{"id":13153,"date":"2024-10-29T12:15:00","date_gmt":"2024-10-29T16:15:00","guid":{"rendered":"https:\/\/www.protrainings.com\/blog\/?p=13153"},"modified":"2024-12-17T10:54:16","modified_gmt":"2024-12-17T15:54:16","slug":"hallucination-problem-use-ai-based-learning-tools-wisely","status":"publish","type":"post","link":"https:\/\/www.protrainings.com\/blog\/hallucination-problem-use-ai-based-learning-tools-wisely\/","title":{"rendered":"The Hallucination Problem: Avoiding Pitfalls in AI-Assisted Learning"},"content":{"rendered":"\r\n

One of the biggest frustrations people face when using text-based AI tools \u2014 also known as large language models (LLMs) \u2014 is that they tend to hallucinate<\/a>. No, they aren\u2019t dabbling in \u201870s-era party favors! Rather, \u201challucination\u201d is a term used to refer to when AI answers questions with inaccurate, partially accurate, or even completely fabricated information.<\/p>\r\n\r\n\r\n\r\n

Rather than indicating a flaw in the tools\u2019 design, however, this problem demonstrates a widespread misunderstanding of their intended application. In fact, AI hallucination is actually a huge asset when used in the right way.<\/p>\r\n\r\n\r\n\r\n

With proper expectations and mitigations in place, LLMs can be fantastic learning tools. The key is understanding what they are \u2014 and aren\u2019t \u2014 built to do and which tools are best suited to help you produce your desired results.<\/p>\r\n\r\n\r\n\r\n

Here\u2019s what you need to know about LLM hallucination, what to do about it, and the real problem with using LLMs for learning purposes.<\/p>\r\n\r\n\r\n\r\n

Choosing the Right AI Tools<\/h2>\r\n\r\n\r\n\r\n

Before we get into the specifics of LLMs and hallucination, it\u2019s important to understand that AI tools \u2014 like any other kind of tool \u2014 are only effective when used correctly. If you try to complete a task using the wrong tool, you\u2019re unlikely to get the results you\u2019re looking for.<\/p>\r\n\r\n\r\n\r\n

For example, you wouldn\u2019t use a spoon to slice a loaf of bread. Instead, you\u2019d use a knife \u2014 ideally, one made specifically for slicing bread.<\/p>\r\n\r\n\r\n\r\n

In the same way, if you want an AI tool to accomplish a particular goal \u2014 whether that\u2019s supplying reliable information on a given topic or generating new ideas \u2014 you need to use a tool that has been designed and trained for that specific purpose.<\/p>\r\n\r\n\r\n\r\n

\"ProTrainings<\/a><\/figure>\r\n\r\n\r\n\r\n

Additionally, remember that many of these tools are still in development. While they have amazing potential for use in education<\/a>, business, and even daily life, they still require human involvement to monitor and fine-tune results.<\/p>\r\n\r\n\r\n\r\n

For example, at ProTrainings, we are experimenting with how to use AI speech models and LLMs<\/a> to translate and dub our courses into as many languages as possible. However, we don\u2019t blindly use AI to create the original course content specifically because of the hallucination problem. When it comes to life-saving skills training, we can\u2019t rely on AI to generate accurate information, so all of our CPR courses<\/a> undergo a rigorous review process by a board of medical professionals before they are published.<\/p>\r\n\r\n\r\n\r\n

Understanding LLMs<\/h2>\r\n\r\n\r\n\r\n

The earliest iterations of LLMs were limited to predictive text, where a user begins typing and the tool offers suggestions for completing the sentence. Now, however, through reinforcement learning from human feedback (RLHF)<\/a>, LLMs have been trained not only to complete sentences but to generate full conversations.

In the words of Andrej Karpathy, formerly of Tesla and OpenAI, LLMs are \u201cdream machines.\u201d When prompted, they generate ideas and information based on their extensive training data. If they haven\u2019t been trained on the requested information \u2014 or if they simply fail to access it \u2014 they will invent new information to fulfill the given prompt.<\/p>\r\n\r\n\r\n\r\n

However, hallucination is a feature of LLMs, not a bug. LLMs are designed to generate written content that sounds \u201cright.\u201d Often, we want them to hallucinate fresh ideas we can\u2019t or don\u2019t have time to think of on our own. This is why so many people love to use ChatGPT when brainstorming or creating content.<\/p>\r\n\r\n\r\n\r\n

The problem arises when people approach LLMs as a search engine, blindly trusting them to provide factual information and not manually verifying that information. This approach is extremely misguided and can cause serious problems, like the lawyers who recently faced sanctions<\/a> because they cited nonexistent cases generated by ChatGPT.<\/p>\r\n\r\n\r\n\r\n

That\u2019s not to say that LLMs never answer questions correctly or that we should avoid using AI technology altogether. Sometimes LLMs do provide factual information from valid sources, especially if they have been trained specifically to answer questions on that topic. AI tools such as Perplexity are being created for just this reason.<\/p>\r\n\r\n\r\n\r\n

Instead, it\u2019s important to understand how LLMs work and use them wisely according to their intended purpose.<\/p>\r\n\r\n\r\n\r\n

Mitigating Hallucination<\/h2>\r\n\r\n\r\n\r\n

While we can\u2019t \u2014 and shouldn\u2019t \u2014 expect information gained from LLMs to be 100% accurate, there are many ways to mitigate hallucination, both on the developer and the consumer ends.\u00a0<\/p>\r\n\r\n\r\n\r\n

RAG & Fine-Tuning<\/em><\/strong><\/h3>\r\n\r\n\r\n\r\n

When building LLMs, developers may use fine-tuning to train the model on a certain topic or context, which helps improve its accuracy in responding to relevant prompts.<\/p>\r\n\r\n\r\n\r\n

For example, if the LLM is intended to answer healthcare-related questions, the developer could fine-tune it to only reference high-quality scholarly articles and medical texts instead of pulling from less reputable sources or making up false information.<\/p>\r\n\r\n\r\n\r\n

Or if the developer is building a customer service chatbot, they may fine-tune the model on proper ways to respond to customer inquiries.<\/p>\r\n\r\n\r\n\r\n

However, fine-tuning can be cost-prohibitive and time-consuming, and the LLM will require retraining if the underlying training data is changed or if a new base model is released.<\/p>\r\n\r\n\r\n\r\n

Retrieval augmented generation (RAG) is another technique that can force LLMs to answer questions with greater accuracy. Instead of allowing the LLM to rely on its broad base of training data, the developer or user feeds the LLM a specific set of source data \u2014 such as a user manual or website domain \u2014 and asks it to pull answers exclusively from that source.\u00a0<\/p>\r\n\r\n\r\n\r\n

RAG tends to be more popular than fine-tuning for a lot of use cases because of its relative ease of implementation and flexibility with a changing data set.<\/p>\r\n\r\n\r\n\r\n

Manual Fact-Checking<\/em><\/strong><\/h3>\r\n\r\n\r\n\r\n

Finally, when using an LLM \u2014 especially for learning purposes \u2014 always verify that the information it provides is accurate.\u00a0<\/p>\r\n\r\n\r\n\r\n