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In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens.
Roberta: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 1909
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The loading problem
Eilon, S. and Christofides, N · 1971
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Worst-case performance bounds for simple one-dimensional packing algorithms
Johnson, D. S., Demers, A. J., Ullman, J. D., Garey, M. R., and Graham, R. L · 1974
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ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Combinatorial optimization: Theory and algorithms
Bernhard, K. and Vygen, J · 2008
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Teaching machines to read and comprehend
Hermann, K. M., Kociský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P · 2015
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
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QuAC: Question answering in context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-t., Choi, Y., Liang, P., and Zettlemoyer, L · 2018
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The NarrativeQA reading comprehension challenge
Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Narayan, S., Cohen, S. B., and Lapata, M · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Rajpurkar, P., Jia, R., and Liang, P · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., and Gardner, M · 2019
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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., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M.-W., Dai, A. M., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Social IQa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., Le Bras, R., and Choi, Y · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
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HellaSwag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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PIQA: reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., LeBras, R., Gao, J., and Choi, Y · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Cited alongside, same era.
On faithfulness and factuality in abstractive summarization
Maynez, J., Narayan, S., Bohnet, B., and McDonald, R · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y · 2020
Cited alongside, same era.
BLOOM: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilic, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., Ruwase, O., Bawden, R., Bekman, S., McMillan-Major, A., Beltagy, I., Nguyen, H., Saulnier, L., Tan, S., Suarez, P. O., Sanh, V., Laurençon, H., Jernite, Y., Launay, J., Mitchell, M., Raffel, C., Gokaslan, A., Simhi, A., Soroa, A., Aji, A. F., Alfassy, A., Rogers, A., Nitzav, A. K., Xu, C., Mou, C., Emezue, C., Klamm, C., Leong, C., van Strien, D., Adelani, D. I., and et al · 2022
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OPT: open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M. T., Li, X., Lin, X. V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P. S., Sridhar, A., Wang, T., and Zettlemoyer, L · 2022
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Data-juicer: A one-stop data processing system for large language models
Chen, D., Huang, Y., Ma, Z., Chen, H., Pan, X., Ge, C., Gao, D., Xie, Y., Liu, Z., Gao, J., et al · 2023
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Palm: Scaling language modeling with pathways
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2020
Cited alongside, same era.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M. I., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C. J., Terry, M., Le, Q. V., and Sutton, C · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al · 2021
Cited alongside, same era.
Krell, M. M., Kosec, M., Perez, S. P., and Fitzgibbon, A · 2021
Cited alongside, same era.
Entity-based knowledge conflicts in question answering
Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., and Singh, S · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, H. F., Aslanides, J., Henderson, S., Ring, R., Young, S., Rutherford, E., Hennigan, T., Menick, J., Cassirer, A., Powell, R., van den Driessche, G., Hendricks, L. A., Rauh, M., Huang, P., Glaese, A., Welbl, J., Dathathri, S., Huang, S., Uesato, J., Mellor, J., Higgins, I., Creswell, A., McAleese, N., Wu, A., Elsen, E., Jayakumar, S. M., Buchatskaya, E., Budden, D., Sutherland, E., Simonyan, K., Paganini, M., Sifre, L., Martens, L., Li, X. L., Kuncoro, A., Nematzadeh, A., Gribovskaya, E., Donato, D., Lazaridou, A., Mensch, A., Lespiau, J., Tsimpoukelli, M., Grigorev, N., Fritz, D., Sottiaux, T., Pajarskas, M., Pohlen, T., Gong, Z., Toyama, D., de Masson d’Autume, C., Li, Y., Terzi, T., Mikulik, V., Babuschkin, I., Clark, A., de Las Casas, D., Guy, A., Jones, C., Bradbury, J., Johnson, M. J., Hechtman, B. A., Weidinger, L., Gabriel, I., Isaac, W., Lockhart, E., Osindero, S., Rimell, L., Dyer, C., Vinyals, O., Ayoub, K., Stanway, J., Bennett, L., Hassabis, D., Kavukcuoglu, K., and Irving, G · 2021
Cited alongside, same era.
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2023
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Redpajama: an open dataset for training large language models, 2023
Computer, T · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Dao, T · 2023
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A static evaluation of code completion by large language models
Ding, H., Kumar, V., Tian, Y., Wang, Z., Kwiatkowski, R., Li, X., Ramanathan, M. K., Ray, B., Bhatia, P., and Sengupta, S · 2023
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Towards mitigating LLM hallucination via self reflection
Ji, Z., Yu, T., Xu, Y., Lee, N., Ishii, E., and Fung, P · 2023
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Large language models struggle to learn long-tail knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., and Raffel, C · 2023
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Ever: Mitigating hallucination in large language models through real-time verification and rectification, 2023
Kang, H., Ni, J., and Yao, H · 2023
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When less is more: Investigating data pruning for pretraining llms at scale
Marion, M., Üstün, A., Pozzobon, L., Wang, A., Fadaee, M., and Hooker, S · 2023
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Inverse scaling: When bigger isn’t better
McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2023
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Gpt-4 technical report, 2023
OpenAI, :, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.-L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, B., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Chung, H. W., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Fishman, S. P., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Gu, S. S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Łukasz Kaiser, Kamali, A., Kanitscheider, I., Keskar, N. S., Khan, T., Kilpatrick, L., Kim, J. W., Kim, C., Kim, Y., Kirchner, H., Kiros, J., Knight, M., Kokotajlo, D., Łukasz Kondraciuk, Kondrich, A., Konstantinidis, A., Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, D., Li, C. M., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A., Malfacini, K., Manning, S., Markov, T., Markovski, Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., McKinney, S. M., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D., Mu, T., Murati, M., Murk, O., Mély, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Ouyang, L., O’Keefe, C., Pachocki, J., Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., de Avila Belbute Peres, F., Petrov, M., de Oliveira Pinto, H. P., Michael, Pokorny, Pokrass, M., Pong, V., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Raymond, C., Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B., Song, Y., Staudacher, N., Such, F. P., Summers, N., Sutskever, I., Tang, J., Tezak, N., Thompson, M., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Uribe, J. F. C., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J. J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., and Zoph, B · 2023
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Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J · 2023
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Check your facts and try again: Improving large language models with external knowledge and automated feedback, 2023
Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., and Gao, J · 2023
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Prompting GPT-3 to be reliable
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J. L., and Wang, L · 2023
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Fine-tuning language models for factuality, 2023
Tian, K., Mitchell, E., Yao, H., Manning, C. D., and Finn, C · 2023
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Larger language models do in-context learning differently
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al · 2023
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Starcoder 2 and the stack v2: The next generation
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al · 2024
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Fine-grained hallucinations detections
Mishra, A., Asai, A., Balachandran, V., Wang, Y., Neubig, G., Tsvetkov, Y., and Hajishirzi, H · 2024
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In-context pretraining: Language modeling beyond document boundaries
Shi, W., Min, S., Lomeli, M., Zhou, C., Li, M., Lin, X. V., Smith, N. A., Zettlemoyer, L., Yih, W.-t., and Lewis, M · 2024
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