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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
  • 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
  • Visual CoT: Advancing Multi-Modal Language Models with a. . .
    Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks However, they often lack interpretability and struggle with complex visual inputs, especially when the resolution of the input image is high or when the interested region that could provide key information for answering the question is
  • Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
    TL;DR: We propose Loss-Free Balancing, a novel MoE load balancing method that dynamically adjusts expert biases based on its recent load without relying on auxiliary losses, thereby avoiding interference gradients and achieving improved model performance
  • 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
  • MedJourney: Benchmark and Evaluation of Large Language Models over . . .
    Additionally, we evaluate three categories of LLMs against this benchmark: 1) proprietary LLM services such as GPT-4; 2) public LLMs like QWen; and 3) specialized medical LLMs, like HuatuoGPT2 Through this extensive evaluation, we aim to provide a better understanding of LLMs' performance in the medical domain, ultimately contributing to their
  • Evaluating the Instruction-following Abilities of Language Models. . .
    TL;DR: We create a benchmark to evaluate instruction following capability of Large Language Models using knowledge tasks and demonstrate that existing LLMs have room to improve
  • YARN: E CONTEXT WINDOW EXTENSION OF L MODELS - OpenReview
    Published as a conference paper at ICLR 2024 3 1 LOSS OF HIGH FREQUENCY INFORMATION - "NTK-AWARE" INTERPOLATION If we look at rotary position embeddings (RoPE) only from an information encoding perspective,





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