## 1 Introduction  
Large language models are pre-trained on vast amounts of data and are utilized for various tasks such as machine translation, sentiment analysis, and document summarization. Generative language models, such as GPT-3 [1] and LLMA [2], in particular, have played a central role in the field of natural language processing and have made significant progress in recent years. However, despite the rapid progress of LLMs, their general-purpose nature can lead to suboptimal performance compared to models specialized for specific domains or tasks (e.g., code generation [3], medical applications [4, 5], and style-based literature writing [6]). While current research on novel generation using LLMs primarily focuses on maintaining consistent story plots [7]. In contrast, relatively little research has focused on the consistency of character presentation. Existing approaches to character-centered story-telling focus only on aligning character actions with consistent character presentation [8], and do not consider personality-level consistency in character utterances. When reading a novel, if the characters speak consistently with their personalities, it enhances the sense of realism; otherwise, the reader's satisfaction may decrease. Writersstrive to create realistic characters for their readers, but often characters with different personalities speak in similar ways. Prior studies have shown that an individual's linguistic style (idiolect) tends to be consistently reflected in their writing [9], and that an author's stylistic tendencies appear throughout a text, whether consciously or unconsciously [10]. It has also been reported that an author's stylistic tendencies may unintentionally manifest in character utterances, potentially weakening the unique characteristics of the characters [11]. We call this \"unconscious author style,\" that the writer unconsciously influences the character's thoughts, emotions, and utterances [12; 13]. That is, \"unconscious author style\" refers to the phenomenon in which an author's linguistic style unintentionally intrude upon the utterances of characters, thereby blurring or diluting their personalities. Minimizing such interference would facilitate the generation of utterances that align more closely with each character's personality traits, resulting in more plausible and consistent dialogue. As far as we know, there have been no studies aimed at reducing the \"unconscious author style\" on characters' utterances. Some previous studies have tried to generate character utterances, but they were only for free conversation, drama or games, without considering the \"unconscious author style\" issue [14; 15]. In this paper, we focus on the way of speaking (i.e., linguistic style) of characters in novels. Specifically, based on previous findings that the linguistic style has a strong relationship with personality [16], we design a new method that generates personality-consistent utterances of characters in novels. To achieve this goal, we use instruction-tuning to construct a new language model that is tuned to generate utterances that reflect the personalities of the target characters, while suppressing the influence of \"unconscious author style\" on their dialogue. To handle the personality of the characters, we adopt the Five-Factor Model of Personality (FFM), also known as the \"Big Five\" or OCEAN traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism [17]. Based on the theory of OCEAN traits, we assume that the characters' utterances reflect their distinct personalities even though they are not real people. To validate this, we manually annotated the OCEAN trait scores of novel characters, and generated two different character clusters: word-based clusters and personality-based clusters. The word-based clusters are obtained from the words of the characters' utterances, while the personality-based clusters are created from the characters' OCEAN trait scores.