921 citations · 9.3k across the 292 of their papers we have counts for
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Evaluating Step-by-Step Reasoning through Symbolic Verification
Yi-Fan Zhang, Hanlin Zhang, Li Erran Li +1
Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations or chain-of-thoughts (CoT)) for in-context learning. On the other hand, these reaso…
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou +2
Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-t…
MRCLens: an MRC Dataset Bias Detection Toolkit
Yifan Zhong, Haohan Wang, Eric P. Xing
Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to p…
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models
Shibo Hao, Bowen Tan, Kaiwen Tang +5
It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, ba…
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh +6
Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Auto…