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  • Qwen-VL: A Versatile Vision-Language Model for Understanding . . .
    In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images Starting from the Qwen-LM as a foundation, we endow it with visual capacity by the meticulously designed (i) visual receptor, (ii) input-output interface, (iii) 3-stage training pipeline
  • Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND - OpenReview
    The overall network architecture of Qwen-VL consists of three components and the details of model parameters are shown in Table 1: Large Language Model: Qwen-VL adopts a large language model as its foundation component The model is initialized with pre-trained weights from Qwen-7B (Qwen, 2023)
  • LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation
    Remarkably, LLaVA-MoD-2B surpasses Qwen-VL-Chat-7B with an average gain of 8 8\%, using merely $0 3\%$ of the training data and 23\% trainable parameters The results underscore LLaVA-MoD's ability to effectively distill comprehensive knowledge from its teacher model, paving the way for developing efficient MLLMs
  • Alleviating Hallucination in Large Vision-Language Models with. . .
    To assess the capability of our proposed ARA model in reducing hallucination, we employ three widely used LVLM models (LLaVA-1 5, Qwen-VL, and mPLUG-Owl2) across four benchmarks Our empirical observations suggest that by utilizing fitting retrieval mechanisms and timing the retrieval judiciously, we can effectively mitigate the hallucination
  • You Know What Im Saying: Jailbreak Attack via Implicit Reference
    Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding $\textbf{90}$% on most models, including GPT-4o, Claude-3 5-Sonnet, and Qwen-2-72B Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method
  • Evaluating Hallucinations in Chinese Large Language Models
    For evaluation, we design an automated evaluation method using GPT-4 to judge whether a model output is hallucinated We conduct extensive experiments on 24 large language models, including ERNIE-Bot, Baichuan2, ChatGLM, Qwen, SparkDesk and etc Out of the 24 models, 18 achieved non-hallucination rates lower than 50%
  • Junyang Lin - OpenReview
    Junyang Lin Pronouns: he him Principal Researcher, Qwen Team, Alibaba Group Joined ; July 2019
  • Qwen2 Technical Report - OpenReview
    This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models We release a comprehensive suite of foundational and instruction-tuned
  • NEUROMORPHIC PRINCIPLES FOR EFFICIENT LARGE LANGUAGE MODELS ON INTEL . . .
    procedure from Qwen Team (2024) Table 1: Results from quantization of the 370M MatMul-free language model on GPU Baseline: optimized models from Zhu et al (2024) and Qwen Team (2024) PT: PyTorch-only implementa-tion Ax Wx: activations RMSNorm weights quantized to x-bit integers ϵ rms ↑: setting the value for ϵ rms to 10−3 from
  • Qwen2. 5 Technical Report - OpenReview
    In this report, we introduce Qwen2 5, a comprehensive series of large language models (LLMs) designed to meet diverse needs Compared to previous iterations, Qwen 2 5 has been significantly improved during both the pre-training and post-training stages





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