40 citations · 116 across the 23 of their papers we have counts for
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cs.CL2025
Are Language Models Efficient Reasoners? A Perspective from Logic Programming
Andreas Opedal, Yanick Zengaffinen, Haruki Shirakami +5
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: efficiency…
cs.CL2025
Transformers Can Learn Connectivity in Some Graphs but Not Others
Amit Roy, Abulhair Saparov
Reasoning capability is essential to ensure the factual correctness of the responses of transformer-based Large Language Models (LLMs), and robust reasoning about transitive relati…
cs.CL2025
Language Models Might Not Understand You: Evaluating Theory of Mind via Story Prompting
Nathaniel Getachew, Abulhair Saparov
We introduce StorySim, a programmable framework for synthetically generating stories to evaluate the theory of mind (ToM) and world modeling (WM) capabilities of large language mod…