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cs.CL2025
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Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data.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.CL2024
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…
cs.CL2024
Learning and Forgetting Unsafe Examples in Large Language Models
Jiachen Zhao, Zhun Deng, David Madras +2
As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning fro…