Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering (2023.emnlp-main)
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| Challenge: | Existing methods for multistep question answering have shown promise in generating multistep solutions, but they lack robustness. |
| Approach: | They propose a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions. |
| Outcome: | The proposed framework outperforms neuro-symbolic methods on a DROP contrast set and GPT-3.5 on QA adversarial sets. |
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable language generation capabilities, propelling advancements in various understanding/generation tasks, including opendomain question answering (QA). |
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| Challenge: | a system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning would allow better understanding of why a model produced the answer it did. |
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
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Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (2020.lrec-1)
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Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
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