Large language models (LLMs) are advanced AI-based dialogue systems that can answer user queries and generate convincing texts following human instructions. After the advent of ChatGPT, the highly performing model developed by OpenAI, these models have become increasingly popular, and more companies are now investing in their development.
Despite their promise for answering human questions in real-time and creating texts for specific purposes, LLMs can sometimes generate nonsensical, inaccurate or irrelevant texts that diverge from the prompts that were fed to them by human users. This phenomenon, which is often linked to the limitations of the data used to train the models or mistakes in their underlying reasoning, is referred to as LLM "hallucinations."
Researchers at University of Illinois Urbana-Champaign recently introduced KnowHalu, a framework to detect hallucinations in the text generated by LLMs. This framework, introduced in a paper posted to the preprint server arXiv, could help to improve the reliability of these models and simplify their use for completing various text generation tasks.
"As advancements in LLMs continue, hallucinations emerge as a critical obstacle impeding their broader real-world application," Bo Li, advisor of the project, told Tech Xplore. "Although numerous studies have addressed LLM hallucinations, existing methods often fail to effectively leverage real-world knowledge or utilize it inefficiently.
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