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。
Yibin Wang, Zhiyu Tan, Junyan Wang +3 authors
LiFT fine-tunes text-to-video generative models using human feedback to improve video alignment with text descriptions, demonstrating superior performance over a larger model.
Yi Lu, Jing Nathan Yan, Songlin Yang +6 authors
A controlled evaluation protocol for long-context models reveals that exact fine-tuning methods are effective, while approximate attention methods underperform, and extrapolation remains challenging.
Yuri Kuratov, Aydar Bulatov, Petr Anokhin +3 authors
Recursively augmented GPT-2 fine-tuning significantly extends the processing capability of generative models to handle long document sequences beyond $10^6$ elements.
Nikhil Bhendawade, Irina Belousova, Qichen Fu +3 authors
Speculative Streaming is a method that incorporates drafting into a target language model, enhancing decoding speed without quality loss while using fewer parameters.
Jooyoung Choi, Chaehun Shin, Yeongtak Oh +2 authors
The Style-friendly SNR sampler modifies the noise level distribution during fine-tuning to improve style alignment in diffusion models, enabling better capture of unique artistic styles.
Lin Xu, Yilin Zhao, Daquan Zhou +3 authors
Pooling LLaVA improves video-language pre-trained models by smoothing high-norm visual features, achieving state-of-the-art performance in video question-answering, captioning, and benchmark tasks.
Fanqi Wan, Ziyi Yang, Longguang Zhong +3 authors
FuseChat extends the FuseLLM framework for knowledge fusion of chat LLMs through lightweight fine-tuning and parameter merging, achieving superior performance across various domains.
Keming Lu, Bowen Yu, Chang Zhou +1 authors
Ditto, a self-alignment method, enhances the role-playing capabilities of open-source large language models by fine-tuning them on a large generated dataset, achieving performance comparable to proprietary chatbots.
Lianghui Zhu, Xinggang Wang, Xinlong Wang
Large Language Models fine-tuned as scalable judges (JudgeLM) achieve state-of-the-art performance in evaluating open-ended benchmarks through a comprehensive dataset and benchmark, enhancing judgment efficiency and accuracy.
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji +1 authors
Self-play fine-tuning improves diffusion models' performance and alignment with human preferences using competition between model versions, outperforming supervised and RL-based methods with less data.
Haoran Xu, Young Jin Kim, Amr Sharaf +1 authors
A novel fine-tuning approach for moderate-sized language models significantly improves translation performance with less parallel data compared to traditional methods.
Quentin Garrido, Mahmoud Assran, Nicolas Ballas +3 authors
Image World Models extend Joint-Embedding Predictive Architecture to predict global photometric transformations, offering adaptable and high-performance representations through fine-tuning.
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster +2 authors
Experimentation with LLMs in multi-armed bandit environments reveals that robust exploration requires specific prompts, external summarization of history, or algorithmic interventions.
Houwen Peng, Kan Wu, Yixuan Wei +17 authors
A new FP8 automatic mixed-precision framework for training large language models reduces memory usage and increases speed compared to BF16 and Nvidia Transformer Engine.
Ke Wang, Houxing Ren, Aojun Zhou +7 authors
A method is presented for fine-tuning language models to solve math problems using code, achieving state-of-the-art results on MATH and GSM8K datasets.
Da Yin, Faeze Brahman, Abhilasha Ravichander +4 authors
Lumos, a modular language agent framework using open-source LLMs, achieves superior performance across various tasks and demonstrates enhanced generalization.
Octo Model Team, Dibya Ghosh, Homer Walke +15 authors
Octo, a large transformer-based policy trained on extensive robotic datasets, demonstrates versatility and efficient fine-tuning for diverse robotic platforms and tasks.
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +35 authors
Self-training with scalar feedback using expectation-maximization improves language model performance on tasks with verifiable answers, reducing reliance on human data.
Tao Ge, Jing Hu, Xun Wang +2 authors
The In-context Autoencoder (ICAE) compresses long contexts into memory slots for efficient LLM inference using a combination of pretraining and fine-tuning.
Yue Wu, Zhiqing Sun, Huizhuo Yuan +3 authors
A self-play method called SPPO for language model alignment achieves state-of-the-art performance by approximating Nash equilibrium policy in a constant-sum game setting, outperforming other approaches with limited data.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号