Chinese Grammar Error Correction (CGEC) aims to identify and correct grammatical errors in Chinese sentences. Fine-tuning Large Language Models (LLMs) is a popular current method. However, we have observed a significant flaw: LLMs learn grammatical knowledge but often fail to explicitly use specific grammatical concepts to correct erroneous sentences, leading to multiple corrections without a clear indication of which is the most reliable. Humans possess an "intuitive thinking" mode, which allows them to quickly decide which correction is more reliable based on experience and intuition. To address this deficiency in LLMs, we propose the Expanding Intuitive Thinking Model (ExIT). ExIT extends the thinking process of LLMs for CGEC, providing them with a human-like rapid decision-making process. This enables LLMs to quickly select a more reliable correction from multiple alternatives based on experience and intuition. Unlike the LLM decoding process, which focuses only on the trustworthiness of local tokens, this is a global thinking process concerning the erroneous sentence and its correction. ExIT is a lightweight model that performs rapid computations without significantly increasing overhead. Our experimental results on CGEC datasets demonstrate that the proposed ExIT can substantially unleash the error correction potential of LLMs.

Multiple-choice questions (MCQs) are a widely used and vital assessment format for evaluating large language models (LLMs). This study reveals that LLMs are susceptible to “cognitive load” caused by distractor options in MCQs, leading to excessive attention to distractors and consequent vacillation between correct and incorrect options. To mitigate this cognitive burden, we introduce a novel reasoning prompt strategy, called EoT, which effectively reduces cognitive load by steering the model’s attention away from erroneous options. This enables the model to focus more effectively on reasonable answers. Additionally, by documenting the elimination process, EoT enhances the transparency and interpretability of the model’s reasoning. Experimental results demonstrate that EoT, as a plug-and-play approach, significantly reduces cognitive load and improves performance, showcasing its potential to enhance both the accuracy and interpretability of LLMs.