The article discusses the challenge of detecting hallucinations in Large Language Models (LLMs) and how a model was created at Will to address this issue. The exponential increase in LLM usage poses a new challenge of identifying and preventing hallucinations generated by these models. After observing hallucinations in customer service chat messages, a model was developed to detect these instances. The initial challenge was to curate human data to manually detect hallucinations and understand the different types occurring. The model was built using a database constructed from this curation, converting words into numerical codes using techniques like TF-IDF. By reducing the dimensionality of the messages, clusters of hallucinations were identified, allowing for the classification of hallucinations and non-hallucinations. The KNN model was used for classification, achieving an accuracy of 0.98 and an F1 score of 0.71. The focus was on sensitivity, with a recall metric of 0.98. The model’s predictions can be integrated into chat systems to assess the probability of a message being a hallucination. The results demonstrate the effectiveness of the hallucination detection model. The article concludes by inviting readers to share their experiences with hallucinations and how they are handling them.
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Source link: https://medium.com/tech-will/detectando-alucina%C3%A7%C3%B5es-ae5c7499d03d?source=rss——large_language_models-5
Uncovering hallucinations: Fátima’s story. #MentalHealthAwareness
