1 citations · 1 across the 9 of their papers we have counts for
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Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning
Liyi Zhang, Jake Snell, Thomas L. Griffiths
Fine-tuning large language models (LLMs) with low-rank adaptation (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, when a problem require…
Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning
Liyi Zhang, Logan Nelson, Thomas L. Griffiths
Categories can be represented at different levels of abstraction, from prototypes focused on the most typical members to remembering all observed exemplars of the category. These r…
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Liyi Zhang, Michael Y. Li, R. Thomas McCoy +3
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture as…
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity
Raja Marjieh, Sreejan Kumar, Declan Campbell +4
Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has…