arXiv:2606.04915v1 Announce Type: new Abstract: Large language models reach 50 to 70% accuracy on causal reasoning benchmarks such as CLadder, but it is unclear whether this reflects structural reasoning or lexical pattern matching. We introduce Caliper, a controlled perturbation that replaces semantic variable names with placeholder tokens while preserving the causal graph and probabilistic specification of each question. Across nine instruction-tuned LLMs from 3.8B to 671B and three causal reasoning benchmarks, lexical anonymization yields robust accuracy drops of +7.6, +27.0, and +11.1 pp o
This paper leverages advanced LLMs and rigorous testing methodologies to explore the fundamental mechanisms behind their apparent reasoning capabilities.
Understanding whether LLMs perform true causal reasoning or sophisticated pattern matching has profound implications for their development, deployment, and trustworthiness in critical applications.
The findings suggest a significant reliance of LLMs on lexical cues, indicating a current limitation in their ability for abstract structural reasoning, which will guide future AI research and model architecture.
- · AI researchers focusing on explainability
- · Developers of more robust, lexically independent AI models
- · Companies seeking verifiable AI logic
- · Providers of 'black box' LLMs without transparency into reasoning
- · Applications requiring high-stakes causal inference without oversight
This research provides a novel method ('Caliper') for probing LLM reasoning beyond superficial performance metrics.
It will likely lead to a renewed focus on neural network architectures that can abstract causal graphs from data more effectively.
The insights could push the frontier of AI toward systems capable of provable, structural understanding rather than just statistical correlation.
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Read at arXiv cs.CL