27 citations · 137 across the 17 of their papers we have counts for
6 papers · 1 filter
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Dheeru Dua, Emma Strubell, Sameer Singh +1
Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these mo…
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Bernd Bohnet, Vinh Q. Tran, Pat Verga +19
Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in info…
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen +2
While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and even…
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
Wenhu Chen, William W. Cohen, Michiel De Jong +4
In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed…
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization
Yue Dong, John Wieting, Pat Verga
Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or…
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering
Wenhu Chen, Pat Verga, Michiel de Jong +2
Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large n…