Insights into Classifying and Mitigating LLMs' Hallucinations
Alessandro Bruno Pier Luigi Mazzeo Aladine Chetouani Marouane Tliba Mohamed Amine Kerkouri
The widespread adoption of large language models (LLMs) across diverse AI
applications is proof of the outstanding achievements obtained in several
tasks, such as text mining, text generation, and question answering. However,
LLMs are not exempt from drawbacks. One of the most concerning aspects regards
the emerging problematic phenomena known as "Hallucinations". They manifest in
text generation systems, particularly in question-answering systems reliant on
LLMs, potentially resulting in false or misleading information propagation.
This paper delves into the underlying causes of AI hallucination and elucidates
its significance in artificial intelligence. In particular, Hallucination
classification is tackled over several tasks (Machine Translation, Question and
Answer, Dialog Systems, Summarisation Systems, Knowledge Graph with LLMs, and
Visual Question Answer). Additionally, we explore potential strategies to
mitigate hallucinations, aiming to enhance the overall reliability of LLMs. Our
research addresses this critical issue within the HeReFaNMi (Health-Related
Fake News Mitigation) project, generously supported by NGI Search, dedicated to
combating Health-Related Fake News dissemination on the Internet. This
endeavour represents a concerted effort to safeguard the integrity of
information dissemination in an age of evolving AI technologies.