21 citations · 92 across the 7 of their papers we have counts for
12 papers · 1 filter
Reward Design with Language Models
Minae Kwon, Sang Michael Xie, Kalesha Bullard +1
Reward design in reinforcement learning (RL) is challenging since specifying human notions of desired behavior may be difficult via reward functions or require many expert demonstr…
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Hong Liu, Sang Michael Xie, Zhiyuan Li +1
Language modeling on large-scale datasets leads to impressive performance gains on various downstream language tasks. The validation pre-training loss (or perplexity in autoregress…
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen, Robbie Jones, Ananya Kumar +4
We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e.g., photographs) and unlabeled data from a target domain (e.g., sketches) are used to l…
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Fahim Tajwar, Ananya Kumar, Sang Michael Xie +1
Out-of-distribution detection is an important component of reliable ML systems. Prior literature has proposed various methods (e.g., MSP (Hendrycks & Gimpel, 2017), ODIN (Liang et…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
Colin Wei, Sang Michael Xie, Tengyu Ma
Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging…