| Challenge: | Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. |
| Approach: | They propose to jointly learn the retriever and reader from question-answer string pairs and without any IR system. |
| Outcome: | The proposed approach outperforms BM25 on open datasets with a learner and reader by 19 points in exact match. |
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Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
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| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
| Approach: | They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever. |
| Outcome: | The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever. |
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation (2021.emnlp-main)
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| Challenge: | Open-domain question answering uses evidence retrieved from large corpus to answer questions . state-of-the-art approaches require intermediate evidence annotations for training . however, such intermediate annotations are expensive and methods that rely on them cannot transfer to the more common setting . |
| Approach: | They propose an open-domain question answering approach that alternately finds evidence from an up-to-date model and encourages the model to learn the most likely evidence. |
| Outcome: | The proposed approach improves over weak retrievers on multi-hop and single-hop benchmarks without using evidence labels. |
End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021.acl-long)
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Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, Bryan Catanzaro
| Challenge: | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised methods. |
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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
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Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
Relevance-guided Supervision for OpenQA with ColBERT (2021.tacl-1)
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| Challenge: | Recent work has focused on learning to retrieve passages for open-domain question answering . if notions of relevance are not tailored to questions, the MRC model will not reliably see the best passages . |
| Approach: | They propose a retrieval model that uses coarse-grained vector representations of questions and passages to adapt it to OpenQA. |
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Open-Domain Question Answering (2020.acl-tutorials)
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
| Outcome: | The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods . |
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Using customized retrieval models, model transferability and scalability are limited. |
| Approach: | They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets. |
| Outcome: | The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Two-Step Question Retrieval for Open-Domain QA (2022.findings-acl)
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| Challenge: | Existing question retrieval models have shown a significant increase in inference speed but at the cost of lower QA performance compared to the retriever-reader pipeline. |
| Approach: | They propose a two-step question retrieval model with distant supervision to improve inference speed. |
| Outcome: | The proposed model significantly increases the performance of existing question retrieval models with a negligible loss on inference speed. |