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Document-based Question-Answering (QA) tasks are crucial for precise information retrieval.
Effects of reality orientation therapy on elderly patients in the community
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Cognitive rehabilitation combined with drug treatment in alzheimer’s disease patients: a pilot study
Bottino, C.M., Carvalho, I.A., Alvarez, A.M.M., Avila, R., Zukauskas, P.R., Bustamante, S.E., Andrade, F.C., Hototian, S.R., Saffi, F., Camargo, C.H., 2005 · 2005
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Cognitive training in older adults with mild cognitive impairment: Impact on cognitive and functional performance
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Cognitive rehabilitation in patients with mild cognitive impairment
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Impact of metacognition and motivation on the efficacy of strategic memory training in older adults: Analysis of specific, transfer and maintenance effects
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Iirc: A dataset of incomplete information reading comprehension questions
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Benefits of training working memory in amnestic mild cognitive impairment: specific and transfer effects
Carretti, B., Borella, E., Fostinelli, S., Zavagnin, M., 2013 · 2013
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Efficacy of a cognitive intervention program in patients with mild cognitive impairment
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A challenge on large-scale biomedical semantic indexing and question answering
Paliouras, G., Krithara, A., 2014 · 2014
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Repetition-lag training to improve recollection memory in older people with amnestic mild cognitive impairment. a randomized controlled trial
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The pace study: a randomized clinical trial of cognitive activity strategy training for older people with mild cognitive impairment
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Computerized structured cognitive training in patients affected by early-stage alzheimer’s disease is feasible and effective: a randomized controlled study
Cavallo, M., Hunter, E.M., van der Hiele, K., Angilletta, C., 2016 · 2016
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Efficacy of the ubiquitous spaced retrieval-based memory advancement and rehabilitation training (usmart) program among patients with mild cognitive impairment: a randomized controlled crossover trial
Han, J.W., Son, K.L., Byun, H.J., Ko, J.W., Kim, K., Hong, J.W., Kim, T.H., Kim, K.W., 2017 · 2017
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PubMedQA: A dataset for biomedical research question answering, in: Inui, K., Jiang, J., Ng, V., Wan, X. (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China. pp. 2567–2577
Jin, Q., Dhingra, B., Liu, Z., Cohen, W., Lu, X., 2019 · 2019
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A dataset of information-seeking questions and answers anchored in research papers
Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N.A., Gardner, M., 2021 · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., Berant, J., 2021 · 2021
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Cogtale: an online platform for the evaluation, synthesis, and dissemination of evidence from cognitive interventions studies
Sabates, J., Belleville, S., Castellani, M., Dwolatzky, T., Hampstead, B.M., Lampit, A., Simon, S., Anstey, K., Goodenough, B., Mancuso, S., et al., 2021 · 2021
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Effectiveness of a visual imagery training program to improve prospective memory in older adults with and without mild cognitive impairment: A randomized controlled study
Lajeunesse, A., Potvin, M.J., Labelle, V., Chasles, M.J., Kergoat, M.J., Villalpando, J.M., Joubert, S., Rouleau, I., 2022 · 2022
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A survey of gpt-3 family large language models including chatgpt and gpt-4
Kalyan, K.S., 2023 · 2023
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Evaluating open-domain question answering in the era of large language models
Kamalloo, E., Dziri, N., Clarke, C.L., Rafiei, D., 2023 · 2023
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Bioasq-qa: A manually curated corpus for biomedical question answering
Krithara, A., Nentidis, A., Bougiatiotis, K., Paliouras, G., 2023 · 2023
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Gradually excavating external knowledge for implicit complex question answering, in: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 14405–14417
Liu, C., Li, X., Shang, L., Jiang, X., Liu, Q., Lam, E., Wong, N., 2023 · 2023
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OpenAI, 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Huge frozen language models as readers for open-domain question answering, in: ICML 2022 Workshop on Knowledge Retrieval and Language Models
Levine, Y., Ram, O., Jannai, D., Lenz, B., Shalev-Shwartz, S., Shashua, A., Leyton-Brown, K., Shoham, Y., 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al., 2022 · 2022
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Llm based generation of item-description for recommendation system, in: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1204–1207
Acharya, A., Singh, B., Onoe, N., 2023 · 2023
Cited alongside, same era.
Using large language models to simulate multiple humans and replicate human subject studies, in: International Conference on Machine Learning, PMLR. pp. 337–371
Aher, G.V., Arriaga, R.I., Kalai, A.T., 2023 · 2023
Cited alongside, same era.
Benchmarking foundation models with language-model-as-an-examiner
Bai, Y., Ying, J., Cao, Y., Lv, X., He, Y., Wang, X., Yu, J., Zeng, K., Xiao, Y., Lyu, H., Zhang, J., Li, J., Hou, L., 2023 · 2023
Cited alongside, same era.
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., Do, Q.V., Xu, Y., Fung, P., 2023 · 2023
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Bian, N., Han, X., Sun, L., Lin, H., Lu, Y., He, B., 2023 · 2023
Cited alongside, same era.
A survey on evaluation of large language models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P.S., Yang, Q., Xie, X., 2023 · 2023
Cited alongside, same era.
Visconde: Multi-document qa with gpt-3 and neural reranking, in: European Conference on Information Retrieval, Springer. pp. 534–543
Pereira, J., Fidalgo, R., Lotufo, R., Nogueira, R., 2023 · 2023
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Is chatgpt a general-purpose natural language processing task solver?
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., Yang, D., 2023 · 2023
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In-context retrieval-augmented language models
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., Shoham, Y., 2023 · 2023
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Replug: Retrieval-augmented black-box language models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., tau Yih, W., 2023 · 2023
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Tree prompting: Efficient task adaptation without fine-tuning, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 6253–6267
Singh, C., Morris, J., Rush, A.M., Gao, J., Deng, Y., 2023 · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., et al., 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al., 2023 · 2023
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A survey of large language models
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.Y., Wen, J.R., 2023 · 2023
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