Summary of Inverse Constitutional Ai: Compressing Preferences Into Principles, by Arduin Findeis et al.
Inverse Constitutional AI: Compressing Preferences into Principles
by Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, Robert Mullins
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper addresses the issue of unintended biases in human-annotated pairwise text preference data, a crucial component in fine-tuning and evaluating state-of-the-art AI models. The authors formulate this problem as the Inverse Constitutional AI (ICAI) task: given a dataset of feedback, extract a constitution that enables a large language model (LLM) to reconstruct original annotations. They propose an initial ICAI algorithm and validate its generated constitutions quantitatively based on reconstructed annotations. This approach has potential applications in identifying undesirable biases, scaling feedback to unseen data, or adapting LLMs to individual user preferences. The authors demonstrate their method on various datasets, including synthetic feedback datasets with known principles, the AlpacaEval dataset, and the crowdsourced Chatbot Arena dataset. By releasing their code and experiments, this research aims to aid in understanding and mitigating biases in AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence (AI) systems don’t learn to like or dislike certain things just because of how people prefer them. Right now, humans give feedback to AI by choosing which text is better, but this can lead to unwanted biases. The researchers came up with a new way to figure out what these biases are and eliminate them. They tested their method on different datasets and found it works well. This could help make sure AI systems are fair and don’t discriminate against certain things or people. |
Keywords
» Artificial intelligence » Fine tuning » Large language model
