LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching

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2018

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D. Stamoulis, R. Ding, D. Wang, D. Lymberopoulos, B. Priyantha, J. Liu, and D. Marculescu, “Single-path nas: Designing hardware-efficient convnets in less than 4 hours,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2019, pp. 481–497

2019

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C. Guo, Z. Tian, J. Tang, S. Li, Z. Wen, K. Wang, and T. Wang, “Retrieval-augmented gpt-3.5-based text-to-sql framework with sample-aware prompting and dynamic revision chain,” in International Conference on Neural Information Processing . Springer, 2023, pp. 341–356

2023

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S. Kim, S. Moon, R. Tabrizi, N. Lee, M. W. Mahoney, K. Keutzer, and A. Gholami, “An llm compiler for parallel function calling,” arXiv preprint arXiv:2312.04511 , 2023

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2023

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X. Ning, Z. Lin, Z. Zhou, H. Yang, and Y. Wang, “Skeleton-of-thought: Large language models can do parallel decoding,” arXiv preprint arXiv:2307.15337 , 2023

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2023

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X. Ma, G. Fang, and X. Wang, “Llm-pruner: On the structural pruning of large language models,” Advances in neural information processing systems , vol. 36, pp. 21 702–21 720, 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 , 2023, pp. 611–626

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H. Jiang, Q. Wu, C.-Y. Lin, Y. Yang, and L. Qiu, “Llmlingua: Compressing prompts for accelerated inference of large language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 13 358–13 376

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C. Packer, V. Fang, S. G. Patil, K. Lin, S. Wooders, and J. E. Gonzalez, “Memgpt: Towards llms as operating systems,” arXiv preprint arXiv:2310.08560 , 2023

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2023

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Y. Zhuang, Y. Yu, K. Wang, H. Sun, and C. Zhang, “Toolqa: A dataset for llm question answering with external tools,” Advances in Neural Information Processing Systems , vol. 36, 2024

2024

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Y. Qin, S. Liang et al. , “Toolllm: Facilitating large language models to master 16000+ real-world apis,” International Conference on Learning Representations , 2024

2024

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S. Singh, G. Pavlakos, and D. Stamoulis, “Evaluating zero-shot gpt-4v performance on 3d visual question answering benchmarks,” arXiv preprint arXiv:2405.18831 , 2024

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2024

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S. Zhou, F. F. Xu, H. Zhu, X. Zhou, R. Lo, A. Sridhar, X. Cheng, T. Ou, Y. Bisk, D. Fried, U. Alon, and G. Neubig, “Webarena: A realistic web environment for building autonomous agents,” in ICLR 2024 Foundation Models for Decision Making , 2024

2024

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M. Fore, S. Singh, and D. Stamoulis, “Geckopt: Llm system efficiency via intent-based tool selection,” in Proceedings of the Great Lakes Symposium on VLSI 2024 , 2024, pp. 353–354

2024

Closest in time.

S. Singh, M. Fore, and D. Stamoulis, “Geollm-engine: A realistic environment for building geospatial copilots,” in CVPR Workshop EarthVision , 2024

2024

Closest in time.

T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,” Advances in Neural Information Processing Systems , vol. 36, 2024

2024

Closest in time.

M. Fore, S. Singh, C. Lee, A. Pandey, A. Anastasopoulos, and D. Stamoulis, “Unlearning climate misinformation in large language models,” in Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) . ACL, 2024

2024

Closest in time.