Compara mètodes

	Detecció d'al·lucinacions	Classificació de text
Camp	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline
Any d'origen	2020 (faithfulness framing); 2023 (SelfCheckGPT)	—
Autor original	Established as a formal task by Maynez et al. (2020); SelfCheckGPT zero-resource variant by Manakul et al. (2023)	—
Tipus	NLP evaluation / quality-assurance pipeline	Supervised NLP classification task
Font seminal	Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906-1919.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Àlies	factual consistency checking, faithfulness evaluation, LLM output verification, Hallüsinasyon Tespiti (Factual Consistency)	text categorization, document classification, topic classification, metin sınıflandırma
Relacionats	5	4
Resum	Hallucination detection is a natural-language-processing pipeline that measures whether the output of a language model is consistent with a reference source document or with verifiable facts. Formalised as a faithfulness evaluation task by Maynez et al. (2020) and extended to a zero-resource black-box setting by Manakul et al. (2023) with SelfCheckGPT, the approach is used to flag unreliable LLM outputs in high-stakes domains such as medicine, law, and journalism.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.