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Found 3 papers, 3 papers with code
ORPO: Monolithic Preference Optimization without Reference Model
While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence.
Robust Fine-Tuning of Vision-Language Models for Domain Generalization
To address these gaps, we present a new recipe for few-shot fine-tuning of the popular vision-language foundation model CLIP and evaluate its performance on challenging benchmark datasets with realistic distribution shifts from the WILDS collection.
Can Large Language Models Capture Dissenting Human Voices?
Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks.
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