What Is "Evaluate Hallucinations" in LLMs?
When we make use of big language models (LLMs) such as ChatGPT or other computing tools, sometimes they provide responses that sounds smart but they are incorrect. Those incorrect responses are referred to as hallucinations. Term "hallucination" comes from when a human sees or hears something which is not actually present. Similarly, when some machine learning computer generates something that appears to be correct but is actually false, then that machine learning computer is called as hallucinating.
Hallucinations are our attempt to verify whether the AI provided any of these incorrect responses. We look at the AI's answers and compare them with actual facts from reliable sources, such as Wikipedia or official websites. We want to determine when the AI provides incorrect information and how frequently that occurs. This is crucial since individuals utilize AI for writing, learning, working, and even making choices. When AI provides incorrect responses, it results in issues or confusion. That's why, we must have a means of verification and enhancing the validity of what AI provides.
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Tools or Libraries to Evaluate Hallucinations in NLP Projects
In NLP projects, humans use various tools and libraries to verify if AI hallucinating something. With these tools, we are able to test and measure how truthful the AI is being. One common way to do it is to use special tests known as benchmarks. These are groups of questions in which we already know what the right answers are. We allow the AI to respond to those questions, and then we count how many times it answers incorrectly. If AI provides various fake answers, that means it hallucinates frequently.
There are libraries of code, which assist us in detecting hallucinations programmatically. One approach is known as Retrieval-Augmented Generation (RAG). This assists AI in retrieving actual information from documents prior to providing an answer. If AI is unable to find anything, then that answer could be a guess. Another useful check is perplexity checking. This verifies how "surprised" AI is at the words it is writing. If AI is surprised at its own response, it may be creating it.
Other tools such as THaMES or HaluEval, are more sophisticated. These assist researchers in testing whether the AI is telling the truth or not and recommend how to enhance it. There are even other AIs (such as a checker AI) that can analyze an AI's response and let you know if something appears fake or not. These tools are very helpful, especially for developers and researchers creating or testing language models.
Role of Human Feedback in Enhancing Hallucination Detection and Reduction in Generative Model
Yet we have intelligent programs to verify AI responses, and people's feedback is still important. Humans can pick up on things that computers may not. For instance, a person might know that something recently happened, but the AI may not yet be aware of this. In such case, a human can comment, "This response is out of date," or "This is no longer correct."
Human feedback is also used to train the AI to do better. When people point out mistakes and give the correct answer, that information is added to the AI’s learning process. The AI can then avoid similar mistakes in the future. Human reviewers also help with very difficult or confusing questions. Sometimes, the AI gives an answer that is not clearly wrong or right. Someone can assist in determining what's correct by context or greater insight.
In short, human input assists the AI in learning right and wrong, similar to how a teacher marks a student's homework. Without human input, it would be far more difficult for AI systems to learn and minimize their hallucinations.
Differentiate Between Minor Factual Mistakes and Actual Hallucinations
Sometimes, the AI gets a little wrong, such as a number or the incorrect date. That is referred to as a minor factual error. It is still an error, but not a large one. These types of errors can occur even with people. For instance, the AI may state a book was released in 2002 when it was really 2001. That is close, and perhaps it won't make much of a difference to the meaning.
But a real hallucination is when the AI mentions something totally untrue. For instance, it may claim someone won an award that never existed, or it may refer to a city or event that does not exist. These are worse because they can totally mislead a person who believes the response. A real hallucination has no source or evidence whatsoever behind it.
In order to differentiate, we compare how close the answer is to reality. We search the internet or consult reliable sources. If it is actual but slightly incorrect, it's a small mistake. If we find nothing to verify it, and AI fabricated itself, it's a hallucination. This process is quite significant when testing AI systems, it enables us to know how accurate AI is.