Summary of Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: a Survey, by Genta Indra Winata et al.
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
by Genta Indra Winata, Hanyang Zhao, Anirban Das, Wenpin Tang, David D. Yao, Shi-Xiong Zhang, Sambit Sahu
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey on recent advancements in preference tuning, which aligns deep generative models with human preferences. It explores various reinforcement learning frameworks, preference tuning tasks, models, and datasets across modalities such as language, speech, and vision. The study is divided into three main sections: introduction and preliminaries, in-depth exploration of each approach, and applications, discussion, and future directions. The paper focuses on the latest methodologies in preference tuning and model alignment, highlighting their potential to enhance the understanding of this field for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The survey explores how deep generative models can be trained to align with human preferences. It covers different approaches to preference tuning across various modalities, including language, speech, and vision. The study shows how these methods can be used in real-world applications, such as evaluating the quality of generated text or images. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
