Identifying personality accurately can improve interpretability and adaptability for human–computer interaction and psychological assessment. Recent studies have introduced evidence utterance into large language models (LLMs) for personality recognition, providing supporting evidence to explain personality labels. However, the mixed background and social expressions in dialogue scenarios interfere with the identification of evidence utterance. At the same time, the overconfidence of LLMs leads to biased personality inference. Inspired by the lens theory in psychology, how to systematically explore the mutual influence between evidence utterance identification and personality recognition while improving the performance of both tasks remains a key challenge. In this paper, we reframe personality recognition as an Evidence-Personality Iterative Reasoning problem (EPIR). A graph model is employed to select an initial evidence utterance set, and personality recognition is performed based on a Generative-Discriminative Feedback mechanism. Then, the inferred results are fed back to the graph mapping network to dynamically adjust evidence utterance filtering. This interactive iterative process continues until the evidence and personality results across different iterations converge. Experimental results on the two public datasets demonstrate the effectiveness of the proposed method.

It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimate problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach.