The proliferation of Large Language Models (LLMs) has spurred extensive research into LLM-related Prompt investigations, such as Instruction Learning (IL), In-context Learning (ICL), and Chain-of-Thought (CoT). These approaches aim to improve LLMs' responses by enabling them to provide concise statements or examples for deeper contemplation when addressing questions. However, independent thinking by LLMs can introduce variability in their thought processes, leading to potential inaccuracies. In response, our study seeks to bridge the gap between LLM and human-like thinking processes, recognizing that text comprehension begins with understanding individual words. To tackle this challenge, we have expanded the CoT method to cater to a specific domain. Our approach, known as "Think from Words" (TFW), initiates the comprehension process at the word level and then extends it to encompass the entire text. We also propose "TFW with Extra word-level information" (TFW Extra), augmenting comprehension with additional word-level data. To assess our methods, we employ text classification on six Japanese datasets comprising text-level and word-level elements. Our findings not only validate the effectiveness of TFW but also shed light on the impact of various word-level information types on LLMs' text comprehension, offering insights into their potential to cause misinterpretations and errors in the overall comprehension of the final text.

我们的研究旨在弥合大语言模型（LLMs）和人类思维过程之间的差距，通过理解个别单词开始，将信息传达至整个文本，我们通过引入“Think from Words”（TFW）和“TFW with Extra word-level information”（TFW Extra）方法进行了实证研究，并验证了TFW在六个日语数据集上的有效性，同时也揭示了不同单词级信息类型对LLMs文本理解的影响，以及它们对最终文本整体理解中可能引起的误解和错误的见解。

从文字思考（TFW）：通过文字思考在大型语言模型中引发类似人类认知的方法，用于日文文本层级分类