A novel KT framework that leverages the reasoning ability of large language models (LLMs) to enhance the modelling of unseen students, without relying on numerous students with historical records is proposed and validated to achieve robust predictive performance compared to others.
Lin-Qing Li, Xuri Ge, Ying Zhou et al.· Proceedings of the 35th ACM...· 0 citations
Multimodal sentiment analysis (MSA) aims to infer sentiment by integrating linguistic, acoustic, and visual information. However, many fusion methods integrate modality-specific representations and cross-modal interactions into joint representations, making it difficult to determine whether predictive utility arises fr...
Xiao-Ting Xie, Feng Qiao, Xian-Wei Zhang et al.· 0 citations
ZS-CIR aims to retrieve a target image from a reference image and a modification text without paired supervision, typically by encoding composed queries as text-dominant representations within the image-text matching space of VLPs. However, queries reconstructed by visual pseudo-word learning or MLLM-based target reaso...
Xuri Ge, Chun-Hao Wang, Junchen Fu et al.· 0 citations
Analysis shows that effective QE induces layer-concentrated changes in sparse latents aligned with retrieval intent and entity attributes, rather than only perturbing final query embeddings, and suggests that SAEs can explain QE effects and offer a lightweight option for precise retrieval behavior modulation without qu...
Fang Dong, Wei-Ran Shi, Zhi-Wei Xu et al.· 0 citations
This survey reviews core tasks, representative methods, and datasets in multimodal facial state analysis, focusing on facial expression recognition, AU detection, and face-based soft biometric estimation, and emphasizing the unique value of language in providing contextual semantics, enhancing reasoning, and generating...
Xuri Ge, Tian-Shuo Zhang, Ruihan Li et al.· 0 citations
AdaRAS (Adaptive Reasoning Activation Steering), a lightweight test-time framework that improves reasoning reliability by selectively intervening on neuron activations during inference, enhancing incorrect reasoning traces while avoiding degradation on already-correct cases.
Fang Dong, Zu-Ming Yan, Xuri Ge et al.· arXiv.org· 5 citations
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