Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
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Computer Science > Computation and Language
Title:Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
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.
| Comments: | ACM SIGKDD Explorations Newsletter, Volume 28, Issue 1 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.14797 [cs.CL] |
| (or arXiv:2608.14797v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.14797
arXiv-issued DOI via DataCite (pending registration)
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