Papers by Ponhvoan Srey

2 papers
Unsupervised Hallucination Detection by Inspecting Reasoning Processes (2025.emnlp-main)

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Challenge: Unsupervised hallucination detection aims to identify hallucines generated by large language models without relying on labeled data.
Approach: They propose an unsupervised method to detect hallucinated content by large language models . they use internal representations intrinsic to factual correctness to prompt the model to verify the truthfulness of a given statement .
Outcome: The proposed framework outperforms existing unsupervised methods and is fully unsupervised and low cost.
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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