AI Hallucination

Uncertainty of AI (
Epsilon Greedy,
Creativity,
Extrapolation)

For example, hallucinations in robotics models pose significant dangers, unlike language models' hallucinations, which merely provide incorrect information. Mechanistic interpretability provides a promising and explicit method to control AI.

LLMs hallucinate when fine-tuned with new factual knowledge, as they learn new information slower than consistent knowledge

AI Hallucination Notion

Hallucination Benchmark

Extrinsic hallucination

Intrinsic hallucination

Hallucination Mitigation

Multimodal Hallucination

LLMs Will Always Hallucinate, and We Need to Live With This

As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically. This work argues that hallucinations in language models are...

https://arxiv.org/abs/2409.05746

The Internal State of an LLM Knows When It’s Lying

arXiv reCAPTCHA

https://arxiv.org/pdf/2304.13734

arxiv.org

https://arxiv.org/pdf/2310.07064.pdf

Bigger AI chatbots more inclined to spew nonsense

Bigger AI chatbots more inclined to spew nonsense — and people don't always realize

Nature - Artificial-intelligence models are improving overall but are more likely to answer every question, leading to wrong answers.

https://www.nature.com/articles/d41586-024-03137-3

Masking

Retrieval Head or relevant

Induction head could induce hallucinations

arxiv.org

https://arxiv.org/pdf/2410.18860

AI Hallucination

Uncertainty of AI (Epsilon Greedy, Creativity, Extrapolation)

The Internal State of an LLM Knows When It’s Lying

Recommendations

Uncertainty of AI (
Epsilon Greedy,
Creativity,
Extrapolation)