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Large Language Models (LLMs) are difficult to fully fine-tune (e.g., with instructions or human feedback) due to their sheer number of parameters.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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The modular organization of human anatomical brain networks: Accounting for the cost of wiring
Betzel, R. F., Medaglia, J. D., Papadopoulos, L., Baum, G. L., Gur, R., Gur, R., Roalf, D., Satterthwaite, T. D., and Bassett, D. S · 2017
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Memory-efficient adaptive optimization
Anil, R., Gupta, V., Koren, T., and Singer, Y · 2019
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Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
Earlier work this paper cites.
TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages
Clark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., and Palomaki, J · 2020
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Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Pfeiffer, J., Vulić, I., Gurevych, I., and Ruder, S · 2020
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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
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Parameter-efficient transfer learning with diff pruning
Guo, D., Rush, A., and Kim, Y · 2021
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
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Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., and Peste, A · 2021
Cited alongside, same era.
The hardware lottery
Hooker, S · 2021
Cited alongside, same era.
Training neural networks with fixed sparse masks
Sung, Y.-L., Nair, V., and Raffel, C. A · 2021
Cited alongside, same era.
Composable sparse fine-tuning for cross-lingual transfer
Ansell, A., Ponti, E., Korhonen, A., and Vulić, I · 2022
Cited alongside, same era.
GPT3.int8(): 8-bit matrix multiplication for transformers at scale
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023
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Scaling down to scale up: A guide to parameter-efficient fine-tuning
Lialin, V., Deshpande, V., and Rumshisky, A · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., and Roberts, A · 2023
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OpenAI · 2023
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., and Raffel, C. A · 2022
Cited alongside, same era.
Peft: State-of-the-art parameter-efficient fine-tuning methods
Mangrulkar, S., Gugger, S., Debut, L., Belkada, Y., and Paul, S · 2022
Cited alongside, same era.
Falcon-40B: An open large language model with state-of-the-art performance
Almazrouei, E., Alobeidli, H., Alshamsi, A., Cappelli, A., Cojocaru, R., Debbah, M., Goffinet, E., Heslow, D., Launay, J., Malartic, Q., Noune, B., Pannier, B., and Penedo, G · 2023
Cited alongside, same era.
QLoRA: Efficient finetuning of quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
Cited alongside, same era.
Camels in a changing climate: Enhancing LM adaptation with Tulu 2
Ivison, H., Wang, Y., Pyatkin, V., Lambert, N., Peters, M., Dasigi, P., Jang, J., Wadden, D., Smith, N. A., Beltagy, I., and Hajishirzi, H · 2023
Cited alongside, same era.
Peng, B., Li, C., He, P., Galley, M., and Gao, J · 2023
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Modular deep learning
Pfeiffer, J., Ruder, S., Vulić, I., and Ponti, E · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E., Zhou, D., and Wei, J · 2023
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Stanford Alpaca: An instruction-following LLaMA model
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., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T · 2023
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Alpaca-LoRA: Instruct-tune LLaMA on consumer hardware
Wang, E · 2023
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How far can camels go? Exploring the state of instruction tuning on open resources
Wang, Y., Ivison, H., Dasigi, P., Hessel, J., Khot, T., Chandu, K. R., Wadden, D., MacMillan, K., Smith, N. A., Beltagy, I., and Hajishirzi, H · 2023
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