2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.CL2024
SimpleStrat: Diversifying Language Model Generation with Stratification
Justin Wong, Yury Orlovskiy, Michael Luo +2
Generating diverse responses from large language models (LLMs) is crucial for applications such as planning/search and synthetic data generation, where diversity provides distinct…
cs.PL2024
Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages
Federico Mora, Justin Wong, Haley Lepe +6
Recent advances in large language models (LLMs) for code applications have demonstrated remarkable zero-shot fluency and instruction following on challenging code related tasks ran…
cs.CV2024★ 2 cited
Stylus: Automatic Adapter Selection for Diffusion Models
Michael Luo, Justin Wong, Brandon Trabucco +5
Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters…