New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
A Framework for Fine-Tuning LLMs using Heterogeneous Feedback
Ryan Aponte, Ryan A. Rossi, Shunan Guo, Franck Dernoncourt, Tong Yu, Xiang Chen, Subrata Mitra, Nedim Lipka
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak
DeepNetBeam: A Framework for the Analysis of Functionally Graded Porous Beams
Mohammad Sadegh Eshaghi, Mostafa Bamdad, Cosmin Anitescu, Yizheng Wang, Xiaoying Zhuang, Timon Rabczuk
MAO: A Framework for Process Model Generation with Multi-Agent Orchestration
Leilei Lin, Yumeng Jin, Yingming Zhou, Wenlong Chen, Chen Qian
The Artificial Intelligence Disclosure (AID) Framework: An Introduction
Kari D. Weaver
Metareasoning in uncertain environments: a meta-BAMDP framework
Prakhar Godara, Tilman Diego Aléman, Angela J. Yu
An Efficient and Effective Transformer Decoder-Based Framework for Multi-Task Visual Grounding
Wei Chen, Long Chen, Yu Wu
CommonUppRoad: A Framework of Formal Modelling, Verifying, Learning, and Visualisation of Autonomous Vehicles
Rong Gu, Kaige Tan, Andreas Holck Høeg-Petersen, Lei Feng, Kim Guldstrand Larsen
Temporal Feature Matters: A Framework for Diffusion Model Quantization
Yushi Huang, Ruihao Gong, Xianglong Liu, Jing Liu, Yuhang Li, Jiwen Lu, Dacheng Tao
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir, Philippe Jouvet, Vincent Michalski, Samira Ebrahimi Kahou
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Yuhang Yao, Han Jin, Alay Dilipbhai Shah, Shanshan Han, Zijian Hu, Yide Ran, Dimitris Stripelis, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention
Ahan Gupta, Yueming Yuan, Devansh Jain, Yuhao Ge, David Aponte, Yanqi Zhou, Charith Mendis
A Framework for Pupil Tracking with Event Cameras
Khadija Iddrisu, Waseem Shariff, Suzanne Little