---
title: "Who cites Zhihao Jia — 376 papers of our library"
description: "376 papers of the Elamur Research library cite the works of Zhihao Jia, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/zhihao-jia/cited-by/"
---
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# Who cites Zhihao Jia

415 citations of their works from 376 papers of our library (of the 149,439 whose bibliographies we read) · 31 with a page here, the most cited first · [← Zhihao Jia](https://md.research.elamur.ai/free/en/authors/zhihao-jia/)

1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. [**The Faiss library arXiv:2401.08281**Matthijs Douze, Alexandr Guzhva, Chengqi Deng, et al.*2024 · 453 citations*](https://md.research.elamur.ai/free/en/papers/douze-2024-faiss-library/)
3. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/)
4. [**FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance arXiv:2305.05176**Lingjiao Chen, Matei Zaharia, James Zou*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-frugalgpt-use-large-language-models/)
5. **[SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://md.research.elamur.ai/free/en/papers/miao-2023-specinfer-accelerating-generative-large-language/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 220 citations*
6. **[DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency](https://md.research.elamur.ai/free/en/papers/stojkovic-2024-dynamollm-designing-llm-inference-clusters/) [Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-high-performance-computer-architecture/)**Jovan Stojkovic, Chaojie Zhang, Inigo Goiri, et al.*2024 · 218 citations*
7. [**Mesh-TensorFlow: Deep Learning for Supercomputers arXiv:1811.02084**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/)
8. **[MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://md.research.elamur.ai/free/en/papers/jiang-2024-minference-1-0-accelerating-pre/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, et al.*2024 · 202 citations*
9. **[Tool Learning with Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/qu-2024-tool-learning-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Changle Qu, Sunhao Dai, Xiaochi Wei, et al.*2024 · 195 citations*
10. [**PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation arXiv:2104.12369**Wei Zeng, Xiaozhe Ren, Teng Su, et al.*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2021-pangu-alpha-large-scale/)
11. [**PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling arXiv:2406.02069**Zefan Cai, Yichi Zhang, Bofei Gao, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-pyramidkv-dynamic-kv-cache-compression/)
12. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, et al.*2020 · 153 citations*
13. [**Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention arXiv:2502.11089**Jingyang Yuan, Huazuo Gao, Damai Dai, et al.*2025 · 142 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2025-native-sparse-attention-hardware/)
14. [**Llumnix: Dynamic Scheduling for Large Language Model Serving OSDI 2024**Biao Sun, Ziming Huang, Hanyu Zhao, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-llumnix-dynamic-scheduling-large-language/)
15. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
16. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations*
17. [**DAPPLE: A Pipelined Data Parallel Approach for Training Large Models arXiv:2007.01045**Shiqing Fan, Yi Rong, Chen Meng, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fan-2020-dapple-pipelined-data-parallel-approach/)
18. [**Accelerating Distributed MoE Training and Inference with Lina arXiv:2210.17223**Jiamin Li, Yimin Jiang, Yibo Zhu, et al.*2022 · 92 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-accelerating-distributed-moe-training-inference/)
19. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/)
20. [**Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training arXiv:2208.06102**Jie You, Jae-Won Chung, Mosharaf Chowdhury*2022 · 82 citations*](https://md.research.elamur.ai/free/en/papers/you-2022-zeus-understanding-optimizing-gpu-energy/)
21. [**TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions SOSP 2019**Zhihao Jia, Oded Padon, James Thomas, et al.*2019 · 72 citations*](https://md.research.elamur.ai/free/en/papers/jia-2019-taso-optimizing-deep-learning-computation/)
22. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
23. **[The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs](https://md.research.elamur.ai/free/en/papers/nawrot-2026-sparse-frontier-sparse-attention-trade/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Piotr Nawrot, Jianing Li, Renjie Huang, et al.*2026 · 10 citations*
24. **[HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference](https://md.research.elamur.ai/free/en/papers/zeng-2026-hybridkv-hybrid-kv-cache-compression/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bowen Zeng, Feiyang Ren, Jun Zhang, et al.*2026 · 4 citations*
25. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutao Sun, Tianzhu Ye, Li Dong, et al.*2026 · 1 citation*
26. **[SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression](https://md.research.elamur.ai/free/en/papers/yun-2026-sharvet-similarity-aware-parameter-sharing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongin Yun, Jaeri Lee, Jongjin Kim, et al.*2026 · 1 citation*
27. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026*
28. **[Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression](https://md.research.elamur.ai/free/en/papers/zhao-2026-question-tells-you-where-answer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Zhao, Xiaocheng Feng, Weihong Zhong, et al.*2026*
29. **[RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention](https://md.research.elamur.ai/free/en/papers/shi-2026-robsa-rope-based-blockwise-sparse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Shi, Kairong Luo, Zhen Zheng, et al.*2026*
30. **[TPS-Bench: Evaluating AI Agents' Tool Planning & Scheduling Abilities in Compounding Tasks](https://md.research.elamur.ai/free/en/papers/xu-2026-tps-bench-evaluating-ai-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanwen Xu, Xuyao Huang, Yuzhe Liu, et al.*2026*
31. **[Research Progress on Large Models for Edge Intelligence (面向边缘智能的大模型研究进展)](https://md.research.elamur.ai/free/en/papers/wang-2025-large-models-edge/) [Journal of Computer Research and Development](https://md.research.elamur.ai/free/en/venues/journal-of-computer-research-and-development/)**Rui Wang (王睿), Liuyang Zhang (张留洋), Zhiyong Gao (高志涌), et al.*2025*

345 more citing papers of our library have no page here yet.

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