24 citations · 25 across the 4 of their papers we have counts for
4 papers
MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models
Deepak Nathani, David Wang, Liangming Pan +1
Language Models (LMs) have shown impressive performance in various natural language tasks. However, when it comes to natural language reasoning, LMs still face challenges such as h…
FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation
Yan Meng, Liangming Pan, Yixin Cao +1
Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question ge…
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Liangming Pan, Michael Saxon, Wenda Xu +3
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behavior…
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
Xuan Long Do, Bowei Zou, Shafiq Joty +4
Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast in…