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LLM text decoding is key component for perceived LLM quality.
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Z. Yang, Z. Dai, R. Salakhutdinov, and W. W. Cohen, “Breaking the softmax bottleneck: A high-rank rnn language model,” 2018. doi: 10.48550/arXiv.1711.03953
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T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush, “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , Q. Liu and D. Schlangen, Eds. Online: Association for Computational Linguistics, Oct. 2020. doi: 10.18653/v1/2020.emnlp-demos.6 pp. 38–45. [Online]. Available: https://aclanthology.org/2020.emnlp-demos.6
2020
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A. R. Fabbri, W. Kryściński, B. McCann, C. Xiong, R. Socher, and D. Radev, “SummEval: Re-evaluating summarization evaluation,” Transactions of the Association for Computational Linguistics , vol. 9, 2021. doi: 10.1162/tacl_a_00373. [Online]. Available: https://aclanthology.org/2021.tacl-1.24
2021
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C. Meister, M. Forster, and R. Cotterell, “Determinantal beam search,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Online: Association for Computational Linguistics, Aug. 2021. doi: 10.18653/v1/2021.acl-long.512 pp. 6551–6562. [Online]. Available: https://aclanthology.org/2021.acl-long.512
2021
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M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021. doi: 10.48550/arXiv.2107.03374
2021
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J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus, “Emergent abilities of large language models,” 2022. doi: 10.48550/arXiv.2206.07682
2022
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C. Meister, T. Pimentel, G. Wiher, and R. Cotterell, “Locally typical sampling,” Transactions of the Association for Computational Linguistics , vol. 11, pp. 102–121, 2022. doi: 10.48550/arXiv.2202.00666. [Online]. Available: https://api.semanticscholar.org/CorpusID:252918666
2022
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L. Gao, J. Tow, B. Abbasi, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu, A. Le Noac’h, H. Li, K. McDonell, N. Muennighoff, C. Ociepa, J. Phang, L. Reynolds, H. Schoelkopf, A. Skowron, L. Sutawika, E. Tang, A. Thite, B. Wang, K. Wang, and A. Zou, “A framework for few-shot language model evaluation,” 12 2023. doi: 10.5281/zenodo.10256836. [Online]. Available: https://zenodo.org/records/10256836
2023
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W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. Gonzalez, H. Zhang, and I. Stoica, “Efficient memory management for large language model serving with pagedattention,” in Proceedings of the 29th Symposium on Operating Systems Principles , ser. SOSP ’23. New York, NY, USA: Association for Computing Machinery, 2023. doi: 10.1145/3600006.3613165. ISBN 9798400702297 p. 611–626. [Online]. Available: https://doi.org/10.1145/3600006.3613165
2023
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C. Shen, L. Cheng, X.-P. Nguyen, Y. You, and L. Bing, “Large language models are not yet human-level evaluators for abstractive summarization,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for Computational Linguistics, Dec. 2023. doi: 10.18653/v1/2023.findings-emnlp.278 pp. 4215–4233. [Online]. Available: https://aclanthology.org/2023.findings-emnlp.278
2023
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L. Vilnis, Y. Zemlyanskiy, P. Murray, A. Passos, and S. Sanghai, “Arithmetic sampling: parallel diverse decoding for large language models,” in Proceedings of the 40th International Conference on Machine Learning , ser. ICML’23. JMLR.org, 2023. doi: 10.5555/3618408.3619870
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J. Wang, Y. Liang, F. Meng, Z. Sun, H. Shi, Z. Li, J. Xu, J. Qu, and J. Zhou, “Is ChatGPT a good NLG evaluator? a preliminary study,” in Proceedings of the 4th New Frontiers in Summarization Workshop , Y. Dong, W. Xiao, L. Wang, F. Liu, and G. Carenini, Eds. Singapore: Association for Computational Linguistics, Dec. 2023. doi: 10.18653/v1/2023.newsum-1.1 pp. 1–11. [Online]. Available: https://aclanthology.org/2023.newsum-1.1
2023
Cited alongside, same era.
C. Shen, L. Cheng, X.-P. Nguyen, Y. You, and L. Bing, “Large language models are not yet human-level evaluators for abstractive summarization,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for Computational Linguistics, Dec. 2023. doi: 10.18653/v1/2023.findings-emnlp.278 pp. 4215–4233. [Online]. Available: https://aclanthology.org/2023.findings-emnlp.278
2023
Cited alongside, same era.
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed, “Mistral 7b,” 2023. doi: 10.48550/arXiv.2310.06825
2023
Cited alongside, same era.
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “Gptq: Accurate post-training quantization for generative pre-trained transformers,” 2023. doi: 10.48550/arXiv.2210.17323
2023
Cited alongside, same era.
P. Liu, Z. Liu, Z.-F. Gao, D. Gao, W. X. Zhao, Y. Li, B. Ding, and J.-R. Wen, “Do emergent abilities exist in quantized large language models: An empirical study,” 2023. doi: 10.48550/arXiv.2307.08072
2023
Cited alongside, same era.
2023
Later among the works it cites.
K. Pillutla, L. Liu, J. Thickstun, S. Welleck, S. Swayamdipta, R. Zellers, S. Oh, Y. Choi, and Z. Harchaoui, “Mauve scores for generative models: Theory and practice,” 2023. doi: 10.5555/3648699.3649055
2023
Later among the works it cites.
A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed, “Mixtral of experts,” 2024. doi: 10.48550/arXiv.2401.04088
2024
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D. Kim, C. Park, S. Kim, W. Lee, W. Song, Y. Kim, H. Kim, Y. Kim, H. Lee, J. Kim, C. Ahn, S. Yang, S. Lee, H. Park, G. Gim, M. Cha, H. Lee, and S. Kim, “Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling,” 2024. doi: 10.48550/arXiv.2312.15166
2024
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