@inproceedings{chen-etal-2026-evidence,
title = "Evidence-Guided Bidirectional Iterative Reasoning for Interpretable Personality Recognition",
author = "Chen, Tinghao and
Song, Yuxuan and
Xiang, Mengyu and
Li, Qiudan and
Wu, Shu and
Zeng, Daniel Dajun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1770/",
doi = "10.18653/2026.findings-emnlp.1770",
pages = "37877--37896",
ISBN = "979-8-89176-462-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Evidence-Guided Bidirectional Iterative Reasoning for Interpretable Personality Recognition
%A Chen, Tinghao
%A Song, Yuxuan
%A Xiang, Mengyu
%A Li, Qiudan
%A Wu, Shu
%A Zeng, Daniel Dajun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-evidence
%X 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.
%R 10.18653/2026.findings-emnlp.1770
%U https://aclanthology.org/2026.findings-emnlp.1770/
%U https://doi.org/10.18653/2026.findings-emnlp.1770
%P 37877-37896
Markdown (Informal)
[Evidence-Guided Bidirectional Iterative Reasoning for Interpretable Personality Recognition](https://aclanthology.org/2026.findings-emnlp.1770/) (Chen et al., Findings 2026)
ACL