Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States
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Computer Science > Computation and Language
Title:Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States
Abstract:Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations. We introduce Prompt Embedding Probes (PEP), a white-box method for answer-level hallucination detection from the hidden states of a frozen LLM. PEP extends standard linear probes by augmenting the input with a small number of learnable prompt embeddings. We evaluate PEP on TriviaQA, GSM8K, and MedQA using Qwen3 models at multiple scales. PEP improves hidden-state-based detection over standard linear probes in the main in-distribution setting. We further evaluate PEP for pre-generation prediction, cross-model transfer, and out-of-distribution generalization. PEP remains effective in the pre-generation and cross-model settings, whereas robust cross-dataset transfer remains difficult. These results show that prompt-based adaptation can strengthen hidden-state probing while keeping the backbone frozen and adding only a small number of trainable parameters.
| Comments: | 10 pages, 7 figures. Code available at this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.08024 [cs.CL] |
| (or arXiv:2608.08024v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.08024
arXiv-issued DOI via DataCite (pending registration)
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