1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024
RARe: Retrieval Augmented Retrieval with In-Context Examples
Atula Tejaswi, Yoonsang Lee, Sujay Sanghavi +1
While in-context learning is well-studied with decoder-only language models (LLMs), its utility for encoder-only models remains underexplored. We study in-context learning for enco…
cs.CL2024
Exploring Design Choices for Building Language-Specific LLMs
Atula Tejaswi, Nilesh Gupta, Eunsol Choi
Despite rapid progress in large language models (LLMs), their performance on a vast majority of languages remains unsatisfactory. In this paper, we study building language-specific…
cs.LG2024★ 1 cited
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
Vijay Lingam, Atula Tejaswi, Aditya Vavre +7
Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(ΔW\). These \(ΔW\) mat…