LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
arxiv(2024)
摘要
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
language models 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
large language model (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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要