The trustworthiness may be greatly impacted by two key threats: data hunger and data poisoning
A Large Language Model that is given an inadequate quantity of data or data that is deficient in variety may display limits in its capabilities
LLM tools that are confined in their data diet have difficulty understanding subtle topics and may misread sophisticated inquiries at times
The LLM tools will reflect that bias in its replies, which might possibly produce to outputs that promote discrimination or offensiveness
The manipulation of the LLM’s results in order to cater to a certain agenda might have devastating effects
Extensive volumes of high-quality data derived from a wide variety of sources are essential for LLMs
Users become reluctant to interact with AI-powered services because they are unable to depend on the knowledge that is provided by Large Language Models