2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.IR2023★ 1 cited
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
Ye Liu, Semih Yavuz, Rui Meng +4
The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there…
cs.CL2023★ 1 cited
HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Execution
Ye Liu, Semih Yavuz, Rui Meng +3
The dominant paradigm of textual question answering systems is based on end-to-end neural networks, which excels at answering natural language questions but falls short on complex…
cs.CL2022★ 2 cited
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database
Ye Liu, Semih Yavuz, Rui Meng +3
Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or…