Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, often attributed to inference patterns encoded in pre-training data and enhanced by techniques like Chain-of-Thought (CoT) prompting. However, a fundamental question arises: can core reasoning patterns—induction, deduction, abduction—be independently controlled and decoupled?
A new paper titled "Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models" tackles this issue head-on. The research explores whether LLMs can deliberately switch between reasoning modes or if they are rigidly bound to learned patterns.
Key insights include:
- Analytical breakdown of how LLMs handle distinct reasoning tasks.
- Data-backed observations on model performance across inductive, deductive, and abductive scenarios.
- Implications for AI safety and explainability, as controllable reasoning could lead to more transparent and trustworthy systems.
The findings suggest that while LLMs exhibit flexibility, there remains a tension between compliance with prompt instructions and the model's inherent "sensibility"—its tendency to rely on dominant inference patterns.
For those seeking deeper understanding, the full paper is available on Hugging Face. This research is part of a growing body of work aimed at making AI reasoning more controllable and interpretable.