Frequently Asked Questions
What is GPT-4?
What are Large Language Models (LLMs)?
Large Language Models (LLMs) refer to advanced AI models that are trained on vast amounts of text data to understand and generate human-like language. They use deep learning techniques, particularly transformer architectures, to process and generate natural language. LLMs, such as OpenAI’s GPT-4, have the capability to comprehend and generate text in a wide range of topics and styles. These models have been trained on diverse sources like books, articles, and websites, enabling them to provide answers, generate creative content, assist with language translation, simulate human conversation, and perform various other language-related tasks. LLMs have been a breakthrough in the field of natural language processing and have found applications in various domains, including customer service, content generation, language translation, and more. Learn more about LLMs.
What is explainability in Large Language Models (LLMs)?
Why are enterprises hesitant to implement GPT and other large language models (LLMs)?
Enterprises are hesitant to implement GPT and other large language models (LLMs) for several reasons. Firstly, LLMs may not meet the precision and accuracy requirements of sensitive industries like healthcare and government. They may not consistently provide correct answers, making them unsuitable for critical tasks. Secondly, the lack of explainability in LLMs poses a challenge. Enterprises need transparency in the reasoning behind the model’s outputs, but LLMs lack visibility and explainability, hindering effective debugging and tracing of inaccuracies.
Moreover, concerns about security and predictability arise with LLMs. They can be manipulated by biases in training data or user prompts, leading to potential risks and liabilities, especially in data-sensitive industries. Additionally, the limited adaptability of LLMs to various channels beyond chat interfaces hinders omnichannel deployment, a priority for many enterprises. Lastly, enterprises require complex actions and integrations, which LLMs may not support adequately. Achieving end-to-end automation and proactive behavior often necessitates additional capabilities beyond what LLMs can offer. These considerations push enterprises to seek alternative solutions that better align with their specific needs.
