84 citations · 357 across the 32 of their papers we have counts for
6 papers · 1 filter
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
Boshi Wang, Sewon Min, Xiang Deng +4
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermed…
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin A. Inan, Xuechen Li +6
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating syntheti…
Synthetic Question Value Estimation for Domain Adaptation of Question Answering
Xiang Yue, Ziyu Yao, Huan Sun
Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic que…
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington +4
The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which…
Iteratively Prompt Pre-trained Language Models for Chain of Thought
Boshi Wang, Xiang Deng, Huan Sun
While Pre-trained Language Models (PLMs) internalize a great amount of world knowledge, they have been shown incapable of recalling these knowledge to solve tasks requiring complex…
DOM-LM: Learning Generalizable Representations for HTML Documents
Xiang Deng, Prashant Shiralkar, Colin Lockard +2
HTML documents are an important medium for disseminating information on the Web for human consumption. An HTML document presents information in multiple text formats including unst…