Understanding the Logical Capabilities of Large Language Models via Out-of-Context Representation Learning

• URL: http://arxiv.org/abs/2503.10408v1
• Date: Thu, 13 Mar 2025 14:32:30 GMT
• Title: Understanding the Logical Capabilities of Large Language Models via Out-of-Context Representation Learning
• Authors: Jonathan Shaki, Emanuele La Malfa, Michael Wooldridge, Sarit Kraus,
• Abstract summary: This work focuses on equality, inequality, and inclusion, along with the properties they satisfy, such as ir/reflexivity, a/symmetry, transitivity, and logical complexity.<n>We propose an alternative to in-context learning that trains only the representations of newly introduced tokens, namely out-of-context representation learning.
• Score: 14.326344469446438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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