Testing theory of mind in large language models and humans

In a recent groundbreaking study published in the renowned journal Nature, a team of researchers, including AlienTT members Alessandro Rufo, Guido Manzi, Saurabh Gupta and Krati Saxena, explored the theory of mind capabilities in humans and large language models (LLMs) such as GPT-4 and LLaMA2. This study, central to the ASTOUND project (GA 101071191) dives into how well these AI models can track and interpret human mental states, an ability central to social interactions and communication.

Our team, alongside other prominent researchers, embarked on a comprehensive examination of theory of mind in both humans and AI. The study involved a series of tests designed to measure various aspects of theory of mind, including understanding false beliefs, interpreting indirect requests, and recognizing irony and faux pas.

We tested two families of LLMs (GPT-4 and LLaMA2) against a battery of measurements, comparing their performance with a sample of 1,907 human participants. This rigorous approach ensured a fair and systematic comparison between human and artificial intelligences.

The findings highlight that while AI models can mimic human-like reasoning in several theory of mind tasks, they also reveal distinct limitations and biases. For instance, GPT models often adopt a hyperconservative approach, hesitating to commit to conclusions without sufficient evidence, which contrasts with human tendencies to make more definitive judgments.

This study was a collaborative effort involving experts from various institutions, including our own team. Our involvement was crucial in designing and conducting the experiments, analyzing the data, and interpreting the results.

The insights gained from this research are invaluable for future developments in AI. Understanding the nuances of how AI models process social information can guide the creation of more sophisticated and human-like AI systems. It also opens avenues for further research into mitigating biases and improving the robustness of AI’s social reasoning abilities.

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