Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs | Paper Citation Record

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This paper cites OpenAI Research Paper.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs OpenAI Research Paper

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Unresolved cited work

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This paper cites W., Lee, K., Toutanova, K.: BERT: Pre-Training of Deep Bidirec- tional Transformers for Language Understanding.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs W., Lee, K., Toutanova, K.: BERT: Pre-Training of Deep Bidirec- tional Transformers for Language Understanding

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This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Sequence to Sequence Learning with Neural Networks

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs J., Marcu, D

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Hierarchical Neural Story Generation

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This paper cites The Curious Case of Neural Text Degeneration.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs The Curious Case of Neural Text Degeneration

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This paper cites In: Trans- actions of the Association for Computational Linguistics, Vol 11, pp.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Trans- actions of the Association for Computational Linguistics, Vol 11, pp

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This paper cites A Thorough Exam- ination of Decoding Methods in the Era of LLMs.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs A Thorough Exam- ination of Decoding Methods in the Era of LLMs

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This paper cites APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding

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This paper cites Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding

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This paper cites In: Proceedings of the 202 4 Conference on Empirical Methods in Natural Language Pro- cessing, pp.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 202 4 Conference on Empirical Methods in Natural Language Pro- cessing, pp

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This paper cites When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Online Speculative Decoding

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Findings of the Association for Computational Linguistics, ACL 2024, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Nearest Neighbor Speculative Decoding for LLM Generation and Attribution

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This paper cites Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs L., Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., Zettlemoyer, L., Lewis, M.: Contrastive Decoding: Open-ended Text Generation as Optimization

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This paper cites In: Proceedings of the 2024 Con- ference on North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1, Long Paper, pp.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 2024 Con- ference on North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1, Long Paper, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Contrastive Decoding Improves Reasoning in Large Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In : Pro- ceedings of the 62 nd Annual Meeting of the Association for Computational Linguistics, Vol 1, Long Papers, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Adaptive Draft-Verification for Efficient Large Language Model Decoding

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs PEARL: Parallel Speculative Decoding with Adaptive Draft Length

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs On Decoding Strategies for Neural Text Generators

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 61st Annual Meeting of the ACL, Vol 1, Long Papers, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In : Proceedings of the 1 st International Workshop on Multilin- gual, Multimodal, and Multitask Language Generation, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Language Generation with Strictly Proper Scoring Rules

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Controllable Text Generation for Large Language Models: A Survey

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Toward Inference-optimal Mixture-of-Expert Large Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Free

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC - COLING’24), pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs MiniLLM: On-Policy Distillation of Large Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs DistillSpec: Improving Speculative Decoding via Knowledge Distillation

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC - COLING’24), pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

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This paper cites In: Findings of the Association for Computa- tional Linguistics, pp.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Findings of the Association for Computa- tional Linguistics, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs CoNT: Contrastive Neural Text Generation

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This paper cites In: Proceedings of the 38th Conference on Uncertainty in Artifi- cial Intelligence, Vol 180, pp.

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 38th Conference on Uncertainty in Artifi- cial Intelligence, Vol 180, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Unresolved cited work

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Semi-Autoregressive Training Improves Mask-Predict Decoding

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Learning to Generate Better Than Your LLM

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Controlled Decoding from Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs In: Proceedings of the 2024 Con- ference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 2: Short Papers, pp

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs A., Lange, L., Adel, H., Strötgen, J., Klakow, D.: A Survey on Recent Approaches for Natural Language Processing in Low -Resource Scenarios

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Complex & Intelligent Systems, Vol 11, Article No: 57

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

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Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft Heads with Adversarial Learning

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