AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper focuses on the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model, and builds on state space models (SSMs), which require only linear inference time given the input.
The effectiveness of MoPLEx for tackling multi-way rankings following heterogeneous preferences through measuring alignment via gradients through measuring alignment via gradients is demonstrated.
We consider the problem of learning a mixture of $k$ Plackett-Luce models given multi-way ranking responses from annotators that may represent heterogeneous underlying preferences. This problem has many applications in AI alignment and preference optimization. Prior work has studied mixtures of Bradley-Terry models fro...
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