Studying quantization trade-offs for efficient inference deployment in machine translation | Paper Citation Record

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This paper cites Findings of the WMT 2024 Shared Task of the Open Language Data Initiative.

Studying quantization trade-offs for efficient inference deployment in machine translation Findings of the WMT 2024 Shared Task of the Open Language Data Initiative

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This paper cites Findings of the WMT 2025 Shared Task of the Open Language Data Initiative.

Studying quantization trade-offs for efficient inference deployment in machine translation Findings of the WMT 2025 Shared Task of the Open Language Data Initiative

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Studying quantization trade-offs for efficient inference deployment in machine translation Unresolved cited work

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Studying quantization trade-offs for efficient inference deployment in machine translation Unresolved cited work

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Studying quantization trade-offs for efficient inference deployment in machine translation arXiv preprint arXiv:2509.25149 , year=

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Studying quantization trade-offs for efficient inference deployment in machine translation GPT-4 Technical Report

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Studying quantization trade-offs for efficient inference deployment in machine translation 18th USENIX symposium on operating systems design and implementation (OSDI 24) , pages=

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Studying quantization trade-offs for efficient inference deployment in machine translation Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 8

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 14th International Conference on Spoken Language Translation , pages=

Reference 9

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This paper cites Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters.

Studying quantization trade-offs for efficient inference deployment in machine translation Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters

Reference 10

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Studying quantization trade-offs for efficient inference deployment in machine translation IEEE Micro , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 28th international conference on evaluation and assessment in software engineering , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Findings of the Association for Computational Linguistics: ACL 2025 , pages=

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This paper cites Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels.

Studying quantization trade-offs for efficient inference deployment in machine translation Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

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Studying quantization trade-offs for efficient inference deployment in machine translation GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

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Studying quantization trade-offs for efficient inference deployment in machine translation Transactions of the Association for Computational Linguistics , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation Distilling the Knowledge in a Neural Network

Reference 17

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Studying quantization trade-offs for efficient inference deployment in machine translation Advances in Neural Information Processing Systems , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation 2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS) , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the Eighth Conference on Machine Translation , pages=

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This paper cites Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist.

Studying quantization trade-offs for efficient inference deployment in machine translation Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist

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Studying quantization trade-offs for efficient inference deployment in machine translation Adam: A Method for Stochastic Optimization

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 29th symposium on operating systems principles , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation

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Studying quantization trade-offs for efficient inference deployment in machine translation GetMobile: Mobile Computing and Communications , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation Evaluating Quantized Large Language Models

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Studying quantization trade-offs for efficient inference deployment in machine translation DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration

Reference 27

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Studying quantization trade-offs for efficient inference deployment in machine translation Transactions of the association for computational linguistics , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation Nature medicine , volume=

Reference 29

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Studying quantization trade-offs for efficient inference deployment in machine translation Amin and Bawden, Rachel and Zhang, Michael and Martins, Andr \'e F

Reference 30

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Studying quantization trade-offs for efficient inference deployment in machine translation The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

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Studying quantization trade-offs for efficient inference deployment in machine translation Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

Reference 32

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Studying quantization trade-offs for efficient inference deployment in machine translation The Uneven Impact of Post-Training Quantization in Machine Translation

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Studying quantization trade-offs for efficient inference deployment in machine translation Procedia Computer Science , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation ACM Computing Surveys (CSUR) , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation arXiv preprint arXiv:2602.15563 , year=

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the IEEE/CVF international conference on computer vision , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Unresolved cited work

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Studying quantization trade-offs for efficient inference deployment in machine translation Training language models to follow instructions with human feedback

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 40th annual meeting of the Association for Computational Linguistics , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the second conference on machine translation , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation Findings of the Association for Computational Linguistics: NAACL 2025 , pages=

Reference 42

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Studying quantization trade-offs for efficient inference deployment in machine translation Microscaling Data Formats for Deep Learning

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026)-Vol

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Studying quantization trade-offs for efficient inference deployment in machine translation Neurocomputing , volume=

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Studying quantization trade-offs for efficient inference deployment in machine translation Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products , year=

Reference 46

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Studying quantization trade-offs for efficient inference deployment in machine translation LLaMA: Open and Efficient Foundation Language Models

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Studying quantization trade-offs for efficient inference deployment in machine translation The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

Reference 48

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Studying quantization trade-offs for efficient inference deployment in machine translation Document-Level Machine Translation with Large Language Models

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Studying quantization trade-offs for efficient inference deployment in machine translation When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training

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Studying quantization trade-offs for efficient inference deployment in machine translation BitNet: Scaling 1-bit Transformers for Large Language Models

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Studying quantization trade-offs for efficient inference deployment in machine translation arXiv preprint arXiv:2510.13998 , year=

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Studying quantization trade-offs for efficient inference deployment in machine translation International conference on machine learning , pages=

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Studying quantization trade-offs for efficient inference deployment in machine translation arXiv preprint arXiv:2601.20088 , year=

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Studying quantization trade-offs for efficient inference deployment in machine translation 16th USENIX symposium on operating systems design and implementation (OSDI 22) , pages=

Reference 55

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Studying quantization trade-offs for efficient inference deployment in machine translation 2025 International Joint Conference on Neural Networks (IJCNN) , pages=

Reference 56

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Studying quantization trade-offs for efficient inference deployment in machine translation 2024 IEEE International Conference on Multimedia and Expo (ICME) , pages=

Reference 57

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This paper cites QQQ: Quality Quattuor-Bit Quantization for Large Language Models.

Studying quantization trade-offs for efficient inference deployment in machine translation QQQ: Quality Quattuor-Bit Quantization for Large Language Models

Reference 58

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Studying quantization trade-offs for efficient inference deployment in machine translation Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild

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