Papers with KPO

2 papers
K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)

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Challenge: Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities.
Approach: They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples.
Outcome: The proposed model can be extended to accommodate top-K ranking and improve training efficiency.
Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)

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Challenge: Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc.
Approach: They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences.
Outcome: The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality.

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