AI Hallucinations

Larger Models. Training models with more training data

Using larger models with more parameters and expanding training datasets can significantly reduce hallucinations in LLMs. Increased model size allows AI systems to capture more complex relationships and patterns, while broader training data provides more comprehensive examples to learn from.

Larger models can:

• Process more contextual information simultaneously
• Maintain better coherence across longer outputs
• Develop stronger internal representations of factual knowledge

However, this approach faces inherent limitations. Even the most sophisticated models struggle when:

• The relationship between input and output is exceptionally complex
• Computational constraints prevent full pattern recognition
• The underlying rule requires reasoning beyond what statistical pattern matching can achieve
• Training data itself contains inaccuracies or biases

While scaling up helps reduce hallucinations, it doesn’t eliminate them entirely. As models grow, improvements often follow a law of diminishing returns, where substantial increases in size and training data yield increasingly modest gains in accuracy.

Chain of Thought

Chain of Thought (CoT) is part of in-context learning, where models are guided through examples or relevant contextual information provided in the prompt. This technique instructs the model to break down complex problems into a sequence of logical reasoning steps rather than jumping directly to a conclusion.

Reasoning models are specifically designed to implement this approach, systematically working through problems by generating an explicit thinking process, before providing final answers.

Chain of Thought helps models:

• Decompose complex problems into manageable logical steps
• Follow explicit reasoning paths that can be verified
• Reflect on their own assumptions and conclusions
• Identify and correct potential errors before responding to the user
• Produce outputs that more closely align with human reasoning preferences

Limitations:

• Cannot overcome fundamental knowledge gaps in the model’s training
• Doesn’t guarantee factual accuracy if the base knowledge is missing
• May still hallucinate “plausible-sounding” but incorrect reasoning steps

Knowledge-Enhanced LLMs (RAGs)

Knowledge-enhanced LLMs supplement their built-in knowledge with external information sources to reduce hallucinations. These systems combine the reasoning capabilities of LLMs with factual information from trusted external sources, creating more reliable and accurate outputs.

Retrieval Augmented Generation (RAG) is the most common implementation, allowing models to access relevant information from documents, databases, and organizational knowledge bases. RAG works by retrieving context-relevant documents or passages that provide the factual basis for the model’s response, helping to ground outputs in verifiable information rather than relying solely on the model’s internal parameters.

Beyond RAG, models can also be connected to the internet, specialized APIs, and other data sources to access real-time information, creating a more comprehensive and up-to-date knowledge foundation.

Knowledge enhancement helps models:

• Access up-to-date information beyond their training cutoff
• Ground responses in verifiable facts
• Provide more accurate answers to domain-specific questions

Limitations:

• Retrieved information may still be misinterpreted by the model
• Hallucinations can occur in the synthesis of multiple information sources
• Models may blend retrieved facts with their internal knowledge incorrectly
• Models can still hallucinate when filling gaps between retrieved facts