Summary of Value Alignment From Unstructured Text, by Inkit Padhi et al.
Value Alignment from Unstructured Text
by Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush R. Varshney
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper proposes an end-to-end methodology for aligning large language models (LLMs) to implicit and explicit values represented in unstructured text data. This approach leverages synthetic data generation techniques to efficiently align the model with values present in the text, outperforming other methods. The authors demonstrate the effectiveness of their method on the Mistral-7B-Instruct model through two use-cases, showcasing improved performance through automatic metrics and win rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to teach large language models to follow certain rules or values based on unstructured text data. They used a new method that creates synthetic data to help align the model with these values. This approach worked better than others in two real-world examples, showing improved results through specific metrics and comparisons. |
Keywords
» Artificial intelligence » Synthetic data
