Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
📰 ArXiv cs.AI
Learn to decompose estimation from aggregation in multi-stakeholder LLM alignment to reduce weighting noise and improve stability
Action Steps
- Decompose the LLM alignment process into estimation and aggregation steps to reduce weighting noise
- Use empirical and theoretical methods to analyze the impact of weighting noise on stakeholder satisfaction
- Implement a multi-stakeholder task framework to test the effectiveness of the decomposition approach
- Compare the performance of the decomposed approach with traditional holistic LLM judges
- Apply the decomposition approach to real-world multi-stakeholder tasks to improve stability and reduce score shifts
Who Needs to Know This
AI researchers and engineers working on LLM alignment can benefit from this approach to improve the stability of their models, especially in multi-stakeholder tasks
Key Insight
💡 Decomposing estimation from aggregation can reduce weighting noise and improve the stability of LLM alignment in multi-stakeholder tasks
Share This
🚀 Improve LLM alignment in multi-stakeholder tasks by decomposing estimation from aggregation! 🤖
Key Takeaways
Learn to decompose estimation from aggregation in multi-stakeholder LLM alignment to reduce weighting noise and improve stability
Full Article
Title: Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
Abstract:
arXiv:2605.26878v1 Announce Type: new Abstract: Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility aggregation, yielding unstable implicit weights. We show empirically and theoretically that this aggregation-specific \emph{weighting noise} can create large score shifts when stakeholder satisfaction is dispersed; in our experiments, these weight-induced shifts also increase with stakeholder count. W
Abstract:
arXiv:2605.26878v1 Announce Type: new Abstract: Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility aggregation, yielding unstable implicit weights. We show empirically and theoretically that this aggregation-specific \emph{weighting noise} can create large score shifts when stakeholder satisfaction is dispersed; in our experiments, these weight-induced shifts also increase with stakeholder count. W
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