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LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model (2403.07581v1)

Published 12 Mar 2024 in cs.CL

Abstract: Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn post features directly by fine-tuning the pre-trained LLMs under the supervision of limited personality labels. This leads to inferior quality of post features and consequently affects the performance. In addition, they treat personality traits as one-hot classification labels, overlooking the semantic information within them. In this paper, we propose a LLM based text augmentation enhanced personality detection model, which distills the LLM's knowledge to enhance the small model for personality detection, even when the LLM fails in this task. Specifically, we enable LLM to generate post analyses (augmentations) from the aspects of semantic, sentiment, and linguistic, which are critical for personality detection. By using contrastive learning to pull them together in the embedding space, the post encoder can better capture the psycho-linguistic information within the post representations, thus improving personality detection. Furthermore, we utilize the LLM to enrich the information of personality labels for enhancing the detection performance. Experimental results on the benchmark datasets demonstrate that our model outperforms the state-of-the-art methods on personality detection.

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Authors (6)
  1. Linmei Hu (14 papers)
  2. Hongyu He (143 papers)
  3. Duokang Wang (2 papers)
  4. Ziwang Zhao (4 papers)
  5. Yingxia Shao (54 papers)
  6. Liqiang Nie (191 papers)
Citations (7)
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