Summary of Value Imprint: a Technique For Auditing the Human Values Embedded in Rlhf Datasets, by Ike Obi et al.
Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets
by Ike Obi, Rohan Pant, Srishti Shekhar Agrawal, Maham Ghazanfar, Aaron Basiletti
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Value Imprint, a framework for auditing and classifying the human values embedded within Reinforcement Learning from Human Feedback (RLHF) datasets. The authors conducted three case study experiments on popular RLHF datasets, including Anthropic/hh-rlhf, OpenAI WebGPT Comparisons, and Alpaca GPT-4-LLM. They developed a taxonomy of human values through an integrated review of prior works and applied it to annotate 6,501 RLHF preferences. The authors found that information-utility values, such as Wisdom/Knowledge and Information Seeking, were the most dominant, while prosocial and democratic values, like Well-being, Justice, and Human/Animal Rights, were less represented. This study has significant implications for developing language models that align with societal values and norms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can better understand what values are built into big AI systems called language models. These models are trained to be more like humans by using feedback from people who use them. The problem is that we don’t really know which human values are being used in these training processes. To fix this, the authors created a new way to look at and categorize these values. They tested their method on three big language model datasets and found that most of the values were about gaining knowledge or seeking information. The least common values were related to helping others or promoting fairness. This research is important because it can help us make language models that are more like what we want them to be, rather than just mimicking human behavior. |
Keywords
» Artificial intelligence » Gpt » Language model » Reinforcement learning from human feedback » Rlhf
