Summary of Towards Scalable Automated Alignment Of Llms: a Survey, by Boxi Cao et al.
Towards Scalable Automated Alignment of LLMs: A Survey
by Boxi Cao, Keming Lu, Xinyu Lu, Jiawei Chen, Mengjie Ren, Hao Xiang, Peilin Liu, Yaojie Lu, Ben He, Xianpei Han, Le Sun, Hongyu Lin, Bowen Yu
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 Medium Difficulty summary: This paper tackles the crucial issue of aligning large language models (LLMs) with human needs as they rapidly surpass human capabilities. Traditional methods relying on human annotation are no longer scalable, making it essential to explore new automated alignment signals and approaches. The study reviews recent developments in automated alignment, categorizing existing methods into four major categories based on signal sources and discussing their current status and potential development. Additionally, the authors delve into the underlying mechanisms enabling automated alignment and identify key factors for its feasibility and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making sure large language models (LLMs) are working properly to help humans. As LLMs get better than humans at doing certain tasks, we need new ways to make sure they’re aligned with what humans want them to do. Right now, we rely too much on people to label data, but this isn’t scalable as LLMs keep getting more powerful. The authors look at recent ideas for automated alignment and how they can be used to create better models that work well with humans. |
Keywords
» Artificial intelligence » Alignment
