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arXiv · 2507.22457

What is an "Abstract Reasoner"? Revisiting Experiments and Arguments about Large Language Models

Tian Yun·Chen Sun·Ellie Pavlick
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Abstract

Recent work has argued that large language models (LLMs) are not "abstract reasoners", citing their poor zero-shot performance on a variety of challenging tasks as evidence. We revisit these experiments in order to add nuance to the claim. First, we show that while LLMs indeed perform poorly in a zero-shot setting, even tuning a small subset of parameters for input encoding can enable near-perfect performance. However, we also show that this finetuning does not necessarily transfer across datasets. We take this collection of empirical results as an invitation to (re-)open the discussion of what it means to be an "abstract reasoner", and why it matters whether LLMs fit the bill.

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Tian Yun, Chen Sun, Ellie Pavlick. 2025-07-30. What is an "Abstract Reasoner"? Revisiting Experiments and Arguments about Large Language Models. https://arxiv.org/abs/2507.22457

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