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The mismatch between human expectations of AI capabilities and the actual performance of AI systems does not allow users to effectively utilize LLMs. Incorrect assumptions about AI capabilities can lead to dangerous situations, especially in critical applications like self-driving cars or medical diagnosis. If AI systems consistently fail to meet human expectations, it can erode public trust and hinder the widespread adoption of AI technology.
MIT researchers in collaboration with Harvard University address the challenge of evaluating large language models (LLMs) due to their broad applicability across various tasks, from drafting emails to assisting in medical diagnoses. Evaluating these models systematically is difficult because creating a comprehensive benchmark dataset to test every possible question that can be asked is impossible. The key challenge is understanding how humans form beliefs about the capabilities of LLMs and how these beliefs influence the decision to deploy these models in specific tasks.
Current methods of evaluating LLMs involve benchmarking their performance on a wide range of tasks, but these methods fall short of capturing the human aspect of deployment decisions. Researchers propose a new framework that evaluates LLMs based on their alignment with human beliefs about their performance capabilities. They introduce the concept of a human generalization function, which models how people update their beliefs about an LLM’s capabilities after interacting with it. This approach aims to understand and measure the alignment between human expectations and LLM performance, recognizing that misalignment can lead to overconfidence or underconfidence in deploying these models.
The human generalization function is designed for observing how people form beliefs about a person’s or LLM’s capabilities based on their responses to specific questions. The researchers designed a survey to measure this generalization, showing participants questions that a person or LLM got right or wrong and then asking whether they thought the person or LLM would answer a related question correctly. This survey generated a dataset of nearly 19,000 examples across 79 tasks, highlighting how humans generalize about LLM performance. Results showed that humans are better at generalizing about other humans’ performance than about LLMs, often placing undue confidence in LLMs based on incorrect responses. Notably, simpler models sometimes outperformed more complex ones like GPT-4 in scenarios where people put more weight on incorrect responses.
In conclusion, the study focuses on the misalignment between human expectations and LLM capabilities that can lead to failures in high-stakes situations. The human generalization function provides a novel framework to evaluate this alignment. It highlights the need for better understanding and integrating human generalization into LLM development and evaluation. The proposed framework accounts for human factors in deploying general-purpose LLMs to improve their real-world performance and user trust.
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“}]] [[{“value”:”The mismatch between human expectations of AI capabilities and the actual performance of AI systems does not allow users to effectively utilize LLMs. Incorrect assumptions about AI capabilities can lead to dangerous situations, especially in critical applications like self-driving cars or medical diagnosis. If AI systems consistently fail to meet human expectations, it can erode
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