This article from Cohere for AI presents a comprehensive study on multilingual preference optimization
Multilingual Natural Language Processing (NLP) is a rapidly advancing field that aims to develop language models capable of understanding and ...
Tag: Optimization
Meta AI introduces Meta LLM Compiler: a next-generation LLM based on Code Llama with improved performance for code optimization and compiler reasoning
Software engineering has witnessed notable advancements with the development of large language models (LLMs). These models, trained on extensive data ...
DiffUCO: A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sampling complex, high-dimensional target distributions, such as the Boltzmann distribution, is crucial in many scientific fields. For example, predicting molecular ...
Demonstration Iterated Task Optimization (DITTO): A new AI method that aligns language model results directly with demonstrated user behaviors
Language models (LMs) are designed to reflect a wide range of voices, generating results that do not perfectly match any ...
Quantized Eigenvector Matrices for 4-bit Second-Order Optimization of Deep Neural Networks
Deep neural networks (DNNs) have achieved notable success in several fields, including computer vision, natural language processing, and speech recognition. ...
This AI article introduces KernelSHAP-IQ: Weighted Least Squares Optimization for Shapley Interactions
Machine learning interpretability is a critical area of research for understanding the decision-making processes of complex models. These models are ...
Iterative preference optimization to improve reasoning tasks in language models
Iterative preference optimization methods have demonstrated effectiveness in general instruction tuning tasks, but produce limited improvements in reasoning tasks. These ...
Talaria: Interactive Optimization of Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves cloud computing to personal devices, protecting user privacy and enabling intelligent user experiences. However, tailoring ...
The Slingshot Effect: A Late-Stage Optimization Anomaly in the Adam Family of Optimization Methods
Adaptive gradient methods, particularly Adam, have become indispensable for optimizing neural networks, particularly in conjunction with Transformers. In this paper, ...
Dataset Reset Policy Optimization (DR-PO) – a machine learning algorithm that exploits the ability of a generative model to reset offline data to improve RLHF from preference-based feedback
Reinforcement learning (RL) continually evolves as researchers explore methods to refine algorithms that learn from human feedback. This domain of ...
