StackingNet: Collective Inference Across Independent AI Foundation Models

2023-11-17 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

StackingNet, a meta-ensemble framework for combining predictions from independent AI foundation models, operates without access to internal parameters or training data. It improves accuracy, reduces bias, enables reliability ranking, and prunes underperforming models. Tested across language comprehension, visual estimation, and academic paper rating tasks, StackingNet consistently outperformed individual models and classic ensembles. For academic paper rating, it achieved accuracy comparable to or higher than individual human reviewers, requiring only 1% labeled data (10 examples). The framework also mitigated biases in facial attribute rating tasks across demographic groups and unified regression and classification tasks. StackingNet further allows unsupervised ranking and pruning of compromised models, enhancing robustness and reducing query costs.

Key takeaway

For AI Architects designing robust, multi-model systems, StackingNet offers a practical solution for coordinating independent foundation models. You can enhance accuracy, reduce bias, and improve fairness across diverse tasks like paper review or image attribute rating. Consider deploying this lightweight meta-ensemble to rank and prune models, ensuring system reliability and cost efficiency, even with limited labeled data. This shifts focus from single-model optimization to principled cooperation.

Key insights

StackingNet coordinates independent black-box foundation models via meta-ensemble, improving accuracy, fairness, and robustness with minimal data.

Principles

• Collective intelligence improves predictions by aggregating diverse models.
• Model diversity, not just scale, is crucial for robust AI.
• Minimal labeled data can effectively align AI predictions.

Method

StackingNet is a lightweight neural architecture that aggregates only output predictions from diverse base models, learning per-model weights and a scalar bias (for regression) or just weights (for classification) with minimal labeled data.

In practice

• Use StackingNet to combine black-box LLM/VLM outputs.
• Employ StackingNet for unsupervised model ranking.
• Prune unreliable or compromised models using StackingNet weights.

Topics

• Foundation Models
• Ensemble Learning
• Collective Inference
• Bias Mitigation
• Model Evaluation
• Adversary Pruning

Code references

• sylyoung/TestEnsemble
• openreview/openreview-py
• pymupdf/PyMuPDF

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.