Self-Rewarding Language Models by Meta AI – Path to Open-Source AGI?
In this video we review a new paper titled "Self-Rewarding Language Models" by Meta AI. This paper is published on the same day that Mark Zuckerberg announced that Meta AI is working towards building an open-source AGI, and this paper may be a step in that direction.
The paper introduces a method to self-align a pre-trained large language model (LLM) that can replace standard RLHF and RLAIF.
The method includes training the LLM using DPO, with responses that were evaluated by the model itself.
The researchers have evaluated the method with Llama 2 70B and achieved impressive results when comparing to Claude-2, Gemini Pro and GPT-4. That said, a lot of follow up research is essential.
Watch the video to learn more.
Post - https://aipapersacademy.com/self-rewarding-language-models/
Paper - https://arxiv.org/abs/2401.10020
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Chapters:
0:00 Paper Introduction
1:04 High-Level Idea
2:38 Self-Rewarding Language Models Method
4:41 Results
In this video we review a new paper titled “Self-Rewarding Language Models” by Meta AI. This paper is published on the same day that Mark Zuckerberg announced that Meta AI is working towards building an open-source AGI, and this paper may be a step in that direction.
The paper introduces a method to self-align a pre-trained large language model (LLM) that can replace standard RLHF and RLAIF.
The method includes training the LLM using DPO, with responses that were evaluated by the model itself.
The researchers have evaluated the method with Llama 2 70B and achieved impressive results when comparing to Claude-2, Gemini Pro and GPT-4. That said, a lot of follow up research is essential.
Watch the video to learn more.
Post – https://aipapersacademy.com/self-rewarding-language-models/
Paper – https://arxiv.org/abs/2401.10020
———————————————————————————————–
✉️ Join the newsletter – https://aipapersacademy.com/newsletter/
👍 Please like & subscribe if you enjoy this content
We use VideoScribe to edit our videos – https://tidd.ly/44TZEiX (affiliate)
———————————————————————————————–
Chapters:
0:00 Paper Introduction
1:04 High-Level Idea
2:38 Self-Rewarding Language Models Method
4:41 Results
