Summary of Leveraging Unstructured Text Data For Federated Instruction Tuning Of Large Language Models, by Rui Ye et al.
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Modelsby Rui Ye, Rui…
Summary of Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models, by Xingyun Hong et al.
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Modelsby Xingyun Hong,…
Summary of Points: Improving Your Vision-language Model with Affordable Strategies, by Yuan Liu et al.
POINTS: Improving Your Vision-language Model with Affordable Strategiesby Yuan Liu, Zhongyin Zhao, Ziyuan Zhuang, Le…
Summary of Achieving Peak Performance For Large Language Models: a Systematic Review, by Zhyar Rzgar K Rostam et al.
Achieving Peak Performance for Large Language Models: A Systematic Reviewby Zhyar Rzgar K Rostam, Sándor…
Summary of Tc-llava: Rethinking the Transfer From Image to Video Understanding with Temporal Considerations, by Mingze Gao et al.
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerationsby Mingze Gao, Jingyu…
Summary of Game On: Towards Language Models As Rl Experimenters, by Jingwei Zhang et al.
Game On: Towards Language Models as RL Experimentersby Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki, Jost…
Summary of How Much Data Is Enough Data? Fine-tuning Large Language Models For In-house Translation: Performance Evaluation Across Multiple Dataset Sizes, by Inacio Vieira et al.
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation…
Summary of Fine-tuning Large Language Models For Domain Adaptation: Exploration Of Training Strategies, Scaling, Model Merging and Synergistic Capabilities, by Wei Lu and Rachel K. Luu and Markus J. Buehler
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and…
Summary of Boosting Generalizability Towards Zero-shot Cross-dataset Single-image Indoor Depth by Meta-initialization, By Cho-ying Wu et al.
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initializationby Cho-Ying Wu, Yiqi Zhong, Junying…
Summary of Booster: Tackling Harmful Fine-tuning For Large Language Models Via Attenuating Harmful Perturbation, by Tiansheng Huang et al.
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbationby Tiansheng Huang, Sihao…