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  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • 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
  • Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND QWEN-VL: A . . .
    In this paper, we explore a way out and present the newest members of the open-sourced Qwen fam-ilies: Qwen-VL series Qwen-VLs are a series of highly performant and versatile vision-language foundation models based on Qwen-7B (Qwen, 2023) language model We empower the LLM base-ment with visual capacity by introducing a new visual receptor including a language-aligned visual encoder and a
  • Instance-adaptive Zero-shot Chain-of-Thought Prompting
    Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e g , GSM8K, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing
  • ADIFF: Explaining audio difference using natural language
    We evaluate our model using objective metrics and human evaluation and show our model enhancements lead to significant improvements in performance over naive baseline and SoTA Audio-Language Model (ALM) Qwen Audio
  • Junyang Lin - OpenReview
    Junyang Lin Pronouns: he him Principal Researcher, Qwen Team, Alibaba Group Joined July 2019
  • Retraining-Free Merging of Sparse Mixture-of-Experts via. . .
    We validate our approach through extensive experiments on eight zero-shot language tasks and demonstrate its effectiveness in large-scale SMoE models such as Qwen and Mixtral Our comprehensive results demonstrate that HC-SMoE consistently achieves strong performance, which highlights its potential for real-world deployment
  • Towards Understanding Distilled Reasoning Models: A. . .
    To explore this, we train a crosscoder on Qwen-series models and their fine-tuned variants Our results suggest that the crosscoder learns features corresponding to various types of reasoning, including self-reflection and computation verification
  • 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
  • LiveVQA: Assessing Models with Live Visual Knowledge
    We introduce LiveVQA, an automatically collected dataset of latest visual knowledge from the Internet with synthesized VQA problems LiveVQA consists of 3,602 single- and multi-hop visual questions from 6 news websites across 14 news categories, featuring high-quality image-text coherence and authentic information Our evaluation across 15 MLLMs (e g , GPT-4o, Gemma-3, and Qwen-2 5-VL family





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