DKE: LLM-Based Domain Knowledge Enhancement for Comprehensible Personality Detection

Personality detection aims to identify personality traits of individuals based on social media posts. Many existing methods map words to psycholinguistic categories by utilizing Linguistic Inquiry and Word Count LIWC to introduce the domain knowledge required for personality detection. However, the categories of LIWC are static and lack a direct connection to personality detection, thereby limiting the model's ability to effectively leverage domain-specific knowledge. Trained on massive amounts of data, large language models LLMs possess extensive world knowledge, especially domain-specific knowledge that is beneficial to comprehending personality taxonomies. Inspired by this, we propose an LLM-based domain knowledge enhanced model to capture the implicit psycholinguistic knowledge in posts, achieving more accurate and comprehensible personality detection. Specifically, to leverage the LLM’s comprehensive knowledge base, we first input the posts into the LLM to obtain personality judgments and corresponding rationales, which are derived based on the core characteristics of each MBTI dimension. To better incorporate personality-related knowledge, the proposed model then conducts feature interactions between the rationales and the posts, generating text representations that better reflect personality traits. After that, the model adaptively adjusts the weights of the interactive features and aggregates them with the semantic features to form the final representations, based on which personality detection is performed. Experimental results on real-world datasets demonstrate the proposed model effectively improves the quality of user personality representation and outperforms baseline methods.