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BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely
Restart Sampling for Improving Generative Processes
The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE.
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models Angéline Pouget
Second, pretraining with global, unfiltered data before fine-tuning on English content can improve cultural understanding without sacrificing performance on said popular benchmarks.
Normalization and effective learning rates in reinforcement learning Clare Lyle
Layer normalization has demonstrated remarkable effectiveness at preventing plasticity loss in continual and reinforcement learning (RL), though the precise reasons for this effectiveness remain mysterious.
Overleaf Example
Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.
Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks
In image classification, it is common to utilize a pretrained model to extract meaningful features of the input images, and then to train a classifier on top of it to make predictions for any downstream task.