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Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs).
Some studies in machine learning using the game of checkers
Samuel, A. L · 1959
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The strength of weak learnability
Schapire, R. E · 1990
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Cryptographic limitations on learning boolean formulae and finite automata
Kearns, M. and Valiant, L · 1994
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Boosting a weak learning algorithm by majority
Freund, Y · 1995
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Temporal difference learning and td-gammon
Tesauro, G. et al · 1995
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y. and Schapire, R. E · 1997
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Integral probability metrics and their generating classes of functions
Müller, A · 1997
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The nature of statistical learning theory
Vapnik, V · 1999
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Some studies in machine learning using the game of checkers
Samuel, A. L · 2000
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Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y · 2004
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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Baby steps: How “less is more” in unsupervised dependency parsing
Spitkovsky, V. I., Alshawi, H., and Jurafsky, D · 2009
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Self-paced learning for latent variable models
Kumar, M., Packer, B., and Koller, D · 2010
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Learning the easy things first: Self-paced visual category discovery
Lee, Y. J. and Grauman, K · 2011
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Hinton, G., Srivastava, N., and Swersky, K · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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A self-paced multiple-instance learning framework for co-saliency detection
Zhang, D., Meng, D., Li, C., Jiang, L., Zhao, Q., and Han, J · 2015
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Visualizing and understanding curriculum learning for long short-term memory networks
Cirik, V., Hovy, E., and Morency, L.-P · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Thinking fast and slow with deep learning and tree search
Anthony, T., Tian, Z., and Barber, D · 2017
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D., and Graepel, T · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S · 2017
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Fisher gan
Mroueh, Y. and Sercu, T · 2017
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Emergent complexity via multi-agent competition
Bansal, T., Pachocki, J., Sidor, S., Sutskever, I., and Mordatch, I · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Is multiagent deep reinforcement learning the answer or the question? a brief survey
Hernandez-Leal, P., Kartal, B., and Taylor, M. E · 2018
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The relativistic discriminator: a key element missing from standard gan
Jolicoeur-Martineau, A · 2018
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Curriculum learning for natural answer generation
Liu, C., He, S., Liu, K., Zhao, J., et al · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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An empirical exploration of curriculum learning for neural machine translation
Zhang, X., Kumar, G., Khayrallah, H., Murray, K., Gwinnup, J., Martindale, M. J., McNamee, P., Duh, K., and Carpuat, M · 2018
Cited alongside, same era.
A generalized training approach for multiagent learning
Muller, P., Omidshafiei, S., Rowland, M., Tuyls, K., Perolat, J., Liu, S., Hennes, D., Marris, L., Lanctot, M., Hughes, E., et al · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Scaling multimodal pre-training via cross-modality gradient harmonization
Wu, J., Liang, Y., Akbari, H., Wang, Z., Yu, C., et al · 2022
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Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
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Open llm leaderboard, 2023
Beeching, E., Fourrier, C., Habib, N., Han, S., Lambert, N., Rajani, N., Sanseviero, O., Tunstall, L., and Wolf, T · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
bench authors, B · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
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AlphaStar: Mastering the Real-Time Strategy Game StarCraft II, 2019
Vinyals, O., Babuschkin, I., Chung, J., Mathieu, M., Jaderberg, M., Czarnecki, W., Dudzik, A., Huang, A., Georgiev, P., Powell, R., Ewalds, T., Horgan, D., Kroiss, M., Danihelka, I., Agapiou, J., Oh, J., Dalibard, V., Choi, D., Sifre, L., Sulsky, Y., Vezhnevets, S., Molloy, J., Cai, T., Budden, D., Paine, T., Gulcehre, C., Wang, Z., Pfaff, T., Pohlen, T., Yogatama, D., Cohen, J., McKinney, K., Smith, O., Schaul, T., Lillicrap, T., Apps, C., Kavukcuoglu, K., Hassabis, D., and Silver, D · 2019
Cited alongside, same era.
Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
Cited alongside, same era.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
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.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Cited alongside, same era.
Zero: Memory optimizations toward training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y · 2020
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Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Burns, C., Izmailov, P., Kirchner, J. H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al · 2023
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Adversarial preference optimization, 2023
Cheng, P., Yang, Y., Li, J., Dai, Y., and Du, N · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
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Ultrafeedback: Boosting language models with high-quality feedback, 2023
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Dao, T · 2023
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Rephrase and respond: Let large language models ask better questions for themselves
Deng, Y., Zhang, W., Chen, Z., and Gu, Q · 2023
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Enhancing chat language models by scaling high-quality instructional conversations
Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., Liu, Z., Sun, M., and Zhou, B · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Josifoski, M., Sakota, M., Peyrard, M., and West, R · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Lee, H., Phatale, S., Mansoor, H., Lu, K., Mesnard, T., Bishop, C., Carbune, V., and Rastogi, A · 2023
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Textbooks are all you need ii: phi-1.5 technical report, 2023
Li, Y., Bubeck, S., Eldan, R., Giorno, A. D., Gunasekar, S., and Lee, Y. T · 2023
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Tinygsm: achieving> 80% on gsm8k with small language models
Liu, B., Bubeck, S., Eldan, R., Kulkarni, J., Li, Y., Nguyen, A., Ward, R., and Zhang, Y · 2023
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Luo, H., Sun, Q., Xu, C., Zhao, P., Lou, J., Tao, C., Geng, X., Lin, Q., Chen, S., and Zhang, D · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Rephrase, augment, reason: Visual grounding of questions for vision-language models
Prasad, A., Stengel-Eskin, E., and Bansal, M · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
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Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
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Beyond human data: Scaling self-training for problem-solving with language models
Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Liu, P. J., Harrison, J., Lee, J., Xu, K., Parisi, A., et al · 2023
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Stanford alpaca: An instruction-following llama model, 2023
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 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
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Some things are more cringe than others: Preference optimization with the pairwise cringe loss
Xu, J., Lee, A., Sukhbaatar, S., and Weston, J · 2023
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Decoding data quality via synthetic corruptions: Embedding-guided pruning of code data
Yang, Y., Singh, A. K., Elhoushi, M., Mahmoud, A., Tirumala, K., Gloeckle, F., Rozière, B., Wu, C.-J., Morcos, A. S., and Ardalani, N · 2023
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Metamath: Bootstrap your own mathematical questions for large language models
Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J. T., Li, Z., Weller, A., and Liu, W · 2023
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Scaling relationship on learning mathematical reasoning with large language models
Yuan, Z., Yuan, H., Li, C., Dong, G., Tan, C., and Zhou, C · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J · 2024
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