1 citations · 2 across the 9 of their papers we have counts for
3 papers · 1 filter
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar, Quentin Fournier, Matthew D Riemer +4
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-training remains out of reach for most r…
Locally Differentially Private Document Generation Using Zero Shot Prompting
Saiteja Utpala, Sara Hooker, Pin Yu Chen
Numerous studies have highlighted the privacy risks associated with pretrained large language models. In contrast, our research offers a unique perspective by demonstrating that pr…
Language Agnostic Code Embeddings
Saiteja Utpala, Alex Gu, Pin Yu Chen
Recently, code language models have achieved notable advancements in addressing a diverse array of essential code comprehension and generation tasks. Yet, the field lacks a compreh…