Understanding Why AI Models Like Microsoft Copilot Can Provide Incorrect Answers

Microsoft Copilot, powered by large language models (LLMs), is designed to assist with tasks by generating human-like text based on patterns learned from vast datasets. However, these models do not possess consciousness or genuine understanding in the human sense. Instead, they predict the most statistically probable sequence of words based on their training data and the given prompt. This fundamental operational method is the root cause of why they can sometimes generate inaccurate or entirely incorrect responses.

How AI Generates Responses

AI models like Copilot are trained on enormous volumes of text and code from the internet and other sources. During training, they learn relationships, grammar, facts, and writing styles. When a prompt is received, the model uses this learned information to predict the next word, then the next, and so on, to form a coherent and relevant-sounding response. This process is essentially pattern matching and prediction, not factual retrieval and verification in the way a human might perform it.

Common Reasons for Incorrect Answers

Several factors contribute to the generation of wrong answers by AI assistants.

Data Limitations and Quality

• Outdated Information: The training data has a cutoff date. Information or events that have occurred since the last training update will not be known to the model, leading to outdated or factually incorrect statements about recent topics.
• Inaccuracies in Training Data: If the data the model was trained on contained false or biased information, the model may reproduce these inaccuracies. The web, for instance, contains misinformation.
• Limited Scope: While trained on vast data, no model has access to all information. Specialized or niche topics may not be covered sufficiently in the training data, resulting in superficial or incorrect responses.

Misinterpreting Context or Nuance

• Ambiguity in Prompts: If a user's request is unclear, uses ambiguous language, or could be interpreted in multiple ways, the model might guess the user's intent incorrectly and provide an answer that doesn't match what was sought.
• Lack of Real-World Understanding: AI models lack personal experience or subjective understanding. They might fail to grasp subtle nuances, sarcasm, irony, or implicit assumptions embedded in a prompt or required for an accurate answer in a specific real-world situation.

"Hallucinations"

• Generating Plausible Fabrications: A significant cause of wrong answers is the phenomenon known as "hallucination." The model generates information that sounds entirely plausible, is grammatically correct, and fits the context, but is factually incorrect or completely made up. This can include citing non-existent sources, fabricating facts, or creating fictional events. This occurs because the model prioritizes generating coherent text based on patterns over verifying factual accuracy.

Bias in Training Data

• Reinforcing Societal Biases: AI models can inadvertently learn and perpetuate biases present in the data they were trained on. This can lead to responses that are discriminatory, unfair, or inaccurate when dealing with sensitive topics or demographic groups.

Difficulty with Complex Reasoning

• Logical Errors: While good at pattern recognition, LLMs can struggle with complex logical deduction, multi-step reasoning, or intricate problem-solving, leading to flawed conclusions or incorrect factual statements.
• Conflicting Information: The training data may contain conflicting information on certain topics. The model might struggle to reconcile these conflicts and produce a definitive, correct answer, potentially generating a response based on one source of information while ignoring contradictory, potentially more accurate, information elsewhere in its training data.

Mitigating the Risk of Incorrect Information

While AI models are powerful tools, recognizing their limitations is crucial. Steps can be taken to reduce reliance on potentially incorrect answers and improve the chances of getting accurate information.

• Verify Critical Information: Always cross-reference facts, figures, and crucial details provided by the AI with reliable, authoritative sources, especially for important decisions or sensitive topics.
• Refine Prompts: Provide clear, specific, and unambiguous instructions or questions. Breaking down complex requests into simpler steps can also improve accuracy.
• Understand AI Limitations: Be aware that the model is a tool for generating text based on patterns, not a definitive source of truth or an expert with real-world knowledge.
• Use Follow-Up Questions: If an initial answer seems off or is unclear, ask follow-up questions to probe specific details or clarify misunderstandings.
• Report Inaccuracies: Utilize feedback mechanisms within the application to flag incorrect or misleading responses. This helps developers improve the model over time.
• Seek Diverse Sources: Do not rely solely on AI for information, especially on critical subjects. Consult multiple traditional and digital sources.

Understanding that AI models are sophisticated pattern-matching engines rather than infallible knowledge bases is key to using them effectively while recognizing the inherent possibility of receiving incorrect information.