4 papers · 1 filter
Context Tuning for In-Context Optimization
Jack Lu, Ryan Teehan, Zhenbang Yang +1
We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates. In-Context Learning…
When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers
Jack Lu, Ryan Teehan, Jinran Jin +1
Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of solver-generated candidates. This r…
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Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data.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…
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…