Summary of Collective Constitutional Ai: Aligning a Language Model with Public Input, by Saffron Huang et al.
Collective Constitutional AI: Aligning a Language Model with Public Input
by Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, Deep Ganguli
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 presents Collective Constitutional AI (CCAI), a multi-stage process for sourcing and integrating public input into language model (LM) systems. This approach addresses the need for methods that enable the broader public to collectively shape LM behavior, which affects them. The CCAI process involves identifying target populations, sourcing principles, training, and evaluating models. To demonstrate this approach’s real-world practicality, the authors created a fine-tuned LM using collective public input and compared it with a baseline model trained with established principles from an LM developer. Quantitative evaluations show that the CCAI-trained model exhibits lower bias across nine social dimensions while maintaining equivalent performance on language, math, and helpful-harmless evaluations. Qualitative comparisons reveal differences in model responses to contentious topics, suggesting that the CCAI-trained model reframes matters positively rather than refusing them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how people should have a say in what language models do and behave like. It presents a way to make this happen, called Collective Constitutional AI (CCAI). This method lets people contribute to the development of language models that affect them. The authors show that this approach works by creating a new language model using public input and comparing it with an existing one made by experts. They found that the new model is fairer and behaves differently when faced with tough topics. |
Keywords
» Artificial intelligence » Language model
