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Users often ask dialogue systems ambiguous questions that require clarification.
Metacognition: A literature review research report
Lai, E. R · 2011
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Overview of the trec 2012 web track
Clarke, C. L., Craswell, N., and Voorhees, E. M · 2012
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Liu, C.-W., Lowe, R., Serban, I. V., Noseworthy, M., Charlin, L., and Pineau, J · 2016
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Continuously learning neural dialogue management
Su, P.-H., Gasic, M., Mrksic, N., Rojas-Barahona, L., Ultes, S., Vandyke, D., Wen, T.-H., and Young, S · 2016
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Kelcey, M., Devlin, J., Lee, K., Toutanova, K. N., Jones, L., Chang, M.-W., Dai, A., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Answer-based adversarial training for generating clarification questions
Rao, S. and Daumé III, H · 2019
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Identifying unclear questions in community question answering websites
Trienes, J. and Balog, K · 2019
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Asking clarification questions in knowledge-based question answering
Xu, J., Wang, Y., Tang, D., Duan, N., Yang, P., Zeng, Q., Zhou, M., and Sun, X · 2019
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Convai3: Generating clarifying questions for open-domain dialogue systems (clariq)
Aliannejadi, M., Kiseleva, J., Chuklin, A., Dalton, J., and Burtsev, M · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Resolving intent ambiguities by retrieving discriminative clarifying questions
Dhole, K. D · 2020
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Ambigqa: Answering ambiguous open-domain questions
Min, S., Michael, J., Hajishirzi, H., and Zettlemoyer, L · 2020
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
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Survey of hallucination in natural language generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., and Fung, P · 2022
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How to approach ambiguous queries in conversational search? a survey of techniques, approaches, tools and challenges
Keyvan, K. and Huang, J. X · 2022
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Assistance with large language models
Krasheninnikov, D., Krasheninnikov, E., and Krueger, D · 2022
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Kuhn, L., Gal, Y., and Farquhar, S · 2022
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Template-guided clarifying question generation for web search clarification
Wang, J. and Li, W · 2021
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
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Wahde, M. and Virgolin, M · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D · 2022
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