## 1 Introduction
We observed that the word-based clusters show a similar trend to the personality-based clusters, i.e., the characters with similar personality traits have similar word distributions in their utterances. Based on the finding of the above assumption, our hypothesis is that a language model trained on utterances of character clusters can translate a given utterance into a new utterance that better reflects the personality of a target character cluster. That is, even though a character's utterances may reflect the \"unconscious author style\" (i.e., linguistic patterns of the author), using the utterances of numerous similar characters from different novels will reduce the impact of the \"unconscious author style.\" This study does not aim to prevent changes in a character's personality, but rather focuses on addressing the problem of the unconscious author style unintentionally influencing character utterances. If this hypothesis holds, then the language model will contribute greatly to the novel-writing process. Suppose that a novelist has defined the personality of a main character in her/his novel, and she/he has found that the personality of the main character is close to the character cluster 'A'. When the main character needs to deliver a line like \"I never want to go back to my hometown,\" the language model is prompted to generate a translatedversion of the line that matches the personality of the cluster 'A', so that the translated utterance will better reflect the personality that the novelist intended. Figure 1 shows an example of such a translation, where the left conversation is adapted from the [PERSON] stories \"The Adventure of the Blue Carbuncle\" [18] and \"A Scandal in Bohemia\" [19] and the right conversation is a translated version using our language model. The bottom of Figure 1 shows the personality difference between the characters by representative per-trait keywords [20]. Note that we have two target character clusters in the right conversation, each represented by [PERSON] (_Mansfield Park_) and [PERSON] (_The Man Who Was Thursday_). The translated version is created by prompting our language model to translate the utterances of [PERSON] and [PERSON] into the utterances of [PERSON] and [PERSON], respectively. As shown in the translated conversation, the translated utterances seem to successfully reflect the personality of [PERSON] and [PERSON]. This study addresses the following research question: Can we generate character utterances that better reflect their personality by using language models to mitigate the influence of the unconscious author style? Our contributions can be summarized as follows. 1. We present a language model that is tuned for translating a given utterance into a new utterance that better reflects a desired personality in novel. As far as we know, this is the first language model designated for generating utterances reflecting personality in novels. 2. We propose a method for building our language model. It involves (1) clustering characters using utterance words, and (2) prompt design for instruction-tuning data preparation using the character clusters. Figure 1: An example of utterance translation. The conversation between [PERSON] and [PERSON] adapted from the [PERSON] stories [18; 19] (**left**) is translated into a conversation between two different characters (**right**). The lower table describes the personalities of the four characters by their per-trait keywords (all keywords can be found in Table 1).