In Short:
Large language models (LLMs) are powerful tools with diverse applications, but evaluating them systematically is challenging due to their wide usability. MIT researchers propose an evaluation framework based on human beliefs about LLM capabilities. The study reveals that misalignment with human generalization function leads to unexpected failures, with more capable models underperforming in high-stakes scenarios. The research aims to improve model deployment by aligning them with human understanding.
The Impact of Human Beliefs on Large Language Models (LLMs)
Large language models (LLMs) are known for their versatility in various tasks, from drafting emails to diagnosing cancer. However, evaluating these models systematically poses a challenge due to their broad applicability.
Understanding Human Perception
In a new paper, MIT researchers propose a novel method to evaluate LLMs based on human beliefs about their capabilities. They emphasize the importance of aligning LLM performance with human expectations.
Using a human generalization function, the researchers study how people update their beliefs about LLM capabilities after interacting with them. This alignment is crucial to avoid unexpected model failures, especially in high-stakes scenarios.
Co-author Ashesh Rambachan highlights the significance of considering human factors in deploying LLMs, as they often collaborate with individuals in various tasks.
Key Findings
The researchers conducted a survey to analyze how people generalize LLM performance across different tasks. They observed that individuals had difficulty predicting LLM behavior compared to human responses.
Participants were more likely to update their beliefs about LLMs based on incorrect responses and underestimated the impact of simple tasks on complex ones. Notably, simpler models outperformed larger ones in scenarios where incorrect responses carried more weight.
The researchers aim to further study how human beliefs evolve over time with LLM interactions and explore integrating human generalization into LLM development.
Implications
As LLMs become more prevalent in everyday use, understanding human generalization is crucial for enhancing model performance. Aligning LLM capabilities with human expectations can lead to more effective and reliable applications.
Professor Alex Imas commends the research for shedding light on the alignment between LLMs and human understanding, emphasizing the need to address this fundamental issue.
This study was supported by the Harvard Data Science Initiative and the Center for Applied AI at the University of Chicago Booth School of Business.
