Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Björn Engdahl, Adrian Kosowski, Jan Chorowski +6 authors
A 150M-parameter reasoning model using recurrent latent reasoning and in-context learning achieves a new cost-accuracy frontier on ARC-AGI-1.
Jingqi Tong, Yurong Mou, Hangcheng Li +11 authors
The "Thinking with Video" paradigm enhances multimodal reasoning by integrating video generation models, demonstrated through the Video Thinking Benchmark and improved performance on both vision and text tasks.
Mo Yu, Lemao Liu, Junjie Wu +5 authors
A study investigates whether large language models understand physical concepts through a grid-based task, showing that they significantly underperform compared to humans and highlighting the stochastic parrot phenomenon.
Tianzhu Ye, Li Dong, Yuqing Xia +4 authors
Diff Transformer improves large language models by selectively focusing attention on relevant context and reducing noise, leading to better performance in scaling, long-context modeling, key information retrieval, and in-context learning.
Yuzhang Tian, Jianbo Zhao, Haoyu Dong +8 authors
SpreadsheetLLM introduces SheetCompressor and Chain of Spreadsheet to enhance LLMs' performance on spreadsheet tasks through efficient encoding and understanding.
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +28 authors
A study of Multimodal Large Language Models finds a mix of pre-training data and image encoder design crucial for SOTA performance on multimodal benchmarks.
Shen Nie, Fengqi Zhu, Zebin You +7 authors
LLaDA, a diffusion model trained from scratch, outperforms autoregressive models in benchmarks and demonstrates strong instruction-following capabilities, challenging the dominance of ARMs in LLMs.
Le Xue, Manli Shu, Anas Awadalla +24 authors
xGen-MM, an extension of Salesforce's xGen initiative, provides a framework for developing Large Multimodal Models with pre-trained, instruction-tuned, and safety-tuned variants.
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan +3 authors
A new 1.3 billion parameter Transformer-based language model, phi-1.5, demonstrates comparable performance to much larger models on common sense reasoning and complex tasks despite the absence of web data.
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov +3 authors
XLand-100B is a large-scale dataset for in-context reinforcement learning, containing extensive learning histories in the XLand-MiniGrid environment.
Yaroslav Aksenov, Nikita Balagansky, Sofia Maria Lo Cicero Vaina +3 authors
A modification to the Based model kernel enhances its in-context learning capabilities, outperforming existing subquadratic architectures in language modeling tasks on the Pile dataset.
Ruibin Yuan, Hanfeng Lin, Shuyue Guo +54 authors
YuE, a family of open foundation models based on LLaMA2, can generate long-form music with aligned lyrics, coherent structure, and appropriate accompaniment using innovative techniques in next-token prediction, conditioning, and pre-training.
Mo Yu, Tsz Ting Chung, Chulun Zhou +8 authors
A benchmark called PRELUDE evaluates long-context understanding by assessing the consistency of prequel stories with original books, revealing significant challenges for models compared to humans.
Hongcheng Gao, Zihao Huang, Lin Xu +11 authors
The Turing Eye Test evaluates MLLMs' perceptual capabilities on synthetic images, revealing that vision tower generalization is a key gap compared to human perception.
Zeyi Sun, Ziyang Chu, Pan Zhang +6 authors
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
Zhongyang Li, Ziyue Li, Tianyi Zhou
A novel test-time optimization method, C3PO, improves the accuracy and efficiency of Mixture-of-Experts Large Language Models by re-weighting experts based on surrogate objectives from neighboring samples.
Praveen K Kanithi, Clément Christophe, Marco AF Pimentel +7 authors
MEDIC framework evaluates Large Language Models across five clinical dimensions to guide model selection in healthcare applications, identifying performance trade-offs and ensuring practical implementation.
Wei Chen, Zhiyuan Li, Zhen Guo +1 authors
An on-device Planner-Action framework, utilizing Phi-3 Mini for planning and Octopus for execution, employs model fine-tuning and multi-LoRA training to enhance efficiency and multi-domain handling on resource-constrained devices.
Yinghao Ma, Anders Øland, Anton Ragni +40 authors
A review of foundation models in music, including large language models and latent diffusion models, highlights their impact, architectural choices, and the need for ethical considerations in music applications.
Roee Hendel, Mor Geva, Amir Globerson
In-Context Learning in Large Language Models can be understood as compressing a training set into a task vector that modulates a transformer for output generation.
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