TPMM-DPO applies trajectory-aware learned-weight merging of prior policy models to stabilize iterative DPO against preference noise accumulation.
Spread preference annotation: Direct preference judgment for efficient llm alignment
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Empathic similarity feedback in prompts generates more acceptable compromises than chain-of-thought, and margin-based training on the resulting data lets smaller models produce them without ongoing empathy estimation.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
citing papers explorer
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TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization
TPMM-DPO applies trajectory-aware learned-weight merging of prior policy models to stabilize iterative DPO against preference noise accumulation.
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Generating Place-Based Compromises Between Two Points of View
Empathic similarity feedback in prompts generates more acceptable compromises than chain-of-thought, and margin-based training on the resulting data lets smaller models produce them without ongoing empathy estimation.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.