Summary of Evaluating the Role Of `constitutions’ For Learning From Ai Feedback, by Saskia Redgate et al.
Evaluating the role of `Constitutions’ for learning from AI feedback
by Saskia Redgate, Andrew M. Bean, Adam Mahdi
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Large language models (LLMs) have been used as substitutes for human feedback to train and assess other LLMs. A common approach relies on written guidelines called ‘constitutions’, which a critic model uses to provide feedback and improve generations. This study investigates how the choice of constitution affects feedback quality by using four different constitutions to improve patient-centered communication in medical interviews. The results show that detailed constitutions led to better emotive qualities, but none outperformed the baseline in learning practically-oriented skills like information gathering and provision. The findings suggest prioritizing detailed constitutions, but also highlight limitations of AI feedback as a reward signal in certain areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to use big language models (like those used for chatbots) to help teach other computers how to talk better. They want to know if these “constitutions” – like instructions for a critic model – make a difference. The study looked at four different constitutions and saw that the detailed ones did a better job with emotional stuff, but not so much with practical skills like asking questions or giving information. It’s saying that while detailed constitutions are good, there might be limits to how well AI can help learn these skills. |
