arXiv AI

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

arXiv:2606. 29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment.

Hugging Face Trending Papers

Jun 29

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of interviewees in AVI.

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arXiv AI

Aug 11

EMMR: Emotion-Mediated Multimodal Reasoning for Personality Assessment in Asynchronous Video Interviews

arXiv:2608. 07512v1 Announce Type: cross Abstract: Asynchronous Video Interviews (AVIs) have become increasingly popular for personality assessment.

By Dongsheng Hu, Tianyi Zhang, Chuang Liu, Yuan Zong Yong Li, Wenming Zheng, Xiu-xiu Zhan

llms multimodal

arXiv AI

Jun 11

Frozen Multimodal Embeddings for Personality and Cognitive Ability Assessment in Asynchronous Video Interviews

arXiv:2606. 11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.

By Kuo-En Hung, Hung-Yue Suen, Shih-Ching Yeh, Hsiang-Wen Wang

rag fine-tuning multimodal benchmarks

arXiv AI

Jun 12

Frozen Multimodal Embeddings for AI-Assisted Interview Assessment of Personality and Cognitive Ability

arXiv:2606. 11930v2 Announce Type: replace-cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging problem in AI-assisted interview assessment because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.

By Kuo-En Hung, Hung-Yue Suen, Shih-Ching Yeh, Hsiang-Wen Wang

rag fine-tuning multimodal benchmarks

arXiv AI

Jul 17

Knowing You at First Glance: Inferring Apparent Personality from Faces

arXiv:2607. 14631v1 Announce Type: cross Abstract: Inferring apparent personality from facial images is important in social scenarios for embodied agents in human-robot interaction.

By Shuhuan Chen, Xiangyu Zhu, Weisong Zhao, Haichao Shi, Xiao-Yu Zhang, Zhen Lei

llms agents robotics multimodal benchmarks

arXiv AI

Jun 10

Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models

arXiv:2606. 11074v1 Announce Type: cross Abstract: With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under complex personality conditions is essential.

By Peiqi Jia (Xi'an Jiaotong University), Haonan Jia (Beihang University), Ziqi Miao (Beihang University), Linkang Du (Xi'an Jiaotong University), Yuntao Wang (Xi'an Jiaotong University), Zhou Su (Xi'an Jiaotong University)

llms nlp multimodal

arXiv AI

Jul 15

HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition

arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.

By Aleksei Bakin, Andrey V. Savchenko

fine-tuning multimodal safety

arXiv AI

Jul 22

Why Do Vision Language Models Struggle To Recognize Human Emotions?

arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.

By Madhav Agarwal, Sotirios A. Tsaftaris, Laura Sevilla-Lara, Steven McDonagh

llms multimodal safety

arXiv AI

Jul 31

Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests

arXiv:2510. 22170v3 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation.

By Alexandra Yost, Shreyans Jain, Shivam Raval, Grant Corser, Allen Roush, Nina Xu, Jacqueline Hammack, Ravid Shwartz-Ziv, Amirali Abdullah

llms benchmarks

arXiv AI

Jun 16

MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs

arXiv:2606. 15694v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in understanding complex multimodal content.

By Hangling Xie

llms nlp multimodal benchmarks

arXiv AI

Jul 17

Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

arXiv:2607. 14702v1 Announce Type: cross Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles.

By Elena Ryumina (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Maxim Markitantov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Alexandr Axyonov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Fedor Shchetinin (HSE University, St. Petersburg, Russia), Timur Abdulkadirov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Dmitry Ryumin (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Alexey Karpov (St. Petersburg Federal Research Center of the Russian Academy of Sciences)

multimodal

arXiv Machine Learning

Jun 3

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals

arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.

By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen

multimodal benchmarks

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