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Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse applications.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A. and Manning, C. D · 2019
Earlier work this paper cites.
Towards vqa models that can read
Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., and Rohrbach, M · 2019
Earlier work this paper cites.
Triton: an intermediate language and compiler for tiled neural network computations
Tillet, P., Kung, H.-T., and Cox, D · 2019
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
Earlier work this paper cites.
Evaluating large language models trained on code, 2021
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
Earlier work this paper cites.
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., et al · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Hawq-v3: Dyadic neural network quantization
Yao, Z., Dong, Z., Zheng, Z., Gholami, A., Yu, J., Tan, E., Wang, L., Huang, Q., Wang, Y., Mahoney, M., et al · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Truthfulqa: Measuring how models mimic human falsehoods
Lin, S., Hilton, J., and Evans, O · 2022
Earlier work this paper cites.
Bablani, D., Mckinstry, J. L., Esser, S. K., Appuswamy, R., and Modha, D. S · 2023
Cited alongside, same era.
Spqr: A sparse-quantized representation for near-lossless llm weight compression
Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., Borzunov, A., Hoefler, T., and Alistarh, D · 2023
Cited alongside, same era.
OPTQ: Accurate quantization for generative pre-trained transformers
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Awq: Activation-aware weight quantization for llm compression and acceleration
Wizardlm: Empowering large language models to follow complex instructions, 2023
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D · 2023
Later among the works it cites.
Deltazip: Multi-tenant language model serving via delta compression
Yao, X. and Klimovic, A · 2023
Later among the works it cites.
Metamath: Bootstrap your own mathematical questions for large language models
Yu, L., Jiang, W., Shi, H., Jincheng, Y., Liu, Z., Zhang, Y., Kwok, J., Li, Z., Weller, A., and Liu, W · 2023
Later among the works it cites.
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
Later among the works it cites.
Fast matrix multiplications for lookup table-quantized llms, 2024
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Lin, J., Tang, J., Tang, H., Yang, S., Dang, X., and Han, S · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Openchat: Advancing open-source language models with mixed-quality data
Wang, G., Cheng, S., Zhan, X., Li, X., Song, S., and Liu, Y · 2023
Cited alongside, same era.
Magicoder: Source code is all you need
Wei, Y., Wang, Z., Liu, J., Ding, Y., and Zhang, L · 2023
Cited alongside, same era.
Mole: Mixture of lora experts
Wu, X., Huang, S., and Wei, F · 2023
Cited alongside, same era.
Visual instruction tuning
Liu, H., Li, C., Wu, Q., and Lee, Y. J
Cited in the paper.
Bitdelta: Your fine-tune may only be worth one bit
Liu, J., Xiao, G., Li, K., Lee, J. D., Han, S., Dao, T., and Cai, T
Cited in the paper.
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
Cited in the paper.
Guo, H., Brandon, W., Cholakov, R., Ragan-Kelley, J., Xing, E. P., and Kim, Y · 2024
Closest in time.
Agile-quant: Activation-guided quantization for faster inference of llms on the edge
Shen, X., Dong, P., Lu, L., Kong, Z., Li, Z., Lin, M., Wu, C., and Wang, Y · 2024
Closest in time.
LoRA-flow: Dynamic LoRA fusion for large language models in generative tasks
Wang, H., Ping, B., Wang, S., Han, X., Chen, Y., Liu, Z., and Sun, M · 2024
Closest in time.
T-mac: Cpu renaissance via table lookup for low-bit llm deployment on edge, 2024
Wei, J., Cao, S., Cao, T., Ma, L., Wang, L., Zhang, Y., and Yang, M · 2024
Closest in time.
Language models are super mario: Absorbing abilities from homologous models as a free lunch
Yu, L., Yu, B., Yu, H., Huang, F., and Li, Y · 2024
Closest in time.