Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation

[Submitted on 15 Jul 2025 (v1), last revised 10 Dec 2025 (this version, v2)] View a PDF of the paper ...
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[2510.20059] Enhancing Reasoning Skills in Small Persian Medical Language Models Can Outperform Large-Scale Data Training

[Submitted on 22 Oct 2025 (v1), last revised 10 Dec 2025 (this version, v4)] View a PDF of the paper ...
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Context-Aware Attention Modulation for Better Multimodal In-Context Learning

[Submitted on 21 May 2025 (v1), last revised 8 Dec 2025 (this version, v3)] Authors:Yanshu Li, Jianjiang Yang, Ziteng Yang, ...
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Towards Stable and Efficient Transformer Training via Hybrid Normalization

[Submitted on 6 Mar 2025 (v1), last revised 8 Dec 2025 (this version, v4)] View a PDF of the paper ...
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[2409.17120] Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer

[Submitted on 25 Sep 2024 (v1), last revised 8 Dec 2025 (this version, v2)] Authors:Benji Peng, Xuanhe Pan, Yizhu Wen, ...
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Benchmarking Language Models on Multi-turn Mental Health Support

[Submitted on 23 Nov 2025 (v1), last revised 5 Dec 2025 (this version, v3)] View a PDF of the paper ...
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The Failure of Instruction Hierarchies in Large Language Models

[Submitted on 21 Feb 2025 (v1), last revised 4 Dec 2025 (this version, v4)] View a PDF of the paper ...
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AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition

arXiv:2512.03794v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved remarkable success in visual question answering tasks, but their reliance ...
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Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation

[Submitted on 16 May 2025 (v1), last revised 3 Dec 2025 (this version, v3)] View a PDF of the paper ...
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Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities

arXiv:2512.02973v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) show remarkable capabilities, their safety alignments are susceptible to ...
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