1-PAGER: One Pass Answer Generation and Evidence Retrieval (2023.findings-emnlp)
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| Challenge: | 1-Pager is the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. |
| Approach: | They propose a system that partitions the corpus using constrained decoding to select a document and answer string, and a method that uses a single Transformer-based model to generate evidence. |
| Outcome: | The proposed system outperforms the equivalent ‘closed-book’ question answering model by grounding predictions in evidence corpus. |
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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang
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| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
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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 . |
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| Challenge: | Existing methods for enhancing RAG performance rely on heuristic-based augmentation . Existing approaches rely heavily on a heuriistic-driven approach, resulting in poor generalization and skews in the evidence length. |
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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. |
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| Challenge: | a new benchmark is developed to answer open-domain questions from text . the system uses a single multi-task transformer model to perform all the necessary subtasks . |
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| Challenge: | Recent work shows that answer verification models can improve the state of the art in Question Answering . despite the fact that the supporting candidates are ranked only according to the relevancy with the question, the model still lacks the support needed for other answer candidates. |
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SQUARE: Unsupervised Retrieval Adaptation via Synthetic Data (2025.findings-emnlp)
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| Challenge: | Pre-trained retrieval models often face challenges in zero-shot retrieval for knowledge-based question answering . |
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