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The proliferation of Large Language Models (LLMs) has spurred extensiveresearch into LLM-related Prompt investigations, such as Instruction Learning(IL), In-context Learning (ICL), and Chain-of-Thought (CoT). These approachesaim to improve LLMs' responses by enabling them to provide concise statementsor examples for deeper contemplation when addressing questions. However,independent thinking by LLMs can introduce variability in their thoughtprocesses, leading to potential inaccuracies. In response, our study seeks tobridge the gap between LLM and human-like thinking processes, recognizing thattext comprehension begins with understanding individual words. To tackle thischallenge, we have expanded the CoT method to cater to a specific domain. Ourapproach, known as "Think from Words" (TFW), initiates the comprehensionprocess at the word level and then extends it to encompass the entire text. Wealso propose "TFW with Extra word-level information" (TFW Extra), augmentingcomprehension with additional word-level data. To assess our methods, we employtext classification on six Japanese datasets comprising text-level andword-level elements. Our findings not only validate the effectiveness of TFWbut also shed light on the impact of various word-level information types onLLMs' text comprehension, offering insights into their potential to causemisinterpretations and errors in the overall comprehension of the final text.

Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification