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Found 5 papers, 3 papers with code
Emergent Abilities in Reduced-Scale Generative Language Models
Large language models can solve new tasks without task-specific fine-tuning.
Deconstructing In-Context Learning: Understanding Prompts via Corruption
In contrast, the underlying pre-trained LLMs they use as a backbone are known to be brittle in this respect.
Let's Reinforce Step by Step
While recent advances have boosted LM proficiency in linguistic benchmarks, LMs consistently struggle to reason correctly on complex tasks like mathematics.
ReLoRA: High-Rank Training Through Low-Rank Updates
Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparameterized models remains poorly understood, while training costs grow exponentially.
Properties Of Winning Tickets On Skin Lesion Classification
We find that iterative pruning of the network resulted in improved accuracy, compared to that of the unpruned network, implying that -- the lottery ticket hypothesis can be applied to the problem of skin cancer detection and this hypothesis can result in a smaller network for inference.
