So sánh phương pháp

	Phát hiện ảo giác	Nhận dạng thực thể có tên (NER)
Lĩnh vực	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2020 (faithfulness framing); 2023 (SelfCheckGPT)	—
Người khởi xướng	Established as a formal task by Maynez et al. (2020); SelfCheckGPT zero-resource variant by Manakul et al. (2023)	—
Loại	NLP evaluation / quality-assurance pipeline	NLP sequence-labelling task
Công trình gốc	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.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Tên gọi khác	factual consistency checking, faithfulness evaluation, LLM output verification, Hallüsinasyon Tespiti (Factual Consistency)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Liên quan	5	3
Tóm tắt	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.