aiqbench analysis
This research introduces NeuroActiSep, a method that identifies specific feed-forward neurons in large language models to detect factual hallucinations efficiently in a single pass. By transferring neuron identities across datasets, the study demonstrates that these targeted probes achieve detection performance comparable to those using broader internal state representations. This work advances interpretability and reliability by offering a streamlined approach to monitoring model truthfulness without requiring extensive computational overhead.
NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass
arXiv:2609.14448v1 Announce Type: cross Abstract: Hallucination in large language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to detect patterns of truthfulness and factuality. A less-studied approach is to identify feed-forward neurons correlated with hallucination. We propose a method to rank feed-forward neurons at the final prompt token using a custom neuron selection dataset. We transfer the selected neuron identities to train hallucination classifiers on other factual question answering datasets. Our work provides empirical evidence that probes trained using the features from the selected neurons perform on par with probes trained on internal states. We also analyze the distribution of selected neurons and the effect of layer depth on detection performance.
Abstract:Hallucination in large language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to detect patterns of truthfulness and factuality. A less-studied approach is to identify feed-forward neurons correlated with hallucination. We propose a method to rank feed-forward neurons at the final prompt token using a custom neuron selection dataset. We transfer the selected neuron identities to train hallucination classifiers on other factual question answering datasets. Our work provides empirical evidence that probes trained using the features from the selected neurons perform on par with probes trained on internal states. We also analyze the distribution of selected neurons and the effect of layer depth on detection performance.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.14448 [cs.CL]
(or arXiv:2609.14448v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.14448 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 213 words
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