Learning Uncertainty from Sequential Internal Dispersion in Large Language Models (2026.acl-long)
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| Challenge: | Recent approaches to detect hallucinations depend on model internal states to estimate uncertainty, but they focus on last or mean tokens. |
| Approach: | They propose a supervised hallucination detection framework that leverages token-wise, layer-wise features derived from hidden states. |
| Outcome: | The proposed framework outperforms baseline models and avoids large training sets. |
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