arXiv:2507.06722v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations. While several studies have shown that models encode uncertainty in their hidden states, it is underexplored how this affects the way they process such hidden states. In this work, we demonstrate that the dynamics of output token probabilities across layers for certain and uncertain outputs are largely aligned, revealing that uncertainty does not seem to affect inference dyna
This research is emerging as the field of large language models rapidly develops, emphasizing the need for robust methods to understand and control model behavior, particularly regarding uncertainty and hallucination.
Understanding LLM internal dynamics of uncertainty is crucial for building more reliable AI systems and for developing better techniques to detect and mitigate problematic outputs like hallucinations.
This research suggests that uncertainty in LLMs does not significantly alter the inference dynamics across layers, which may simplify the search for common mechanisms governing both certain and uncertain predictions.
- · AI researchers
- · Developers of LLM safety tools
- · Users of LLMs
- · Approaches relying on divergent layer-wise dynamics for uncertainty detection
Researchers gain new insights into the internal workings of large language models concerning uncertainty.
Improved understanding could lead to more effective methods for detecting and preventing AI hallucinations.
More reliable LLMs could accelerate their deployment in critical applications, increasing societal reliance on AI systems.
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Read at arXiv cs.LG