Understanding Hallucination in Chatbots Using LLMs
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In the world of Large Language Models (#LLMs), there's a term that's catching attention: 'hallucination'. But what does it mean, especially when we talk about #chatbots? Let's dive deep into this topic.
?What is Hallucination in LLMs?
In simple terms, when we talk about LLMs 'hallucinating', we mean that the model sometimes makes things up. It might give answers or responses that aren't quite right or don't make sense. For #chatbots, this can be a big deal, especially when they're answering questions or giving information.
?How Chatbots Can Handle Hallucination:
Using Set Responses: One straightforward way to stop a chatbot from making things up is to use set answers. This means, instead of letting the chatbot think of an answer on its own, we give it a list of answers to pick from. This can be done using LLMs for things like figuring out the topic of a question. The chatbot can then give a set answer based on the topic. This method is very safe, but it can make the chatbot sound less like a human and more like a machine.
Guiding the Chatbot with Prompts: Hallucination can also be minimized using advanced prompting techniques. Consider the two prompts below:
Prompt 1: "You are a customer support agent, helping a customer with a billing issue. Ask the user for their phone number." #CustomerSupport
Prompt 2: You are a customer support agent, helping a customer with a billing issue. The phone number is required for getting the billing related information. The conversation so far with the customer is delimited by four hashes I.e.; ####?
?Conversation:
Agent: How can I help you?
Customer: I got my bill yesterday and looks like I was charged $40 extra for a service that I didn't use. Agent: Sure, I can help you with your billing issue. Can you tell me your name to start with?
?Customer: My name is John
?Agent: ####
?Here is how you need to think:?
Step 1: Check the emotional state of the customer. Possible states are:?
A. Normal?
B. Frustrated?
C. Confused
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?D. Others (specify)
?Step2: Based on the emotional state above, try to articulate the value of asking for a phone number.?
Step 3: Provide the information in the following JSON format. {“prompt": <generated prompt>, "parameter": "phone number”}
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Here are a few ways in which you can ask for this information -
"Hey {customer name}, can I have your phone number", "Can I have your phone number. I need it for getting your billing details"
?In the first prompt, the chatbot has a lot of freedom, resulting in increased possibility of hallucination. But in the second prompt, we give the chatbot more information and direction. This can help it give a better, more accurate answer. #AIAssistance
?How to Check if a Chatbot is Hallucinating:
It's important to know if a chatbot is making things up. So, how can we check? Here are some ways, as mentioned in a LinkedIn article:
?Semantic Coherence: This is a fancy way of saying, "Does the answer make sense with the question?" We check if the chatbot's answer matches the topic and meaning of the question. #LanguageAI
?Fact Verification: Here, we check if the chatbot's answer is true. We can use other tools or databases to see if the information the chatbot gives is correct. #FactCheck
?Contextual Understanding: This means checking if the chatbot's answer fits the bigger picture. For example, if a user has been talking about a billing problem, does the chatbot's answer fit that topic? #ContextMatters
?Adversarial Testing: This is like trying to trick the chatbot. We give it tricky questions to see if it makes things up in its answers. #Testing
?Human Evaluation: Sometimes, the best way to check is just to ask a person. By having humans look at the chatbot's answers, we can get a good idea if it's making things up. #HumanInsight
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Hallucination in LLMs is a vast topic, but it's important to understand, especially for #chatbots. By using set answers, guiding prompts, and checking the chatbot's responses, we can make sure it gives good, accurate information. For businesses, this means better service and happier customers. #EnhancedCX
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