We study the capabilities of Large Language Models (LLM) on binary relations, a ubiquitous concept in math employed in most reasoning, math and logic benchmarks. This work focuses on equality, inequality, and inclusion, along with the properties they satisfy, such as ir/reflexivity, a/symmetry, transitivity, and logical complexity (e.g., number of reasoning ``hops''). We propose an alternative to in-context learning that trains only the representations of newly introduced tokens, namely out-of-context representation learning. This method mitigates linguistic biases already present in a model and, differently from in-context learning, does not rely on external information or illustrations. We argue out-of-context representation learning as a better alternative to in-context learning and fine-tuning to evaluate the capabilities of LLMs on logic tasks that are the building blocks of more complex reasoning benchmarks.
View on arXiv@article{shaki2025_2503.10408,
title={ Understanding the Logical Capabilities of Large Language Models via Out-of-Context Representation Learning },
author={ Jonathan Shaki and Emanuele La Malfa and Michael Wooldridge and Sarit Kraus },
journal={arXiv preprint arXiv:2503.10408},
year={ 2025 }
}