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Can LLMs reason by analogy like humans? We investigate this question in a new paper published in the Journal of Memory and Language (link below). This was a long-running but very rewarding project. Here are a few thoughts on our methodology and main findings. 1/9

We focus on an important feature of analogical reasoning often called "re-representation" – the ability to dynamically select which features of analogs matter to make sense of the analogy (e.g. if one analog is "horse", which properties of horses does the analogy rely on?). 2/9

We tested humans & LLMs on analogical reasoning tasks that involve flexible re-representation. We strived to apply best practices from cognitive science – designing novel tasks to avoid data contamination, including careful controls, and doing proper statistical analysis. 3/9

In our first study, we tested whether LLMs could map semantic relationships between concepts to symbolic patterns. We included controls such as permuting the order of examples or adding semantic distractors to test for robustness and content effects (see full list below). 4/9

In our second study, we highlighted the role of semantic content. Here, the task required identifying specific properties of concepts (e.g., "Is it a mammal?", "How many legs does it have?") and mapping them to features of the symbol strings. 5/9

We found that the best-performing LLMs match human performance across many of our challenging new tasks. This provides evidence that sophisticated analogical reasoning can emerge from domain-general learning, where existing computational models fall short. 6/9

This opens intesting avenues for future work. By using causal intervention methods with open-weights models, we can start to reverse-engineer these emergent analogical abilities and compare the discovered mecanisms to computational models of analogical reasoning. 8/9