AI models, especially large language models (LLMs), process massive amounts of text data. This data can contain a vast array of concepts and information. However, these concepts and information remain abstract within the model's internal representation. They may not relate to the real world, and if they do relate to the real world, the information may be wrong. Because of this, it’s important to ensure that you are grounding AI models to prevent hallucinations in AI.

What are AI hallucinations? In short, they’re wrong responses. AI hallucinations occur when a model generates outputs that seem plausible but are not grounded in reality. By understanding the causes of the AI hallucination problem and implementing mitigation strategies, the AI models you use (or develop) will generate more reliable and realistic outputs.

Hallucinations are difficult to prevent because they are produced by the same process that generates every output of the AI model. This post answers the question, “What is grounding and hallucinations in AI?” It details the causes of artificial intelligence hallucinations and how grounding AI models can decrease their occurrence. There are several ways to ground an AI model in events in reality.

Why Grounding AI Is Important

AI grounding bridges the gap between the model's internal representation and the physical world. This provides several benefits:

• Improved Performance: AI models can achieve better performance (make more relevant and accurate predictions) on real-world tasks. Grounding data can help models generalize better to unseen data. By learning from real-world information, the model is less likely to rely solely on statistical patterns within the training data, which might not hold true in real-world scenarios.
• Reduced Bias: Grounding can help mitigate biases that might be present in the training data. For example, researchers have found gender and skin color biases against women and especially women with darker skin. Grounding can decrease those biases.
• Enhanced Explainability: Grounding can make AI models more interpretable, allowing humans to better understand their decision-making process. For example, by understanding the data sources behind the model's outputs, it becomes easier to understand the model's reasoning and decision-making process. This can be crucial for debugging issues or building trust in the model.
• More realistic images: The AI hallucination example below was generated by an AI when prompted to “draw the letter A as a teacher with a computer in her hand.” The AI drew the teacher holding a pointer because the words “teacher” and “pointer” often occur near each other in sentences. Because the AI does not understand reality, it (a) drew the computer on top of the pointer, and (b) made the pointer visible as if the computer screen was a window.

What are Hallucinations in AI and Why Do They Happen?

“Grounding” refers to the process of connecting the abstract concepts learned by a model to real-world information and experiences. It essentially bridges the gap between the model's internal representation of the world and the external world we identify as reality.

Here's an analogy: Imagine a child learning the word "banana." By seeing, touching, and tasting a banana, the child grounds the abstract mental concept to the real-world object it represents. Even when students are learning physics, if no real-world sensory experiences are associated with the words, the person is, “only told what a word means in terms of other words. You haven’t told it anything about nature”. As the physicist Richard Feynman discovered when teaching in Brazil, knowledge based only on word relations may not generalize to real-world problems.

This is why grounding AI models is needed to connect its internal representation of concepts to the tangible world. Without proper grounding in real-world information and experiences, AI models may not connect their internal representations of the world to verifiable data sources. There are several key reasons why AI may not be grounded in reality and hallucinations happen:

Data Issues

• Limited Data: If an AI model is trained on an insufficient amount of data, it might not have enough information to learn the underlying patterns in the real world. This can lead the model to make up patterns or relationships that don't actually exist, resulting in hallucinations.
• Biased Data: If the training data contains biases or reflects skewed real-world distributions, the model might inherit those biases and generate outputs that perpetuate them. For example, a language model trained on biased text data might generate discriminatory or offensive outputs.
• Statistical Artifacts: Statistical quirks or correlations within the training data that don't hold true in the real world can be misinterpreted by the model and reflected in its outputs. For example, noise in the training data may be interpreted as meaningful patterns and generate outputs based on these misinterpretations. These statistical artifacts can contribute to Gen AI hallucinations.

Mathematical Issues

• Overfitting: When a model is trained for too long on the same data, it can start memorizing specific patterns in the training data instead of learning generalizable concepts. This can lead the model to perform well on the training data but struggle with unseen data, potentially generating nonsensical outputs.
• Underfitting: When a model is underfit, it essentially hasn't learned the underlying patterns and relationships within the training data effectively. This can lead to overly simplistic models that may resort to making random guesses or producing outputs based on irrelevant features in the training data. These outputs might be nonsensical in the real world and are therefore hallucinations.

Other AI Grounding Model Issues

• Incorrect Assumptions: If the underlying assumptions used to design the model architecture are flawed, it can lead the model to make systematic errors and generate hallucinations.
• Inappropriate Model Choice: Choosing the wrong type of AI model architecture for the specific task can lead to limitations in the model's ability to learn the underlying patterns effectively. This mismatch can contribute to hallucinations as the model struggles to represent the data accurately.
• Limited Scope of Training: AI models are often trained for specific tasks and might not perform well outside their intended scope. When encountering unseen scenarios or data that deviates significantly from the training data, the model might resort to hallucinations.
• Inadequate Hyperparameter Tuning: Hyperparameters are settings that control the training process of an AI model. Inappropriate hyperparameter settings can lead to overfitting, where the model prioritizes memorizing the training data over learning generalizable patterns.

AI hallucinations can be decreased and the AI’s performance improved by addressing these issues. In critical applications, consider incorporating human oversight or review mechanisms to identify and address potential hallucinations generated by the AI model.

Focus on acquiring high-quality, diverse, and unbiased data for training an appropriate model architecture for the use cases. Simpler models are generally less prone to overfitting and hallucinations compared to overly complex ones. Monitor the model's performance in production to detect potential hallucinations emerging over time.

How to Ground an AI Model

Learning how to go about grounding AI models is an active research area. The goal is developing models that are good at statistical prediction and that understand and interact with the world in a meaningful way. As AI models continue to evolve, grounding will play a crucial role in building more robust, reliable, and trustworthy AI systems.

There are four common methods for model grounding:

1. Symbolic (Language) Grounding: This involves (a) associating the model's outputs (predictions, classifications) with the specific data used in training, and (b) connecting symbols and language used by the model to real-world entities and concepts. This helps ensure the outputs are based on real-world evidence, not just internal model biases or statistical artifacts.
2. Sensorimotor Grounding: Sensorimotor grounding involves techniques that allow AI models to interact with the physical world through sensors or actuators. This can involve robots manipulating objects based on their understanding of the environment or language models referencing real-world knowledge bases to enhance their responses.
3. External Knowledge Bases: LLM grounding can also be achieved by connecting the model to external knowledge bases containing information about the real world. This can provide the model with additional context and background knowledge.
4. Retrieval Augmented Generation (RAG): RAG is adding contextual information to a prompt, either manually, automatically, or both. Before the prompt is input to the primary AI, it is analyzed to identify the needed contextual information. This information is added to create the expanded prompt that is sent to the primary AI model.

In July 2024, Pieces Copilot+ introduced an advanced version of RAG to use prompt grounding in its AI models to access the user’s “temporal context.” Research has shown it is crucially important to provide an AI that writes code with additional context and feedback—this additional input increases the quality of code and decreases its security vulnerabilities.

When you have the Copilot+ Workstream Pattern Engine enabled, it captures, processes, and stores your workflow on an operating system level for later access. It uses on-device and real-time algorithms to selectively capture the most important information from your workflow in your IDE, browser, and collaborative tool such as Slack or Teams.

As you work, the Workstream Pattern Engine continues to learn more about your sources, your preferences, and your style of working. It is an assistant whose sole focus is being proactively helpful to expand your intelligence and productivity. Grounding AI includes taking context from your previous copilot chats to build on the information you are gathering in the present.

All captured data is stored on your local device. No one has access to your data unless you have chosen to share it. When the AI is accessed in local mode, it is also on-device and can be run without accessing the internet. The Workstream Pattern Engine uses machine learning to filter out (ignore) sensitive information and secrets. This enables the highest levels of performance, security, and privacy.

Conclusion

Grounding AI is essential for reducing hallucinations and enhancing the overall reliability of AI models. By bridging the gap between a model's abstract internal representations and real-world data, we can achieve improved performance, reduced biases, and enhanced explainability.

Methods such as symbolic grounding, sensorimotor grounding, external knowledge bases, and retrieval augmented generation (RAG) are pivotal in this process. As exemplified by Pieces Copilot+ and its advanced use of RAG, grounding AI not only improves the accuracy and security of generated code but also ensures that AI models can effectively integrate temporal context to boost productivity and intelligence. Ensuring that AI systems are well-grounded in reality is crucial for building robust, trustworthy, and high-performing AI solutions.