2 citations · 2 across the 4 of their papers we have counts for
4 papers
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions
Shulin Cao, Jiajie Zhang, Jiaxin Shi +5
Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought (CoT) reasoning. However, they tend to generate factually incorrec…
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering
Jiajie Zhang, Shulin Cao, Tingjia Zhang +5
Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single know…
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Deng Li, Aming Wu, Yahong Han +1
Recently, large-scale pre-trained models have shown their advantages in many tasks. However, due to the huge computational complexity and storage requirements, it is challenging to…
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi +5
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and…