Summary of Aligners: Decoupling Llms and Alignment, by Lilian Ngweta et al.
Aligners: Decoupling LLMs and Alignment
by Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach decouples Large Language Models (LLMs) from the alignment process, allowing for more efficient and scalable alignment with human expectations. This method trains aligner models that can be used to align any LLM for a given criteria on an as-needed basis, reducing costs and potential negative impacts on performance. The aligners are trained solely using synthetic data generated with a prompted LLM and can be easily adjusted for various alignment criteria. A “squad” of multiple aligners is guided by binary miss-alignment classification models, known as inspectors. Empirical results demonstrate consistent improvements when applying the aligner squad to various LLMs, including chat-aligned models, across several instruction-following and red-teaming datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make large language models safer and more useful for everyday applications. They did this by creating special “aligner” models that can adjust the behavior of any large language model to fit what humans want it to do. This approach uses fake data created with a large language model and can be easily customized for different tasks. The aligners are guided by special inspectors that help them identify when something is wrong. In tests, this method showed that it can make large language models better at following instructions and working together. |
Keywords
* Artificial intelligence * Alignment * Classification * Large language model * Synthetic data
