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Large Language Models (LLMs) such as GPT-4 are highly proficient in text generation tasks including summarization and question answering. However, a common problem is their tendency to generate “hallucinations,” which refers to the production of factually incorrect or contextually irrelevant content. This problem becomes critical when it occurs despite the LLMs being given correct facts,…

Large language models (LLMs) such as GPT-4 have shown impressive capabilities in generating text for summarization and question answering tasks. But these models often “hallucinate,” or produce content that is either contextually irrelevant or factually incorrect. This is particularly concerning in applications where accuracy is crucial, such as document-based question answering and summarization, and where…

Transformer-based Large Language Models (LLMs) like ChatGPT and LLaMA are highly effective in tasks requiring specialized knowledge and complex reasoning. However, their massive computational and storage requirements present significant challenges in wider applications. One solution to this problem is quantization, a method that converts 32-bit parameters into smaller bit sizes, which greatly improves storage efficiency…