Winnie Street
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Evolutionary relationships between entities within an ecological niche are characterised by varying degrees of interdependence and resulting forms of symbiotic, predatory or competitive behaviors. This paper hypothesizes that mutual prediction is a defining factor in the kind of relationship which forms between entities, as well as the power distribution and stability of that relationship. Throughout history, humans have engaged in complex mutually predictive relationships with the animals we domesticate, the plants we eat and the tools we create. We have generally had a better predictive model of the entities we have co-evolved with than they have had of us. In AI we encounter the first entity which may be able to predict us - including our thoughts, beliefs, feelings and plans - better than we can predict it. The current state of human predictive advantage may give way to predictive equilibrium or even human out-prediction by AIs. This paper defines a classification system for degrees of mutual prediction in human-AI interactions ranging from rules-based prediction through to a speculative capacity for mindreading, and uses the classification as axes to map human predictive ability against AI predictive ability. Past, present, and speculated future relationships between humans and AIs are plotted on the map, encompassing cases of predictive imbalance in both directions and exploring the implications of mutual prediction for human-AI coevolutionary paths. The examples highlight possible sources of human-AI misalignment and the mutual prediction framework provides a lens through which to understand AI systems as part of evolutionary processes at large.
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LLMs achieve adult human performance on higher-order theory of mind tasks
Oliver Siy
Geoff Keeling
Benjamin Barnett
Michael McKibben
Tatenda Kanyere
Robin I.M. Dunbar
Frontiers in Human Neuroscience (2025)
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This paper examines the extent to which large language models (LLMs) are able to perform tasks which require higher-order theory of mind (ToM)–the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite – Multi-Order Theory of Mind Q&A – and using it to compare the performance of five LLMs of varying sizes and training paradigms to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on our ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences. Our results suggest that there is an interplay between model size and finetuning for higher-order ToM performance, and that the linguistic abilities of large models may support more complex ToM inferences. Given the important role that higher-order ToM plays in group social interaction and relationships, these findings have significant implications for the development of a broad range of social, educational and assistive LLM applications.
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Large language models (LLMs) are transforming human-computer interaction and conceptions of artificial intelligence (AI) with their impressive capacities for conversing and reasoning in natural language. There is growing interest in whether LLMs have theory of mind (ToM); the ability to reason about the mental and emotional states of others that is core to human social intelligence. As LLMs are integrated into the fabric of our personal, professional and social lives and given greater agency to make decisions with real-world consequences, there is a critical need to understand how they can be aligned with human values. ToM seems to be a promising direction of inquiry in this regard. Following the literature on the role and impacts of human ToM, this paper identifies key areas in which LLM ToM will show up in human:LLM interactions at individual and group levels, and what opportunities and risks for alignment are raised in each. On the individual level, the paper considers how LLM ToM might manifest in goal specification, conversational adaptation, empathy and anthropomorphism. On the group level, it considers how LLM ToM might facilitate collective alignment, cooperation or competition, and moral judgement-making. The paper lays out a broad spectrum of potential implications and suggests the most pressing areas for future research.
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