1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
A Bitter Lesson for Data Filtering
Christopher Mohri, John Duchi, Tatsunori Hashimoto
We investigate data filtering for large model pretraining via new scaling studies that target the high compute, data-scarce regime. In spite of an apparently common belief that fil…
cs.CR2024★ 1 cited
Trustless Audits without Revealing Data or Models
Suppakit Waiwitlikhit, Ion Stoica, Yi Sun +2
There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency. For example, a rightsholde…
cs.CL2023
Removing RLHF Protections in GPT-4 via Fine-Tuning
Qiusi Zhan, Richard Fang, Rohan Bindu +3
As large language models (LLMs) have increased in their capabilities, so does their potential for dual use. To reduce harmful outputs, produces and vendors of LLMs have used reinfo…