What is Grounding and Hallucination in AI?

Generative AI(s) like ChatGPT and Gemini are the go-to platforms for anybody who is looking for some information or answers to their queries. However, these gen-AI tools providing misleading and incorrect information is no surprise. Most AI software are unaware of how practical things work, so inaccurate responses become quite common. So, in this blog, we will learn about AI hallucination, its types, and its causes, and also discover about grounding, the ultimate technique to combat hallucinations in AI.  

What are Hallucinations in AI?

Generative AI’s false, misleading, or illogical information, presented as a fact in response to the query, is called hallucination in AI. AI always generates content in a confident tone, making minor or illogical changes hard to spot, so one should always be attentive and double check all the AI query results to the question. Let us now understand the types of hallucinations and how they happen. 

Types of Hallucinations 

Following are the types of hallucinations in AI: 

Sentence Contradiction 

This is when the AI generates answers in which a sentence contradicts the previous sentence. For example, “The shirt is 100% cotton. It is not breathable.” 

Prompt Contradiction 

As the name says, prompt contradiction refers to when the AI’s answer doesn’t relate to the question. For example, you ask a virtual AI assistant about all the places to visit in Rome, but it lists all the places you can visit in Venice.  

Factual Contradiction 

This refers to the usual incorrect information or when the answer contradicts a fact. For example, if the AI states that the moon is made of cheese, it’d be counted as a factual contradiction. 

Irrelevant or Random Hallucinations 

This is when the AI response is completely unrelated to the task given. For example, if the AI object detection tool identifies or tags the picture of a cat as a fire truck, it would be a random or irrelevant hallucination. 

Why do Hallucinations happen? 

Several factors can lead to hallucinations, but the following are some of the major reasons hallucinations in AI occur:

Data Quality 

The data with which the AI model has been trained sets the base for its accuracy and reliability. Thus, if the training dataset has not been cleaned and validated properly and has missing values, bias, errors, etc, it would definitely lead to AI hallucinations and inaccuracy of the model. 

Generation Method 

The generation method of AI, which generates the answer, plays quite an important role. Sometimes, the bias that occurred in the previous answer can create a hallucination. Also, sometimes the model uses techniques like flipping a coin- heads for word A, tails for word B, in which it gets stuck in a loop or makes statistically incorrect choices, which leads to illogical outputs. 

Input Context 

The clarity of input prompts matters in order to have a reliable AI answer in return. For example, if the user is asking a vague question like, “What is the meaning of life?” then the generative AI will definitely write philosophical answers with no correct or incorrect perspective. Therefore the clearer your prompts would be, the better AI would interpret and respond. 

Grounding in AI 

Grounding in AI refers to connecting the abstract knowledge of AI to real-life examples. This allows the AI model to be more reliable and accurate in predictions and answers, thereby building the user’s trust and reliability. It bridges the gap between the AI’s internal world and reality to reduce hallucinations. Using high-quality data, emphasizing clearer prompts, potentially connecting to real-world information, and incorporating feedback mechanisms make AI less likely to make things up, leading to more trustworthy and reliable outputs.

Importance of Grounding 

Grounding AI systems stand better than non-grounded AI systems as they work in consideration of real-world scenarios and, thus, create more relevant and accurate replies. Let us understand better with the key points given below: 

Quality & Accuracy 

Whether for AI in education or e-commerce, AI models or virtual bots deal with the users, provide them with relevant information, and solve user queries. This would require them to be accurate and relevant in order to provide quality service on behalf of businesses to customers. 

Minimizing Hallucinations 

Grounding techniques tend to reduce the occurrence of hallucinations to an extent, which directly elevates the quality of AI responses to the user’s queries. While a certain degree of hallucinations highlights the creative potential of AI, establishing grounding techniques ensures that such outputs remain verifiable and do not disseminate false information.

Enhancing AI Decision-Making 

In industries where AI algorithms are used to make critical decisions like issuing refunds in e-commerce, dynamic pricing, etc, grounding allows the models to make informed decisions while closely aligning with real-life situations, thus minimizing errors and increasing the reliability of outputs. 

Interpreting Complex Situations 

AI algorithms usually struggle to understand complex real-world data. Grounding helps them better grasp the complexity, nuances, ambiguity, and multimodal data, thus enhancing the efficiency and accuracy of the model. 

Some Methods To Ground AI & Prevent Hallucinations 

To have a comprehensive understanding of grounding, let’s go through some simple grounding methods below: 

Fine-tuning with Use Case Data

The AI model developed for a specific industry or use must be trained with the high-quality dataset of the specific industry. This technique ensures the accuracy of the model for the tasks it has been built for. 

Prompt Engineering for Clear Guidance

Hiring prompt engineers to write clear and structured instructions for making the AI model better understand the questions and tasks. With well-defined prompting in AI that outlines the desired outcomes and tasks would guide AI to generate more accurate and relative results. 

Retrieval-Augmented Generation (RAG)

RAG combines two types of neural networks (retrieval-based and generative-based) for AI to work. The first neural network retrieves the required information from a large training dataset, and the second generates the output for the user. This tackles the issue of limited or incomplete responses for AI to work. 

Reinforcement Learning with Feedback

By using reinforcement learning, the technique of training AI by providing positive and negative reinforcement for hallucinations. This can be done through adversarial networks (where AI competes with another AI to improve its performance) or by incorporating human feedback into the training process.

Benefits of Grounding in AI 

There is a constant requirement for more reliable solutions to cater to businesses’ needs and assist them with automation and decision-making. To let any AI solution stand apart, grounding in AI is the key. 

The following are the benefits of Grounding in AI: 

1. Personalization 

Grounding techniques let AI algorithms closely align with the user’s or business’s needs and previous data, thus delivering personalized recommendations or solutions for them. 

2. Compliance with Rules and Regulations

Compliance with the rules is crucial for many industries and countries. Thus, by adopting grounding techniques, AI models can adhere to these regulations, promoting responsible and careful use of AI. 

3. Enhanced Accuracy

By reducing hallucinations, AI minimizes the occurrence of errors in its responses which directly affects the accuracy of the results of AI. This improves the trust of the users in artificial intelligence models. 

4. Limitless Growth

Grounding allows the AI algorithms to quickly grasp the changes and update their working accordingly, which makes the AI model scalable to new industries, tasks, and users. 

5. Industry-Specific Expertise

Grounding techniques let the businesses input the industry-specific details into the AI systems, thus allowing the AI model to function accurately for industry-related tasks and produce dependable insights. 

Hence, Grounding in AI techniques not only controls its hallucinations but helps businesses develop more accurate solutions that would build their customer’s trust and enhance their brand value by letting them stand out of the crowd. As the use of AI increases in various industries, adopting such advanced techniques would become necessary to provide users with high-quality services. This would not only benefit the users and customers, but the developed AI models would even prove to be reliable for businesses and produce accurate business insights for them. These insights would, in turn, help them make more informed and intelligent business decisions, eventually increasing their profits and ROI. 

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By combining artificial intelligence with innovation, we build cutting-edge AI solutions to streamline your business operations. 

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