Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
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Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
| Challenge: | Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding. |
| Approach: | They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding . |
| Outcome: | The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models. |
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| Challenge: | Large language models (LLMs) have a largely increased latency due to their ability to autoregressively model . speculative decoding is a technique that trades generation quality for speed . |
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Decoding Speculative Decoding (2025.naacl-long)
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| Challenge: | Speculative decoding is a widely used technique to speed up inference for Large Language Models (LLMs) Autoregressive decoding has been known to be hardware inefficient, leading to poor resource utilization and low throughput during inference. |
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings. |
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& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
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| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
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| Challenge: | Large Language Models (LLMs) are highly memory-intensive when performing real-time inference. |
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| Challenge: | Existing studies focus on 7B-70B parameters models, leaving a knowledge gap for small language models. |
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Speculative Contrastive Decoding (2024.acl-short)
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| Challenge: | Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. |
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| Challenge: | Existing approaches to inference with Large Language Models (LLMs) are expensive and time-consuming. |
| Approach: | They propose a framework for accelerating large language model inference without additional training or modification to the original LLM. |
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EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models (2025.naacl-long)
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| Challenge: | Speculative decoding is a key technique for enhancing the inference speed of Large Language Models. |
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Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion (2025.naacl-long)
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Jacob K Christopher, Brian R. Bartoldson, Tal Ben-Nun, Michael Cardei, Bhavya Kailkhura, Ferdinando Fioretto
| Challenge: | Existing methods to accelerate large language model inference are limited by the reliance on incremental token generation in existing draft models. |
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