Challenges and Future Directions of Data-Centric AI Alignment
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918005897691136 |
|---|---|
| author | Yeh, Min-Hsuan Wang, Jeffrey Du, Xuefeng Park, Seongheon Tao, Leitian Im, Shawn Li, Yixuan |
| author_facet | Yeh, Min-Hsuan Wang, Jeffrey Du, Xuefeng Park, Seongheon Tao, Leitian Im, Shawn Li, Yixuan |
| contents | As AI systems become increasingly capable and influential, ensuring their alignment with human values, preferences, and goals has become a critical research focus. Current alignment methods primarily focus on designing algorithms and loss functions but often underestimate the crucial role of data. This paper advocates for a shift towards data-centric AI alignment, emphasizing the need to enhance the quality and representativeness of data used in aligning AI systems. In this position paper, we highlight key challenges associated with both human-based and AI-based feedback within the data-centric alignment framework. Through qualitative analysis, we identify multiple sources of unreliability in human feedback, as well as problems related to temporal drift, context dependence, and AI-based feedback failing to capture human values due to inherent model limitations. We propose future research directions, including improved feedback collection practices, robust data-cleaning methodologies, and rigorous feedback verification processes. We call for future research into these critical directions to ensure, addressing gaps that persist in understanding and improving data-centric alignment practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Challenges and Future Directions of Data-Centric AI Alignment Yeh, Min-Hsuan Wang, Jeffrey Du, Xuefeng Park, Seongheon Tao, Leitian Im, Shawn Li, Yixuan Computation and Language As AI systems become increasingly capable and influential, ensuring their alignment with human values, preferences, and goals has become a critical research focus. Current alignment methods primarily focus on designing algorithms and loss functions but often underestimate the crucial role of data. This paper advocates for a shift towards data-centric AI alignment, emphasizing the need to enhance the quality and representativeness of data used in aligning AI systems. In this position paper, we highlight key challenges associated with both human-based and AI-based feedback within the data-centric alignment framework. Through qualitative analysis, we identify multiple sources of unreliability in human feedback, as well as problems related to temporal drift, context dependence, and AI-based feedback failing to capture human values due to inherent model limitations. We propose future research directions, including improved feedback collection practices, robust data-cleaning methodologies, and rigorous feedback verification processes. We call for future research into these critical directions to ensure, addressing gaps that persist in understanding and improving data-centric alignment practices. |
| title | Challenges and Future Directions of Data-Centric AI Alignment |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.01957 |