arXiv:2606.02289v1 Announce Type: new Abstract: Existing hallucination taxonomies classify LLM errors by what is wrong with the output -- memorised misconceptions, reasoning failures, fluent fabrications. These taxonomies are useful for diagnosis but cannot answer a different question: which uncertainty scorer would have caught this error? We propose a complementary taxonomy that classifies errors by their detectability signature -- the signal a scorer family would read. The DECK taxonomy is a 2x2 partition along inter-sample consistency and token-level confidence into four behavioural regimes
The rapid advancement and deployment of LLMs, coupled with persistent hallucination challenges, necessitate more sophisticated diagnostic and mitigation tools.
This new taxonomy provides a framework for understanding and addressing LLM hallucinations based on their detectability, which is crucial for building reliable and trustworthy AI systems.
The approach to classifying and, by extension, mitigating LLM hallucinations shifts from solely focusing on the 'what' of an error to the 'how' a scoring system could identify it.
- · AI developers
- · AI safety researchers
- · Robust AI platforms
- · Enterprise AI adopters
- · Undifferentiated LLM providers
- · Systems highly prone to undetectable hallucinations
Improved methods for detecting and reducing LLM hallucinations will emerge, leading to more robust models.
Increased trust in LLM applications across critical domains as models become more reliable and errors are better understood.
Accelerated adoption of AI in sensitive applications where hallucination risk has previously been a major barrier, potentially altering market dynamics.
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Read at arXiv cs.CL