs necessitates deeper understanding of their internal mechanisms, especially regarding factual accuracy and unfamiliarity detection, to mitigate hallucination risks.
For strategic readers, understanding how models 'know what they don't know' is crucial for deploying reliable AI, particularly in sensitive enterprise or national security applications.
This research provides a potential method to programmatically identify and flag when an LLM is likely to hallucinate due to unfamiliarity, thereby improving trustworthiness and reducing deployment risks.
- · AI developers
- · Enterprises deploying LLMs
- · AI safety researchers
- · Users relying on unverified LLM outputs
- · AI platforms with poor hallucination controls
Improved reliability and reduced hallucination rates in large language models.
Accelerated adoption of LLMs in high-stakes domains where factual accuracy is paramount, such as finance or intelligence.
Development of new AI architectures specifically designed to externalize 'knowledge state' for real-time human or system intervention, leading to hybrid human-AI intelligence systems.
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