Feb 15, 2024 · Empirical evidence demonstrates the efficacy of our method in aligning both Large Language Models (LLMs) and diffusion models to accommodate ...
Jun 5, 2024 · In this paper, we introduce Rewards-in-Context (RiC), which conditions the response of a foundation model on multiple rewards in its prompt ...
Feb 15, 2024 · This paper introduces Rewards-in-Context (RiC), which conditions the response of a foundation model on multiple rewards in its prompt ...
Jun 5, 2024 · The paper makes a compelling case for the RiC approach to multi-objective alignment of large AI models. By using supervised fine-tuning instead ...
Code for the ICML 2024 paper "Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment". This repo is based on ...
The study Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment tackles the complex problem of tuning AI ...
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Feb 24, 2024 · We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful ...
Introduces Rewards-in-Context (RiC), which uses supervised fine-tuning for alignment. RiC conditions model responses on multiple rewards and supports preference ...
2021. Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment. R Yang, X Pan, F Luo, S Qiu, H Zhong, D Yu, J ...
We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI ...