Tag: Multimodal Systems
All the articles with the tag "Multimodal Systems".
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Large Language Models Think Too Fast To Explore Effectively
Published: at 11:17 AM
71.14 🤔本文通过《Little Alchemy 2》游戏评估大型语言模型（LLMs）的探索能力，发现大多数LLMs因过早决策和过度依赖不确定性驱动策略而表现不如人类，但o1和DeepSeek-R1通过平衡赋能和深入推理显著超越人类，揭示了推理深度和架构设计对开放性探索的重要性。
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TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs
Published: at 04:29 PM 70.02 🤔本文提出TeLLMe，一种能量高效的三元LLM FPGA加速器，通过表查找矩阵引擎和反向注意力优化，支持预填充和解码阶段，在7W功率下实现高达9.51 tokens/s吞吐量和低预填充延迟。
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Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning
Published: at 11:19 AM 69.75 🤔本文提出Nemotron-Research-Tool-N1，通过基于规则的强化学习和二元奖励函数训练工具调用语言模型，在不依赖标注推理轨迹的情况下显著提升工具调用能力，实验表明其在多个基准上超越GPT-4o等强基线。
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How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game
Published: at 10:59 AM 68.75 🤔This paper introduces MM-Escape, a benchmark using the customizable 3D environment EscapeCraft to evaluate multimodal reasoning in MLLMs through room escape tasks, revealing that while models like GPT-4o achieve high success in simple scenarios, performance drops significantly with increased difficulty, exposing distinct limitations in reasoning and spatial awareness.
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Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models
Published: at 12:17 AM 68.20 🤔本文提出MAET方法，通过提取语言无关的能力相关权重并跨语言转移，构建多语言能力增强的大型语言模型，在数学和科学任务上以60%的计算资源实现约10%的性能提升，优于多种基线方法。