Hallucinations in LLMs: Understanding and Addressing Challenges

Abstract

Large language models (LLM) are trained to understand and generate human-like language. While LLMs present a cutting-edge concept and their use is becoming widespread, hallucinations sometimes occur during their operation. Hallucinations refer to instances where the model generates inaccurate or fictitious information, deviating from factual knowledge and potentially providing responses that lack a basis in model’s training data. In this paper, the ways in which LLMs generate text are examined to address the question of why hallucinations occur. The paper additional explores how existing LLM models can be leveraged to reduce the likelihood of hallucination. Alongside exploring hallucinations, this paper provides insights into the algorithms used for training LLMs, offering a clear picture of the text generation process and its effective utilization.

Topics & Concepts