Language models, both large and small, are emerging thick and fast. Models like GPT-4 and Claude are among the leading large language models (LLMs) built for deep reasoning and complex problem-solving. On the other hand, smaller language models (SLMs) such as Mistral and Phi are gaining traction for their efficiency, faster processing, and cost-effectiveness in domain-specific applications.
Yet, whether in LLM or a fine-tuned SLM, hallucination remains a key challenge in ensuring AI models are accurate, trustworthy, and adaptable for real-world applications.
Firstly, what is hallucination?
It is either false or misleading information generated by a language model that’s not based on factual data or real-world context. This occurs because, unlike humans, language models lack the self-awareness to acknowledge they don’t have sufficient information about a topic and must avoid making things up.
Our blog offers a deep dive into LLM hallucinations and, more importantly, practical strategies to generate contextual, predictable outputs.
Read the full article: https://www.haptik.ai/blog/solutions-to-reduce-hallucinations-in-llm
