Token probability distributions highlight persistent challenges in LLM fact handling

OpenAI’s GPT models and other large language models (LLMs) exhibit inconsistent behavior when dealing with factual information that has changed over time, as demonstrated through an analysis of how they handle the height measurement of Mount Bartle Frere in Australia.

Key findings: Token probability distributions in LLMs reveal how these models simultaneously learn multiple versions of facts, with varying confidence levels assigned to different values.

• When asked about Mount Bartle Frere’s height, GPT-3 assigns a 75.29% probability to the correct measurement (1,611 meters) and 23.68% to an outdated figure (1,622 meters)
• GPT-4 shows improved accuracy, providing the correct height 99% of the time in standard queries
• Adding seemingly irrelevant context to prompts can shift these probability distributions, causing models to favor outdated information

Technical analysis: The way LLMs process and store information creates inherent challenges in maintaining consistency across different contexts.

• Token probability distributions reflect how models learn from training data containing conflicting information
• Even advanced models like GPT-4 and Google Gemini 1.5 Pro exhibit this behavior when presented with specific prompt patterns
• The phenomenon persists despite attempts by newer models to reason through factual discrepancies

Real-world implications: This finding raises important considerations for the practical application of LLMs in systems requiring factual accuracy.

• Organizations integrating LLMs into their applications may need additional verification mechanisms for factual information
• The issue extends beyond simple fact-checking, as contextual changes can affect the model’s confidence in correct versus incorrect information
• Current solutions attempting to reason through contradictions show promise but don’t fully resolve the underlying problem

Looking ahead: These findings highlight the need for more sophisticated approaches to handling temporal data in LLMs.

• Progress in model development should address how temporal information is encoded and retrieved
• Greater transparency about these limitations would help organizations better understand and account for potential inconsistencies
• Future research might focus on developing more robust methods for managing and updating factual knowledge within neural networks

Beyond the numbers: The discovered inconsistencies in fact handling reveal deeper questions about how neural networks process and prioritize information, suggesting that current approaches to training these models may need refinement to better handle evolving real-world data.