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In real-world applications of large language models, outputs are often required to be confined: selecting items from predefined product or document sets, generating phrases that comply with safety standards, or conforming to specialized formatting styles.
Language models are few-shot learners
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Scalable zero-shot entity linking with dense entity retrieval
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Prefix b-trees
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Gpu computing
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Kilt: a benchmark for knowledge intensive language tasks
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Robust disambiguation of named entities in text
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Climate-fever: A dataset for verification of real-world climate claims
Diggelmann, T., Boyd-Graber, J., Bulian, J., Ciaramita, M., and Leippold, M. (2020) · 2012
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Foundations of json schema
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Guided open vocabulary image captioning with constrained beam search
Anderson, P., Fernando, B., Johnson, M., and Gould, S. (2017) · 2017
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Privacy-enhancing technologies
Fischer-Hbner, S. and Berthold, S. (2017) · 2017
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Dbpedia-entity v2: a test collection for entity search
Hasibi, F., Nikolaev, F., Xiong, C., Balog, K., Bratsberg, S. E., Kotov, A., and Callan, J. (2017) · 2017
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Lexically constrained decoding for sequence generation using grid beam search
Hokamp, C. and Liu, Q. (2017) · 2017
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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) · 2017
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In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · 2017
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Zero-shot relation extraction via reading comprehension
Levy, O., Seo, M., Choi, E., and Zettlemoyer, L. (2017) · 2017
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Wizard of wikipedia: Knowledge-powered conversational agents
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., and Weston, J. (2018) · 2018
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T-rex: A large scale alignment of natural language with knowledge base triples
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Robust named entity disambiguation with random walks
Guo, Z. and Barbosa, D. (2018) · 2018
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Improving entity linking by modeling latent relations between mentions
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Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Post, M. and Vilar, D. (2018) · 2018
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. (2023) · 2023
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Grammar-constrained decoding for structured nlp tasks without finetuning
Geng, S., Josifoski, M., Peyrard, M., and West, R. (2023) · 2023
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Large language model ai chatbots require approval as medical devices
Gilbert, S., Harvey, H., Melvin, T., Vollebregt, E., and Wicks, P. (2023) · 2023
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A review of opportunities and challenges of chatbots in education
Hwang, G.-J. and Chang, C.-Y. (2023) · 2023
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Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine
Lee, P., Bubeck, S., and Petro, J. (2023) · 2023
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Thorne, J., Vlachos, A., Christodoulopoulos, C., and Mittal, A. (2018) · 2018
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Pytrec_eval: An extremely fast python interface to trec_eval
Van Gysel, C. and de Rijke, M. (2018) · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D. (2018) · 2018
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Cgmh: Constrained sentence generation by metropolis-hastings sampling
Miao, Y., Zhou, H., and Mou, L. (2019) · 2019
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Neural grammatical error correction with finite state transducers
Stahlberg, F. and Kumar, S. (2019) · 2019
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Masakhaner: Named entity recognition for african languages
Adelani, D. I., Abbott, J., Neubig, G., D’souza, D., Kreutzer, J., Lignos, C., Palen-Michel, C., Buzaaba, H., Rijhwani, S., Ruder, S., et al. (2021) · 2021
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Autoregressive entity retrieval
De Cao1, N., Izacard, G., Riedel, S., and Petroni, F. (2021) · 2021
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Liu, H., Li, C., Wu, Q., and Lee, Y. J. (2023) · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Ye, Q., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P., Shi, Y., Li, C., Xu, Y., Chen, H., Tian, J., Qi, Q., Zhang, J., and Huang, F. (2023) · 2023
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Safetybench: Evaluating the safety of large language models with multiple choice questions
Zhang, Z., Lei, L., Wu, L., Sun, R., Huang, Y., Long, C., Liu, X., Lei, X., Tang, J., and Huang, M. (2023) · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M. (2023) · 2023
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Guiding LLMs the right way: Fast, non-invasive constrained generation
Beurer-Kellner, L., Fischer, M., and Vechev, M. (2024) · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al. (2024) · 2024
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Can long-context language models subsume retrieval, rag, sql, and more?
Lee, J., Chen, A., Dai, Z., Dua, D., Sachan, D. S., Boratko, M., Luan, Y., Arnold, S. M., Perot, V., Dalmia, S., et al. (2024) · 2024
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Recommender systems with generative retrieval
Rajput, Shashank, e. a. (2024) · 2024
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Recommender systems with generative retrieval
Rajput, S., Mehta, N., Singh, A., Hulikal Keshavan, R., Vu, T., Heldt, L., Hong, L., Tay, Y., Tran, V., Samost, J., et al. (2024) · 2024
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Learning to tokenize for generative retrieval
Sun, W., Yan, L., Chen, Z., Wang, S., Zhu, H., Ren, P., Chen, Z., Yin, D., Rijke, M., and Ren, Z. (2024) · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T. (2024) · 2024
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