565 citations · 1.2k across the 11 of their papers we have counts for
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cs.CL2024
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Hui Dai, Ryan Teehan, Mengye Ren
Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short…
cs.CV2024
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation
Jack Lu, Ryan Teehan, Mengye Ren
In this paper, we propose ProCreate, a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent trai…
cs.CL2024★ 1 cited
CoLLEGe: Concept Embedding Generation for Large Language Models
Ryan Teehan, Brenden Lake, Mengye Ren
Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robus…