  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.
