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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering.

, , , , and . EACL, page 1589-1602. Association for Computational Linguistics, (2023)

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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering., , , , and . EACL, page 1589-1602. Association for Computational Linguistics, (2023)QA Is the New KR: Question-Answer Pairs as Knowledge Bases., , , , , , and . AAAI, page 15385-15392. AAAI Press, (2023)Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization., , and . EMNLP (Findings), page 1067-1082. Association for Computational Linguistics, (2022)To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering., , , and . ACL (1), page 14429-14446. Association for Computational Linguistics, (2023)Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge., , , and . NAACL-HLT, page 3678-3691. Association for Computational Linguistics, (2021)Multilingual Fact Linking., , , , and . AKBC, (2021)Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models., , , , , , , , and . CoRR, (2024)MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text., , , , and . EMNLP, page 5558-5570. Association for Computational Linguistics, (2022)QA Is the New KR: Question-Answer Pairs as Knowledge Bases., , , , , , and . CoRR, (2022)