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    <abstract>Large language model (LLM) hallucinations, meaning fluent but factually incorrect generations, fall into two types: faithfulness violations, where the model misuses provided context, and factuality violations, where answers reflect errors in internal knowledge. Proper mitigation depends on knowing which source drives each answer. We study contributive attribution, i.e. the classification of the dominant knowledge source behind each output, and show that a simple linear probe trained on hidden representations can reliably identify it. We introduce AttriWiki, a self-supervised pipeline that automatically generates labelled training data by prompting models to recall withheld entities from memory or read them from context without relying on knowledge conflicts. Probes trained on AttriWiki achieve up to 0.96 Macro-F1 on Llama-3.1-8B, Mistral-7B, and Qwen2.5-7B, transfer to SQuAD and WebQuestions with 0.92-0.99 accuracy, and generalize zero-shot to Tighidet et al. (2024)’s benchmark, outperforming their probe on conflicting settings without retraining. Furthermore, attribution mismatches raise error rates by up to 70%, though correct attribution does not guarantee correct answers, pointing to the need for broader detection frameworks.</abstract>
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%0 Conference Proceedings
%T Probing for Knowledge Attribution in Large Language Models
%A Brink, Ivo
%A Boer, Alexander
%A Ulmer, Dennis
%Y Arad, Dana
%Y Belinkov, Yonatan
%Y Chen, Hanjie
%Y Feldhus, Nils
%Y Mueller, Aaron
%Y Mohebbi, Hosein
%Y Sarti, Gabriele
%Y Tutek, Martin
%S Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-476-7
%F brink-etal-2026-probing
%X Large language model (LLM) hallucinations, meaning fluent but factually incorrect generations, fall into two types: faithfulness violations, where the model misuses provided context, and factuality violations, where answers reflect errors in internal knowledge. Proper mitigation depends on knowing which source drives each answer. We study contributive attribution, i.e. the classification of the dominant knowledge source behind each output, and show that a simple linear probe trained on hidden representations can reliably identify it. We introduce AttriWiki, a self-supervised pipeline that automatically generates labelled training data by prompting models to recall withheld entities from memory or read them from context without relying on knowledge conflicts. Probes trained on AttriWiki achieve up to 0.96 Macro-F1 on Llama-3.1-8B, Mistral-7B, and Qwen2.5-7B, transfer to SQuAD and WebQuestions with 0.92-0.99 accuracy, and generalize zero-shot to Tighidet et al. (2024)’s benchmark, outperforming their probe on conflicting settings without retraining. Furthermore, attribution mismatches raise error rates by up to 70%, though correct attribution does not guarantee correct answers, pointing to the need for broader detection frameworks.
%U https://aclanthology.org/2026.blackboxnlp-1.17/
%P 269-293

Markdown (Informal)

[Probing for Knowledge Attribution in Large Language Models](https://aclanthology.org/2026.blackboxnlp-1.17/) (Brink et al., BlackboxNLP 2026)

• Probing for Knowledge Attribution in Large Language Models (Brink et al., BlackboxNLP 2026)

ACL

• Ivo Brink, Alexander Boer, and Dennis Ulmer. 2026. Probing for Knowledge Attribution in Large Language Models. In Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 269–293, Budapest, Hungary. Association for Computational Linguistics.