From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
This addresses the issue of unreliable reasoning in language models for applications requiring coherent justifications, though it is incremental as it builds on existing cognitive theories and model fine-tuning methods.
The paper tackled the problem of pre-trained language models generating incoherent or spurious reasoning by incorporating human-like dual-process strategies (heuristic and analytic thinking) into fine-tuning and in-context learning, resulting in state-of-the-art performance on the TRIP benchmark for physical commonsense reasoning.
Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory information. In real-world applications, PLMs should justify decisions with formalized, coherent reasoning chains, but this challenge remains under-explored. Cognitive psychology theorizes that humans are capable of utilizing fast and intuitive heuristic thinking to make decisions based on past experience, then rationalizing the decisions through slower and deliberative analytic reasoning. We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive Physics (TRIP). We also find that this improved coherence is a direct result of more faithful attention to relevant language context in each step of reasoning. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.