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This survey systematically reviews decoding methods designed to improve the efficiency, alignment, and scalability of large language and vision-language models during inference. By shifting focus from training-stage adjustments to token-level selection strategies, these techniques offer a practical pathway for optimizing generative AI performance without altering model weights. The analysis highlights emerging paradigms that address critical challenges in controlling output generation while accelerating processing speeds.
Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
arXiv:2608.14797v1 Announce Type: new Abstract: Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging. While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods offer a more efficient and scalable solution. Decoding methods control model generation by guiding token-level selection, performing sequence-level generation, or generating tokens in parallel to accelerate the process. In this survey, we identify three emerging paradigms from recent works on decoding methods for LLMs and LVLMs, provide a systematic review of these methods, highlight ongoing challenges, and discuss potential future research directions. Our goal is to underscore the efficiency and effectiveness of decoding methods and offer a practical view of their applications. Paper lists and more resources on decoding methods for LLMs and LVLMs can be found at https://github.com/wang2226/Awesome-LLM-Decoding.
Abstract:Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging. While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods offer a more efficient and scalable solution. Decoding methods control model generation by guiding token-level selection, performing sequence-level generation, or generating tokens in parallel to accelerate the process. In this survey, we identify three emerging paradigms from recent works on decoding methods for LLMs and LVLMs, provide a systematic review of these methods, highlight ongoing challenges, and discuss potential future research directions. Our goal is to underscore the efficiency and effectiveness of decoding methods and offer a practical view of their applications. Paper lists and more resources on decoding methods for LLMs and LVLMs can be found at this https URL.
Computation and Language (cs.CL)
arXiv:2608.14797 [cs.CL]
(or arXiv:2608.14797v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.14797 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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