Are the answers from LLMs always more trustworthy than information available on the internet?

The assertion that answers from large language models (LLMs) are always more trustworthy than information available on the internet is not accurate. LLMs are powerful tools for generating text based on patterns they learned from a wide array of data, but they do not inherently possess an ability to assess the accuracy of that information compared to sources on the internet.

The reliability of information from LLMs can depend on multiple factors, such as the quality of the training data, the specificity of the question asked, and the context in which the information is presented. While LLMs can synthesize knowledge and deliver answers that may be precise and relevant, there is still the potential for errors or outdated information, as these models do not have real-time access to the internet for verification purposes.

Moreover, information on the internet is diverse and can vary significantly in reliability. Some sources are highly credible, while others may be misleading or inaccurate. Therefore, instead of assuming a blanket trustworthiness for LLM-generated answers, it is important to evaluate the context, source credibility, and the nature of the inquiry to determine the most reliable information available. This nuanced understanding emphasizes the importance of critical thinking and cross-referencing information, rather than relying solely on LLM outputs.