Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models | SpinGraph: Efficiency framing
SpinGraph analysis of arXiv Computation and Language's Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models story: efficie…
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Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models
decoding methods, inference-time alignment, LLM survey, LVLM
Introduces a taxonomy of three emerging decoding paradigms for LLMs/LVLMs Positions inference-time decoding as more efficient and scalable than training-stage alignment Provides open resources (GitHub repo) for practitioners and researchers
Emphasizes scalability and efficiency while minimizing discussion of accuracy degradation, computational overhead per method, or lack of standardized evaluation across studies.
Technical stewardship — positioning the authors as curators and systematizers of an emerging, high-leverage inference optimization frontier.
Decoding methods are an efficient, scalable way to align LLM and vision-language model outputs with user intent during inference.
Benchmark results comparing decoding methods on alignment fidelity (e.g., truthfulness, instruction following); Hardware or memory constraints limiting real-world applicability; Method-specific failure modes (e.g., hallucination amplification under beam search variants)
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as efficient, scalable, emerging paradigms, practical view. The distribution reads as academic distribution. A pressure point: Benchmark results comparing decoding methods on alignment fidelity (e.g., truthfulness, instruction following).
Decoding methods offer a more efficient and scalable solution to ensuring LLM and LVLM outputs align with user intent.
While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods offer a more efficient and scalable solution.
Extracted statistics from the source narrative
Identified in the survey's systematic review