halucination

If you mean articles about AI/LLM hallucinations, here are some useful ones, from beginner-friendly to research-level:

1. OpenAI — Why Language Models Hallucinate A very useful explanation of why LLMs produce confident but false answers. It explains the connection between next-token prediction, uncertainty, training, and evaluation. Read: Why Language Models Hallucinate — OpenAI

2. IBM — What Are AI Hallucinations? Good for understanding the concept from a practical perspective, including examples such as invented facts, fake studies, nonexistent URLs, and incorrect details. The article was updated in July 2026. Read: What Are AI Hallucinations? — IBM

3. NIST — Generative AI Profile NIST uses the term “confabulation” for AI systems confidently generating false or erroneous information. It also explains why this can naturally occur from statistical generation. Read: NIST Generative AI Profile

4. Baeldung — Hallucinations in Large Language Models A more approachable technical article covering: What hallucinations are Why they happen Training-data problems Probabilistic generation Grounding RAG Mitigation techniques Read: Hallucinations in LLMs — Baeldung

5. Comprehensive Survey — Causes, Detection & Mitigation If you're learning this seriously for AI development, this is a good research survey. It covers hallucination types, causes, detection methods, mitigation techniques, benchmarks, and evaluation metrics. Read: Large Language Models Hallucination — Comprehensive Survey 6. ACM Survey — Principles, Taxonomy, Challenges & Open Questions This research survey organizes hallucination causes into data, training, and inference, and discusses techniques such as RAG, data filtering, model editing, and other mitigation approaches. Read: A Survey on Hallucination in Large Language Models — ACM 7. 2026 Survey — Understanding Hallucinations in Large Visual and Language Models This is particularly interesting if you're interested in multimodal AI, because it covers hallucinations in both language and vision-language models. Published in ACM Computing Surveys in June 2026. Read: Understanding Hallucinations in Large Visual and Language Models For your learning path, I'd read them in this order: IBM → OpenAI → NIST → Baeldung → ACM survey → 2026 multimodal survey