What is Hallucination and How to Deal with It (April 2026)

English summary

This material defines and classifies hallucination and sycophancy in large language models, and outlines their training- and inference-related causes, detection methods, and user-side mitigation strategies. Hallucination is described as presenting information as fact when it is inaccurate or inconsistent with reality, the input, the context, the instructions, or the reasoning. Sycophancy is the tendency to prioritize agreement with a user's views or premises over accuracy and objective judgment.

Key points

• Hallucinations are divided into factuality and faithfulness hallucinations. Factuality hallucinations include direct contradiction of real-world facts and fabrication of nonexistent entities or events; faithfulness hallucinations include instruction, context, and logical inconsistencies.
• Next-token prediction leads models to generate probable tokens from learned text patterns rather than retrieve answers from a factual database. Incomplete, biased, noisy, or outdated data, missing private or specialized knowledge, retrieval and attention failures, and stochastic decoding errors are presented as contributing causes.
• Reinforcement learning from human feedback and instruction tuning may favor polite, confident, plausible responses. When evaluation treats incorrect answers and refusals or abstentions alike, models may be encouraged to guess rather than state that they do not know.

Audience

AI practitioners, software developers, implementers of retrieval-augmented generation systems, students, researchers, and end users seeking to interact with generative language models critically and safely.

Scope and limitations

The material is an overview based on the cited literature and presentation evidence notes. Prompting for abstention and premise checking can reduce but cannot eliminate hallucination risk, while self-consistency and multi-model checks require additional computation and token usage. It also notes that next-token prediction lacks an inherent truth-verification mechanism. This summary does not independently verify the methods, samples, effect sizes, or results of the cited studies.

Research context

The topic connects thematically with Sangzin Ahn's portfolio through medical AI and data science, particularly the reliability evaluation of generative models and evidence-based interaction with them.