Interpreting Reasoning of Large Language Models via Partial Information Decomposition

Research introduces SLIDER, a novel interpretability framework for Large Reasoning Models (LRMs). LRMs, despite their capability in complex problem-solving, often generate lengthy, repetitive, or flawed reasoning. SLIDER utilizes Partial Information Decomposition (PID) to analyze the information flow between consecutive reasoning steps, disentangling unique, redundant, and synergistic information to assess the quality of the reasoning process. This framework proposes a 'Step-wise Repetitive Reasoning Index (Step-RRI)' for theoretical evaluation.

The development of interpretability frameworks like SLIDER is critical for enhancing the reliability and trustworthiness of AI systems, particularly Large Reasoning Models. Improved understanding of model reasoning allows for better identification and mitigation of errors, thereby accelerating the responsible deployment of advanced AI in sensitive and complex applications.

Large Reasoning Models (LRMs) are effective in complex mathematical problem-solving but suffer from issues like lengthy, repetitive, or erroneous reasoning outputs.

Forward consideration, not a verified fact.