When Autocomplete Lies: An Evaluation on LLM Hallucination

Large Language Models (LLMs) have changed how we process natural language. Still, they have a major problem: hallucination. This means they can create believable text that is wrong or not based on evidence. This paper looks at this issue by sorting hallucinations into factual, logical, intrinsic, and extrinsic types. We look at the reasons why this happens. These reasons include the statistics used to train these models, weaknesses that allow data poisoning, and how tests reward guessing instead of showing uncertainty. The paper looks at ways to spot hallucinations, such as self-checking, checking against known information, and using neuro-symbolic methods. It also reviews ways to reduce hallucinations, including using retrieval-augmented generation, prompt engineering, parameter-efficient fine-tuning, and specific decoding techniques. Our study shows that while theory says we can't completely get rid of hallucinations, we can greatly reduce them by using many methods together. This will help create more reliable AI systems.

کلیدواژه ها:

Large Language Models (LLMs) ، Hallucination Detection ، Retrieval-Augmented Generation ، Factuality Verification ، Model Trustworthiness ، Mitigation Strategies