FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection (2024.acl-long)
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| Challenge: | Open Domain Question Answering (ODQA) is a longstanding task in Natural Language Processing that involves generating an answer solely based on a given question. |
| Approach: | They propose a novel approach that executes sentence selection on the encoded passages to enhance the inference speed while reducing the context length required for generating answers. |
| Outcome: | The proposed approach can increase inference speed by **2.3X-5.7X** while maintaining the model’s performance. |
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
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| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
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| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
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Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering (2020.emnlp-main)
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| Challenge: | Existing approaches to Open-Domain Question Answering assume all passages are of equal importance and allocate computation to them. |
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