AI Hallucinations
A phenomenon in large-language-models (LLMs) where models generate plausible but factually incorrect information, often appearing coherent and reasonable to users.
Key Points
- Not a minor bug but a widespread issue affecting most LLMs during generation
- Occurs when models produce outputs that are contextually consistent but factually inaccurate
- Root cause lies in probabilistic nature of training data and lack of real-world grounding
- Common in complex reasoning, historical facts, and technical domains
- Recent analysis (via Gemini 2.5 Flash summary) emphasizes the pervasive nature of these errors in large-language-models
Related Concepts
- large-language-models
- Model Reliability
- Fact-Checking
- AI Ethics
- prompt-engineering
References
- 2026-04-14 2026 04 14 AI Hallicinations
- 2026-04-14 AI Hallicinations
- watch?v=1ONwQzauqkc%7CYouTube Summary Report (2026-03-06)
Source Notes
- 2026-04-23: * * * title: YouTube Summary Report date: 2026-03-06 source_type: youtube_summary api: Gemini 2.5 Flash modes: Summary, Key Timestamps # YouTube Summary Report Generated: 2026-03-06 · API: Gemini 2.5 Flash · Modes: Summary, Key Timestamps * * * Video 1 URL: https://ww (YouTube Summary Report)
- 2026-03-06: YouTube Summary Report regarding the pervasive issue of hallucinations in large-language-models via Gemini 2.5 Flash.