Results 301 to 310 of about 1,512,342 (361)

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Annual Meeting of the Association for Computational Linguistics
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Yongqiang Ma   +5 more
semanticscholar   +1 more source

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Trans. Mach. Learn. Res.
Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs.
Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, S. Zhang   +4 more
semanticscholar   +1 more source

LESS: Selecting Influential Data for Targeted Instruction Tuning

International Conference on Machine Learning
Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots. However, real-world applications often require a specialized suite of skills (e.g., reasoning).
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen   +4 more
semanticscholar   +1 more source

Tuning in action

Proceedings of the 16th International Conference on Extending Database Technology, 2013
Imagine that your database has all the right indexes. Its buffer manager has been tuned to give a high hit ratio, the buffer fits in RAM, and the data is well distributed on disk. You're done, right? Well, no, because the application code might be poorly written. It might include delinquent design patterns.
Wei Cao, Dennis E. Shasha
openaire   +1 more source

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

International Conference on Machine Learning
Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring ...
Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu   +4 more
semanticscholar   +1 more source

Generative Representational Instruction Tuning

arXiv.org
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to ...
Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, Douwe Kiela   +7 more
semanticscholar   +1 more source

Tunes on the table

Multimedia Systems, 2006
The Music Table is an augmented reality system for composing music by manipulating objects on a tabletop as a physicalized representation of the music being heard. Educational theory, and the apparent success of related applications in various learning contexts, seems to support this idea.
Rodney Berry, Mao Makino, Naoto Hikawa, Masami Suzuki, Naomi Inoue   +4 more
openaire   +1 more source

Staying in Tune

IEEE Engineering in Medicine and Biology Magazine, 2008
When performing daily life activities, appropriate sensory-motor transformations are required to successfully map the changing relationships among one's self, the environment, and objects moving in the environment. Our daily actions involve varying combinations of head-eye (gaze), arm-reaching, and whole-body (stepping and walking) movements.
Aimee, Betker, Tony, Szturm, Zahra M K, Moussavi   +2 more
openaire   +2 more sources

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Conference on Empirical Methods in Natural Language Processing
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training.
Zorik Gekhman, G. Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, Jonathan Herzig   +6 more
semanticscholar   +1 more source

SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning

AAAI Conference on Artificial Intelligence
Recent development in Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) have achieved superior performance and generalization capabilities, covered extensive areas of traditional tasks.
Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, Wenmeng Zhou, Yingda Chen   +11 more
semanticscholar   +1 more source