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Found 5 papers, 4 papers with code
Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
In this paper, we focus on the alignment of recent text-to-image diffusion models, such as Stable Diffusion XL (SDXL), and find that this "reference mismatch" is indeed a significant problem in aligning these models due to the unstructured nature of visual modalities: e. g., a preference for a particular stylistic aspect can easily induce such a discrepancy.
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
To overcome these limitations, we introduce the BiGGen Bench, a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.
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.
