4 citations · 11 across the 4 of their papers we have counts for
4 papers
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
Haobo Song, Hao Zhao, Soumajit Majumder +1
Fine-tuning large pre-trained foundation models, such as the 175B GPT-3, has attracted more attention for downstream tasks recently. While parameter-efficient fine-tuning methods h…
Is In-Context Learning Sufficient for Instruction Following in LLMs?
Hao Zhao, Maksym Andriushchenko, Francesco Croce +1
In-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs that can potentially…
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Hao Zhao, Maksym Andriushchenko, Francesco Croce +1
There is a consensus that instruction fine-tuning of LLMs requires high-quality data, but what are they? LIMA (NeurIPS 2023) and AlpaGasus (ICLR 2024) are state-of-the-art methods…
On Pitfalls of Test-Time Adaptation
Hao Zhao, Yuejiang Liu, Alexandre Alahi +1
Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts. However, the lack of consistent settings an…