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Found 5 papers, 1 papers with code
Stable Language Model Pre-training by Reducing Embedding Variability
Stable pre-training is essential for achieving better-performing language models.
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.
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.
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy
Our approach overcomes this limitation by considering both the sentence-level semantics and the document-level rhetorical structure, resulting in a more robust and style-agnostic approach to detecting political bias in news articles.
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection
One of the reasons why it is difficult for the brain to perform backpropagation (BP) is the weight transport problem, which argues forward and feedback neurons cannot share the same synaptic weights during learning in biological neural networks.
