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. |
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You Only Need One Model for Open-domain Question Answering (2022.emnlp-main)
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| Challenge: | Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training. |
| Approach: | They propose to use a single question answering model trained end-to-end to retrieve external knowledge and rerank passages with a separate reranked model. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores on the Natural Questions and TriviaQA open datasets. |
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge (2022.findings-emnlp)
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| Challenge: | Existing open-domain question answering methods rely on the retriever to gather all evidence in isolation, but our approach uses an intermediary module to perform a chain of reasoning over the retrieved set. |
| Approach: | They propose a new open-domain question answering framework that integrates an intermediary module into the current retriever-reader pipeline and integrates it into the model. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two OTT-QA datasets with an exact match score of 47.3 (45% relative gain). |
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer (2022.emnlp-main)
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| Challenge: | eschewing separate architecture and training for knowledge-intensive tasks is cumbersome . end-to-end training only based on supervision from the end task is awkward . |
| Approach: | They propose a single Transformer that performs retrieval as attention and end-to-end training solely based on supervision from the end QA task. |
| Outcome: | The proposed model outperforms state-of-the-art retrievers and readers on in-domain datasets. |
Latent Retrieval for Weakly Supervised Open Domain Question Answering (P19-1)
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| 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. |
Open Domain Question Answering with A Unified Knowledge Interface (2022.acl-long)
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| Challenge: | a retriever-reader framework is popular for open domain question answering . however, accessing heterogeneous knowledge sources through a unified interface remains unknown . |
| Approach: | They propose a retriever-reader framework that uses explicit knowledge to access heterogeneous knowledge sources through a unified interface. |
| Outcome: | The proposed framework can benefit from the expanded knowledge index, the authors show . their approach sets the single-model state-of-the-art on Natural Questions . |
Reinforced IR: A Self-Boosting Framework For Domain-Adapted Information Retrieval (2025.acl-long)
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| Challenge: | Existing retrieval methods struggle with highly specialized situations that require extensive domain expertise. |
| Approach: | They propose a method that integrates additional information from an LLM-based generator to enhance query performance and train the retriever to better discriminate the relevant documents identified by the generator. |
| Outcome: | The proposed method outperforms existing domain adaptation methods by a large margin and leads to substantial improvements in retrieval quality across a wide range of application scenarios. |
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. |
| Approach: | They propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans followed by supervised finetuning using question-context pairs. |
| Outcome: | The proposed approach outperforms models like REALM and RAG in retrieval accuracy and answer extraction. |
RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)
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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth
| Challenge: | Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study modelsâ generalizability across diverse domains. |
| Approach: | They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control . |
| Outcome: | The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control. |