{"slug": "prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states", "title": "Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States", "summary": "Researchers introduced Prompt Embedding Probes (PEP), a white-box method for answer-level hallucination detection in large language models that augments standard linear probes with learnable prompt embeddings. Evaluated on TriviaQA, GSM8K, and MedQA using Qwen3 models at multiple scales, PEP improved hidden-state-based detection over standard linear probes in the main in-distribution setting, and remained effective in pre-generation and cross-model settings, though robust cross-dataset transfer remains difficult.", "body_md": "arXiv:2608.08024v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states", "canonical_source": "https://arxiv.org/abs/2608.08024", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:10:43.843703+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["Prompt Embedding Probes", "TriviaQA", "GSM8K", "MedQA", "Qwen3"], "alternates": {"html": "https://wpnews.pro/news/prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states", "markdown": "https://wpnews.pro/news/prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states.md", "text": "https://wpnews.pro/news/prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states.txt", "jsonld": "https://wpnews.pro/news/prompt-embedding-probes-pep-hallucination-detection-in-llms-from-hidden-states.jsonld"}}