The article is aimed at general readers who are interested in learning more about LLM

While the market for generative AI is expanding, there are still many untruths and myths that surround the operation of language models. There are several myths about LLMs that users need to be aware of, ranging from the idea that they are sentient to the idea that they can produce information with great accuracy and no bias.

1. LLMs Can Think: One of the most prevalent myths about LLMs is that they are capable of independent thought. Language models can, in fact, draw conclusions from a dataset, and provide summaries or text predictions, but they can't comprehend natural language the way a human can.

2. Language Models Create Content: LLMs can be used to produce material, but they don't independently innovate or produce original content. Rather, they use the patterns in the written or visual material they have seen in their training data to anticipate and create new content based on that training data. It is a debatable practice to produce answers using training data.

3. All Inputs Are Confidential: Another important myth regarding LLMs is that the information placed into the entry is fully private. This isn't always the case. Due to worries that the supplied information was being stored on an external server, Samsung prohibited ChatGPT at the start of this year after an employee exposed sensitive information to the application.

4. Generative AI Is 100% Accurate: Many users make the error of thinking that the data that programs like ChatGPT and Bard produce is entirely accurate or, at the very least, consistently correct. Language models are unfortunately prone to hallucination, which allows them to fabricate facts and information and assert them "confidently" as if they were true.

5. LLMS Are Impartial and Unbiased: Due to the fact that LLMs are created by people and resemble human language, it's critical to keep in mind that biases are ingrained in these systems, especially if the underlying training data is inaccurate. Users therefore cannot afford to view them as neutral and unbiased sources.

6. Generative AI Is Effective in All Languages: Although generative AI tools can be used to translate text between languages, how successful they are at doing so relies on how widely spoken the target language is. LLMs excel in producing responses in widely spoken languages in Europe, such as English and Spanish, but struggle when asked to produce arguments in less common tongues.

7. LLMs Report Information from the Internet: Language models like GPT4 and GPT 3.5 process their training data instead of connecting to the Internet in real-time. Users have no idea what data LLMs are utilizing to produce results because providers like Google, OpenAI, and Microsoft keep the nature of this training data completely hidden from consumers.

8. LLMs Are Designed to Replace Human Employees: The intelligence, creativity, and resourcefulness of human workers cannot be replaced by LLMs in their current form, despite the fact that AI has the ability to automate millions of jobs. The goal of generative AI is to support knowledge workers rather than replace them.

9. LLMs Can't Produce Malicious Content: Some users might think that providers like OpenAI's content control safeguards stop people from using their services to make inappropriate or harmful content, however, this is untrue.

10. LLMs Can Learn New Information Continuously: In contrast to humans, LLMs use deep learning methods to find novel patterns in their training data rather than constantly acquiring new information. They can draw more specific conclusions from a dataset by having a better understanding of these patterns.