id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3b4607797a56c785e0912b27a890e8bf85f3f3e29811ba5de7c9a8196c6caa68 | [
"arxiv",
"semantic_scholar"
] | Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification | Recent works have revealed the great potential of speculative decoding in accelerating the autoregressive generation process of large language models. The success of these methods relies on the alignment between draft candidates and the sampled outputs of the target model. Existing methods mainly achieve draft-target a... | [
"Jikai Wang",
"Zhenxu Tian",
"Juntao Li",
"Qingrong Xia",
"Xinyu Duan",
"Zhefeng Wang",
"Baoxing Huai",
"Min Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-19T00:00:00 | https://arxiv.org/abs/2505.13204 | https://arxiv.org/pdf/2505.13204v2 | 2505.13204 | 10.48550/arXiv.2505.13204 | 8 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2386 |
4eeda6b53d1657b1cf7de184f5e1f06c5b24026fc731a4fa38b9fa0c60ed494a | [
"arxiv",
"semantic_scholar"
] | Traversal Verification for Speculative Tree Decoding | Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target model for multiple subsequent timesteps, and then verify them in parallel to determine whether the drafted tokens should be accepted or rejected... | [
"Yepeng Weng",
"Qiao Hu",
"Xujie Chen",
"Li Liu",
"Dianwen Mei",
"Huishi Qiu",
"Jiang Tian",
"Zhongchao Shi"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-18T00:00:00 | https://arxiv.org/abs/2505.12398 | https://arxiv.org/pdf/2505.12398v2 | 2505.12398 | 10.48550/arXiv.2505.12398 | 6 | 0 | false | null | arXiv.org | 0.2113 |
84de75f78a7bda16010ed8a9e7b3da7528cb019d3602670e25ee17ef2db45363 | [
"arxiv",
"semantic_scholar"
] | SpecMemo: Speculative Decoding is in Your Pocket | Recent advancements in speculative decoding have demonstrated considerable speedup across a wide array of large language model (LLM) tasks. Speculative decoding inherently relies on sacrificing extra memory allocations to generate several candidate tokens, of which acceptance rate drives the speedup. However, deploying... | [
"Selin Yildirim",
"Deming Chen"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-16T00:00:00 | https://arxiv.org/abs/2506.01986 | https://arxiv.org/pdf/2506.01986v1 | 2506.01986 | 10.48550/arXiv.2506.01986 | 0 | 0 | false | null | arXiv.org | 0.1616 |
7d116ea662927547c9a1fdfe569b8c728f1dd63762080583f87a7283d7243c1e | [
"arxiv",
"semantic_scholar"
] | SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism | Recently, speculative decoding (SD) has emerged as a promising technique to accelerate LLM inference by employing a small draft model to propose draft tokens in advance, and validating them in parallel with the large target model. However, the existing SD methods still remain fundamentally constrained by their serializ... | [
"Yuhao Shen",
"Junyi Shen",
"Quan Kong",
"Tianyu Liu",
"Yao Lu",
"Cong Wang"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-16T00:00:00 | https://arxiv.org/abs/2506.01979 | https://arxiv.org/pdf/2506.01979v4 | 2506.01979 | null | 12 | 0 | false | null | null | 0.2785 |
0a32a41125d14672f832133dfa4090cc77dea1d1b6035f7e47382f9998d02a91 | [
"arxiv",
"semantic_scholar"
] | MASSV: Multimodal Adaptation and Self-Data Distillation for Speculative Decoding of Vision-Language Models | Speculative decoding significantly accelerates language model inference by enabling a lightweight draft model to propose multiple tokens that a larger target model verifies simultaneously. However, applying this technique to vision-language models (VLMs) presents two fundamental challenges: small language models that c... | [
"Mugilan Ganesan",
"Shane Segal",
"Ankur Aggarwal",
"Nish Sinnadurai",
"Sean Lie",
"Vithursan Thangarasa"
] | [
"cs.LG",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-15T00:00:00 | https://arxiv.org/abs/2505.10526 | https://arxiv.org/pdf/2505.10526v2 | 2505.10526 | 10.48550/arXiv.2505.10526 | 0 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1604 |
5e6c3e7feb57d72657e9ec2437560c424ddd21c135dc1bce2937a2bb410cdbc2 | [
"arxiv",
"semantic_scholar"
] | SpecOffload: Unlocking Latent GPU Capacity for LLM Inference on Resource-Constrained Devices | Efficient LLM inference on resource-constrained devices presents significant challenges in compute and memory utilization. Due to limited GPU memory, existing systems offload model weights to CPU memory, incurring substantial I/O overhead between the CPU and GPU. This leads to two major inefficiencies: (1) GPU cores ar... | [
"Xiangwen Zhuge",
"Xu Shen",
"Zeyu Wang",
"Fan Dang",
"Xuan Ding",
"Danyang Li",
"Yahui Han",
"Tianxiang Hao",
"Zheng Yang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-15T00:00:00 | https://arxiv.org/abs/2505.10259 | https://arxiv.org/pdf/2505.10259v3 | 2505.10259 | 10.48550/arXiv.2505.10259 | 1 | 1 | true | https://github.com/MobiSense/SpecOffload-public | arXiv.org | 0.2479 |
3128cc6a9ba2fa0fd63a91c1e8ff9eaa9673b7930f229f6ba2546c7587b6533b | [
"arxiv",
"semantic_scholar"
] | ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor | We propose ELIS, a serving system for Large Language Models (LLMs) featuring an Iterative Shortest Remaining Time First (ISRTF) scheduler designed to efficiently manage inference tasks with the shortest remaining tokens. Current LLM serving systems often employ a first-come-first-served scheduling strategy, which can l... | [
"Seungbeom Choi",
"Jeonghoe Goo",
"Eunjoo Jeon",
"Mingyu Yang",
"Minsung Jang"
] | [
"cs.DC",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-14T00:00:00 | https://arxiv.org/abs/2505.09142 | https://arxiv.org/pdf/2505.09142v1 | 2505.09142 | 10.48550/arXiv.2505.09142 | 7 | 0 | false | null | arXiv.org | 0.2258 |
84dd8a389eaf47c74725765a7b041096988401415ebd69f559639b91f423d5f5 | [
"arxiv",
"semantic_scholar"
] | Automatic Task Detection and Heterogeneous LLM Speculative Decoding | Speculative decoding, which combines a draft model with a target model, has emerged as an effective approach to accelerate large language model (LLM) inference. However, existing methods often face a trade-off between the acceptance rate and decoding speed in downstream tasks due to the limited capacity of the draft mo... | [
"Danying Ge",
"Jianhua Gao",
"Qizhi Jiang",
"Yifei Feng",
"Weixing Ji"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-13T00:00:00 | https://arxiv.org/abs/2505.08600 | https://arxiv.org/pdf/2505.08600v1 | 2505.08600 | 10.48550/arXiv.2505.08600 | 0 | 0 | false | null | arXiv.org | 0.1581 |
af0e472eecc7b3b4416d6be55cbbe9eec7bb07a4cf3df5ee65cf7e2bdfb979dc | [
"arxiv",
"semantic_scholar"
] | SpecRouter: Adaptive Routing for Multi-Level Speculative Decoding in Large Language Models | Large Language Models (LLMs) present a critical trade-off between inference quality and computational cost: larger models offer superior capabilities but incur significant latency, while smaller models are faster but less powerful. Existing serving strategies often employ fixed model scales or static two-stage speculat... | [
"Hang Wu",
"Jianian Zhu",
"Yinghui Li",
"Haojie Wang",
"Biao Hou",
"Jidong Zhai"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-12T00:00:00 | https://arxiv.org/abs/2505.07680 | https://arxiv.org/pdf/2505.07680v1 | 2505.07680 | 10.48550/arXiv.2505.07680 | 1 | 0 | false | null | arXiv.org | 0.157 |
ff5672cdcffa20de10c8d92ecac2d9842be0fad501fc4b43442304f0ec7a87e3 | [
"arxiv",
"semantic_scholar"
] | Scaling Laws for Speculative Decoding | The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which depend on extended chain-of-thought reasoning. This study investigates speculative decoding techniques through dense LLM architectures to esta... | [
"Siyuan Yan",
"Mo Zhu",
"Guo-qing Jiang",
"Jianfei Wang",
"Jiaxing Chen",
"Wentai Zhang",
"Xiang Liao",
"Xiao Cui",
"Chen Zhang",
"Zhuoran Song",
"Ran Zhu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-08T00:00:00 | https://arxiv.org/abs/2505.07858 | https://arxiv.org/pdf/2505.07858v1 | 2505.07858 | 10.48550/arXiv.2505.07858 | 3 | 1 | false | null | arXiv.org | 0.1524 |
aa29d28a1f46f1a2de01f61cdd67faf40632c4aad43f6f425c9591867abada65 | [
"arxiv",
"semantic_scholar"
] | SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding | This study addresses key challenges in developing domain-specific large language models (LLMs) for Chinese state-owned assets and enterprises (SOAEs), where current approaches face three limitations: 1) constrained model capacity that limits knowledge integration and cross-task adaptability; 2) excessive reliance on do... | [
"Jingyang Deng",
"Ran Chen",
"Jo-Ku Cheng",
"Jinwen Ma"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-07T00:00:00 | https://arxiv.org/abs/2505.04723 | https://arxiv.org/pdf/2505.04723v1 | 2505.04723 | 10.48550/arXiv.2505.04723 | 1 | 0 | false | null | arXiv.org | 0.1513 |
20f8beb58b7db52d32910b1513853ab3014089ebbac64d56bafe19b8a3170e5f | [
"arxiv",
"semantic_scholar"
] | PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding | Speculative decoding accelerates large language model inference by using smaller draft models to generate candidate tokens for parallel verification. However, current approaches are limited by sequential stage dependencies that prevent full hardware utilization. We present PipeSpec, a framework that generalizes specula... | [
"Bradley McDanel",
"Sai Qian Zhang",
"Yunhai Hu",
"Zining Liu"
] | [
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-02T00:00:00 | https://arxiv.org/abs/2505.01572 | https://arxiv.org/pdf/2505.01572v1 | 2505.01572 | 10.48550/arXiv.2505.01572 | 5 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1945 |
9752dc211fc57f85d7976f78d0d672dca1ab107ba0d1e5e6aa7e933709c15bda | [
"arxiv",
"semantic_scholar"
] | Speculative Sampling via Exponential Races | Speculative decoding accelerates large language model inference using a smaller draft model. In this paper, we establish a surprising connection between speculative decoding and channel simulation, which aims at simulating a noisy channel using as few bits as possible. This connection allows us to provide an informatio... | [
"Szymon Kobus",
"Deniz Gündüz"
] | [
"cs.CL",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-04-21T00:00:00 | https://arxiv.org/abs/2504.15475 | https://arxiv.org/pdf/2504.15475v1 | 2504.15475 | 10.48550/arXiv.2504.15475 | 1 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1329 |
8095e57bc85578032b7dc5eb627c54186cb79f0999ad826555f43ab05efbe4b7 | [
"arxiv",
"semantic_scholar"
] | MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference | Modern LLM serving now spans multi-stage pipelines including RAG retrieval and KV cache reuse, each with distinct compute, memory, and latency demands. Inference engines expose a large configuration space with no systematic navigation methodology, and exhaustively benchmarking configurations can exceed 40K in cloud cos... | [
"Abhimanyu Rajeshkumar Bambhaniya",
"Hanjiang Wu",
"Suvinay Subramanian",
"Sudarshan Srinivasan",
"Souvik Kundu",
"Amir Yazdanbakhsh",
"Midhilesh Elavazhagan",
"Madhu Kumar",
"Minlan Yu",
"Arijit Raychowdhury",
"Tushar Krishna"
] | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-04-14T00:00:00 | https://arxiv.org/abs/2504.09775 | https://arxiv.org/pdf/2504.09775v6 | 2504.09775 | null | 2 | 0 | false | null | null | 0.1193 |
1792d10cbdf711c47d677c52532ffdf6e7b1c0f551972561156d287ecedf90b7 | [
"arxiv",
"semantic_scholar"
] | SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning | Recent advances in inference-time compute have significantly improved performance on complex tasks by generating long chains of thought (CoTs) using Large Reasoning Models (LRMs). However, this improved accuracy comes at the cost of high inference latency due to the length of generated reasoning sequences and the autor... | [
"Rui Pan",
"Yinwei Dai",
"Zhihao Zhang",
"Gabriele Oliaro",
"Zhihao Jia",
"Ravi Netravali"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-10T00:00:00 | https://arxiv.org/abs/2504.07891 | https://arxiv.org/pdf/2504.07891v2 | 2504.07891 | 10.48550/arXiv.2504.07891 | 57 | 12 | true | https://github.com/ruipeterpan/specreason | arXiv.org | 0.557 |
915bb99440a5c9610ec1a3bea89bcf3481b8232a3aa737c429cab050c6b7dd56 | [
"arxiv",
"semantic_scholar"
] | SPIRe: Boosting LLM Inference Throughput with Speculative Decoding | Speculative decoding (SD) has been shown to reduce the latency of autoregressive decoding (AD) by 2-3x for small batch sizes. However, increasing throughput and therefore reducing the cost per token requires decoding with large batch sizes. Recent work shows that SD can accelerate decoding with large batch sizes too if... | [
"Sanjit Neelam",
"Daniel Heinlein",
"Vaclav Cvicek",
"Akshay Mishra",
"Reiner Pope"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.06419 | https://arxiv.org/pdf/2504.06419v1 | 2504.06419 | 10.48550/arXiv.2504.06419 | 0 | 0 | false | null | arXiv.org | 0.118 |
7433730f6f8e81b2e4c9b38b2e2ea86ef83a71281ae262eeab79901d5b6c3346 | [
"arxiv",
"semantic_scholar"
] | DEL: Context-Aware Dynamic Exit Layer for Efficient Self-Speculative Decoding | Speculative Decoding (SD) is a widely used approach to accelerate the inference of large language models (LLMs) without reducing generation quality. It operates by first using a compact model to draft multiple tokens efficiently, followed by parallel verification using the target LLM. This approach leads to faster infe... | [
"Hossein Entezari Zarch",
"Lei Gao",
"Chaoyi Jiang",
"Murali Annavaram"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.05598 | https://arxiv.org/pdf/2504.05598v2 | 2504.05598 | 10.48550/arXiv.2504.05598 | 6 | 2 | true | https://github.com/hoenza/DEL | arXiv.org | 0.2386 |
ecb96502b71d3bbac2182da1478f26fd93d76133bc5925d09aecfd890f732b71 | [
"arxiv",
"semantic_scholar"
] | SpecPipe: Accelerating Pipeline Parallelism-based LLM Inference with Speculative Decoding | The demand for large language model inference is rapidly increasing. Pipeline parallelism offers a cost-effective deployment strategy for distributed inference but suffers from high service latency. While incorporating speculative decoding to pipeline parallelism improves performance, it still faces challenges of low h... | [
"Haofei Yin",
"Mengbai Xiao",
"Tinghong Li",
"Xiao Zhang",
"Dongxiao Yu",
"Guanghui Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-05T00:00:00 | https://arxiv.org/abs/2504.04104 | https://arxiv.org/pdf/2504.04104v2 | 2504.04104 | null | 4 | 0 | false | null | null | 0.1747 |
02f2ea2ebb8810f78d0f4caa8723904d254e6c8c1448c4f66cab6a98dfac0760 | [
"arxiv",
"semantic_scholar"
] | Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding | Speculative decoding accelerates large language model (LLM) inference by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, selecting an optimal speculation length is critical for maximizing speedup while minimizing wasted computation. We introduce \textit{GammaTun... | [
"Aayush Gautam",
"Susav Shrestha",
"Narasimha Reddy"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-28T00:00:00 | https://arxiv.org/abs/2504.00030 | https://arxiv.org/pdf/2504.00030v3 | 2504.00030 | 10.48550/arXiv.2504.00030 | 2 | 0 | false | null | arXiv.org | 0.1193 |
23e13f6df3b0fa061484c1c935a46ab6730e4b6af458f300a07d991f10293b61 | [
"arxiv",
"semantic_scholar"
] | Collab: Controlled Decoding using Mixture of Agents for LLM Alignment | Alignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effective technique to align LLMs to human preferences and broader utilities, but it requires updating billions of model parameters, which is comp... | [
"Souradip Chakraborty",
"Sujay Bhatt",
"Udari Madhushani Sehwag",
"Soumya Suvra Ghosal",
"Jiahao Qiu",
"Mengdi Wang",
"Dinesh Manocha",
"Furong Huang",
"Alec Koppel",
"Sumitra Ganesh"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-27T00:00:00 | https://arxiv.org/abs/2503.21720 | https://arxiv.org/pdf/2503.21720v1 | 2503.21720 | 10.48550/arXiv.2503.21720 | 19 | 1 | true | null | International Conference on Learning Representations | 0.3253 |
00471c250e77fe0be58a76d2b261c4723d1f5b7b308d115d15a14208d444eaeb | [
"arxiv",
"semantic_scholar"
] | Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence | Recent advances in reasoning models have demonstrated significant improvements in accuracy by employing detailed and comprehensive reasoning processes. However, generating these lengthy reasoning sequences is computationally expensive and time-consuming. To address this inefficiency, we leverage the inherent paralleliz... | [
"Yijiong Yu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-26T00:00:00 | https://arxiv.org/abs/2503.20533 | https://arxiv.org/pdf/2503.20533v4 | 2503.20533 | 10.48550/arXiv.2503.20533 | 4 | 0 | true | https://github.com/yuyijiong/parallel-decoding-in-one-sequence | Conference on Empirical Methods in Natural Language Processing | 0.1747 |
3875b7afdcb3d338913a88b0842ee39ee285a2dedcc979a1a7700f14ece8b39a | [
"arxiv",
"semantic_scholar"
] | A Multi-Model Adaptation of Speculative Decoding for Classification | The current study introduces a novel adaptation of speculative decoding, repurposed from generation to classification tasks. We propose a multi-model framework employing up to three lightweight worker models and a single, more robust judge model analogous to draft models and target model, respectively, in speculative d... | [
"Somnath Roy",
"Padharthi Sreekar",
"Srivatsa Narasimha",
"Anubhav Anand"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-23T00:00:00 | https://arxiv.org/abs/2503.18076 | https://arxiv.org/pdf/2503.18076v1 | 2503.18076 | 10.48550/arXiv.2503.18076 | 0 | 0 | false | null | arXiv.org | 0.0997 |
8499012a8b1c554a4dca5e69b2c021fc1a6ec1d255aa7fd91ea53a1c2aa8c289 | [
"arxiv",
"semantic_scholar"
] | SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models | Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in a so-called speculation phase, which are subsequently verified by the LLM in a verification phase. However, current state-of-the-art specula... | [
"Fahao Chen",
"Peng Li",
"Tom H. Luan",
"Zhou Su",
"Jing Deng"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-03-20T00:00:00 | https://arxiv.org/abs/2503.15921 | https://arxiv.org/pdf/2503.15921v1 | 2503.15921 | 10.1109/INFOCOM55648.2025.11044522 | 14 | 0 | false | null | IEEE Conference on Computer Communications | 0.294 |
0c403e5639e006c5d8916f31b1e6f74a4fee97444e3a51081acf4cabb849927c | [
"arxiv",
"semantic_scholar"
] | SpeCache: Speculative Key-Value Caching for Efficient Generation of LLMs | Transformer-based large language models (LLMs) have already achieved remarkable results on long-text tasks, but the limited GPU memory (VRAM) resources struggle to accommodate the linearly growing demand for key-value (KV) cache as the sequence length increases, which has become a bottleneck for the application of LLMs... | [
"Shibo Jie",
"Yehui Tang",
"Kai Han",
"Zhi-Hong Deng",
"Jing Han"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-20T00:00:00 | https://arxiv.org/abs/2503.16163 | https://arxiv.org/pdf/2503.16163v1 | 2503.16163 | 10.48550/arXiv.2503.16163 | 5 | 0 | false | null | International Conference on Machine Learning | 0.1945 |
138aa166ad0bd48ec771b6ccd2e0111183c5a0d27dc21568bb9146cb9db0f511 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding for Verilog: Speed and Quality, All in One | The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programming languages, particularly those like Verilog with specific syntax and lower representation in training datasets, pose significant challeng... | [
"Changran Xu",
"Yi Liu",
"Yunhao Zhou",
"Shan Huang",
"Ningyi Xu",
"Qiang Xu"
] | [
"cs.LG",
"cs.AR",
"cs.CL"
] | [
"Computer Science"
] | 2025-03-18T00:00:00 | https://arxiv.org/abs/2503.14153 | https://arxiv.org/pdf/2503.14153v1 | 2503.14153 | 10.1109/DAC63849.2025.11133030 | 2 | 0 | false | null | Design Automation Conference | 0.1193 |
4577ae26bc97202f5a95529643c096ad80ed4cb0c09395ca7d7c8d4d9071b16a | [
"arxiv",
"semantic_scholar"
] | ML-SpecQD: Multi-Level Speculative Decoding with Quantized Drafts | Speculative decoding (SD) has emerged as a method to accelerate LLM inference without sacrificing any accuracy over the 16-bit model inference. In a typical SD setup, the idea is to use a full-precision, small, fast model as "draft" to generate the next few tokens and use the "target" large model to verify the draft-ge... | [
"Evangelos Georganas",
"Dhiraj Kalamkar",
"Alexander Kozlov",
"Alexander Heinecke"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-17T00:00:00 | https://arxiv.org/abs/2503.13565 | https://arxiv.org/pdf/2503.13565v1 | 2503.13565 | 10.48550/arXiv.2503.13565 | 7 | 0 | false | null | arXiv.org | 0.2258 |
c6bb892be526c541d84bfbe0e37fe50302d8fc407564b41896bd22a5398705ec | [
"arxiv",
"semantic_scholar"
] | Collaborative Speculative Inference for Efficient LLM Inference Serving | Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the target large language model (LLM). This approach enhances the efficiency of inference serving by reducing LLM inference latency and costs while... | [
"Luyao Gao",
"Jianchun Liu",
"Hongli Xu",
"Xichong Zhang",
"Yunming Liao",
"Liusheng Huang"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-13T00:00:00 | https://arxiv.org/abs/2503.10325 | https://arxiv.org/pdf/2503.10325v2 | 2503.10325 | 10.48550/arXiv.2503.10325 | 5 | 0 | false | null | arXiv.org | 0.1945 |
b8f12d91ebe3f0c05c86155a77f27bdcb3ddd320bd9279bcf454f80518e68ff0 | [
"arxiv",
"semantic_scholar"
] | Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding | Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted seque... | [
"Jinze Li",
"Yixing Xu",
"Haiduo Huang",
"Xuanwu Yin",
"Dong Li",
"Edith C. H. Ngai",
"Emad Barsoum"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-13T00:00:00 | https://arxiv.org/abs/2503.10135 | https://arxiv.org/pdf/2503.10135v2 | 2503.10135 | 10.48550/arXiv.2503.10135 | 8 | 1 | true | https://github.com/AMD-AIG-AIMA/Gumiho | International Conference on Machine Learning | 0.2386 |
f5fe03837757d5ebda52b54eb542048f91be0a069e3c40814d7a878c92c61d94 | [
"arxiv",
"semantic_scholar"
] | Training Domain Draft Models for Speculative Decoding: Best Practices and Insights | Speculative decoding is an effective method for accelerating inference of large language models (LLMs) by employing a small draft model to predict the output of a target model. However, when adapting speculative decoding to domain-specific target models, the acceptance rate of the generic draft model drops significantl... | [
"Fenglu Hong",
"Ravi Raju",
"Jonathan Lingjie Li",
"Bo Li",
"Urmish Thakker",
"Avinash Ravichandran",
"Swayambhoo Jain",
"Changran Hu"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-10T00:00:00 | https://arxiv.org/abs/2503.07807 | https://arxiv.org/pdf/2503.07807v2 | 2503.07807 | 10.48550/arXiv.2503.07807 | 7 | 0 | false | null | arXiv.org | 0.2258 |
fce8f76ecb8379a2d747f62f47434378a61e60ac232ab53372dec2a588995a10 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding for Multi-Sample Inference | We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios, such as self-consistency and Best-of-N sampling. Our method exploits the intrinsic consensus of parallel generation paths to synthesize high-quality draft tokens without requiring auxiliary models or external databases. By dyn... | [
"Yiwei Li",
"Jiayi Shi",
"Shaoxiong Feng",
"Peiwen Yuan",
"Xinglin Wang",
"Yueqi Zhang",
"Ji Zhang",
"Chuyi Tan",
"Boyuan Pan",
"Yao Hu",
"Kan Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-07T00:00:00 | https://arxiv.org/abs/2503.05330 | https://arxiv.org/pdf/2503.05330v1 | 2503.05330 | 10.48550/arXiv.2503.05330 | 4 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1747 |
071ec866b67bb04e3d9b888ea0938dd3edafeb2fef3f1a3589583ab5cae1e898 | [
"arxiv",
"semantic_scholar"
] | AdaSpec: Adaptive Speculative Decoding for Fast, SLO-Aware Large Language Model Serving | Cloud-based Large Language Model (LLM) services often face challenges in achieving low inference latency and meeting Service Level Objectives (SLOs) under dynamic request patterns. Speculative decoding, which exploits lightweight models for drafting and LLMs for verification, has emerged as a compelling technique to ac... | [
"Kaiyu Huang",
"Hao Wu",
"Zhubo Shi",
"Han Zou",
"Minchen Yu",
"Qingjiang Shi"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-07T00:00:00 | https://arxiv.org/abs/2503.05096 | https://arxiv.org/pdf/2503.05096v2 | 2503.05096 | 10.1145/3772052.3772239 | 14 | 3 | true | https://github.com/cerebellumking/AdaSpec | ACM Symposium on Cloud Computing | 0.301 |
a35d039489f2b418130459207e92848aa90a3be33beefaf121caf3ab9a3fe7c7 | [
"arxiv",
"semantic_scholar"
] | RASD: Retrieval-Augmented Speculative Decoding | Speculative decoding accelerates inference in large language models (LLMs) by generating draft tokens for target model verification. Current approaches for obtaining draft tokens rely on lightweight draft models or additional model structures to generate draft tokens and retrieve context from databases. Due to the draf... | [
"Guofeng Quan",
"Wenfeng Feng",
"Chuzhan Hao",
"Guochao Jiang",
"Yuewei Zhang",
"Hao Wang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-05T00:00:00 | https://arxiv.org/abs/2503.03434 | https://arxiv.org/pdf/2503.03434v1 | 2503.03434 | 10.48550/arXiv.2503.03434 | 5 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1945 |
f302273702346aa266f44960e65f51a83479eda973110564d5417efef338cd80 | [
"arxiv",
"semantic_scholar"
] | DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting | Large language models (LLMs) exhibit exceptional performance across a wide range of tasks; however, their token-by-token autoregressive generation process significantly hinders inference speed. Speculative decoding presents a promising draft-then-verify framework that reduces generation latency while maintaining output... | [
"Kai Lv",
"Honglin Guo",
"Qipeng Guo",
"Xipeng Qiu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-02T00:00:00 | https://arxiv.org/abs/2503.00784 | https://arxiv.org/pdf/2503.00784v1 | 2503.00784 | 10.48550/arXiv.2503.00784 | 5 | 0 | true | https://github.com/KaiLv69/DuoDecoding | arXiv.org | 0.1945 |
3fb7815a944b7f3265c6c6d00920b13f8266319f0f85a240b72f7e82e192cea9 | [
"arxiv",
"semantic_scholar"
] | Tutorial Proposal: Speculative Decoding for Efficient LLM Inference | This tutorial presents a comprehensive introduction to Speculative Decoding (SD), an advanced technique for LLM inference acceleration that has garnered significant research interest in recent years. SD is introduced as an innovative decoding paradigm to mitigate the high inference latency stemming from autoregressive ... | [
"Heming Xia",
"Cunxiao Du",
"Yongqi Li",
"Qian Liu",
"Wenjie Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-01T00:00:00 | https://arxiv.org/abs/2503.00491 | https://arxiv.org/pdf/2503.00491v1 | 2503.00491 | 10.48550/arXiv.2503.00491 | 4 | 1 | false | null | arXiv.org | 0.1747 |
074ce10412b8df85dd200d973989c39a57492c44232da19b87e15b61a50e8fe6 | [
"arxiv",
"semantic_scholar"
] | Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff | Speculative Decoding (SD) enforces strict distributional equivalence to the target model when accepting candidate tokens. While it maintains the target model's generation quality, this strict equivalence limits the speedup achievable by SD and prevents users from trading deviations from the target distribution in excha... | [
"Maximilian Holsman",
"Yukun Huang",
"Bhuwan Dhingra"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-02-28T00:00:00 | https://arxiv.org/abs/2502.20704 | https://arxiv.org/pdf/2502.20704v4 | 2502.20704 | 10.48550/arXiv.2502.20704 | 7 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2386 |
be152fba1c0d31be3a4b83bf7df5e47c44487f8098c7f93a08c3b806e2a9deca | [
"arxiv",
"semantic_scholar"
] | RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding | The emergence of long-context large language models (LLMs) offers a promising alternative to traditional retrieval-augmented generation (RAG) for processing extensive documents. However, the computational overhead of long-context inference presents significant efficiency challenges. While Speculative Decoding (SD) trad... | [
"Guanzheng Chen",
"Qilong Feng",
"Jinjie Ni",
"Xin Li",
"Michael Qizhe Shieh"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-27T00:00:00 | https://arxiv.org/abs/2502.20330 | https://arxiv.org/pdf/2502.20330v2 | 2502.20330 | 10.48550/arXiv.2502.20330 | 9 | 0 | false | null | International Conference on Machine Learning | 0.25 |
89fa649a8661950248303467c7c21cfb8832cf12741d65607f39dff31a4813f7 | [
"arxiv",
"semantic_scholar"
] | Towards Optimal Multi-draft Speculative Decoding | Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts, and th... | [
"Zhengmian Hu",
"Tong Zheng",
"Vignesh Viswanathan",
"Ziyi Chen",
"Ryan A. Rossi",
"Yihan Wu",
"Dinesh Manocha",
"Heng Huang"
] | [
"cs.CL",
"cs.DS"
] | [
"Computer Science"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.18779 | https://arxiv.org/pdf/2502.18779v1 | 2502.18779 | 10.48550/arXiv.2502.18779 | 15 | 3 | false | null | International Conference on Learning Representations | 0.301 |
952dfb175e181df3a1732eab1cae9d6baa147bc74cbb829f9986464cf48caf89 | [
"arxiv",
"semantic_scholar"
] | Harnessing Multiple Large Language Models: A Survey on LLM Ensemble | LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from their individual strengths -- has gained substantial attention recently. The widespread availability of LLMs, coupled with their varying strengt... | [
"Zhijun Chen",
"Xiaodong Lu",
"Jingzheng Li",
"Pengpeng Chen",
"Zhuoran Li",
"Kai Sun",
"Yuankai Luo",
"Qianren Mao",
"Ming Li",
"Likang Xiao",
"Dingqi Yang",
"Xiao Huang",
"Yikun Ban",
"Hailong Sun",
"Philip S. Yu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2502.18036 | https://arxiv.org/pdf/2502.18036v6 | 2502.18036 | 10.48550/arXiv.2502.18036 | 108 | 4 | true | https://github.com/junchenzhi/Awesome-LLM-Ensemble | arXiv.org | 0.5094 |
24eb4df97f0ffef16ad13e5d36c738bdc02c7baeedda2e94dfc3025d191acc8d | [
"arxiv",
"semantic_scholar"
] | LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification | As Large Language Models (LLMs) can now process extremely long contexts, efficient inference over these extended inputs has become increasingly important, especially for emerging applications like LLM agents that highly depend on this capability. Speculative decoding (SD) offers a promising lossless acceleration techni... | [
"Penghui Yang",
"Cunxiao Du",
"Fengzhuo Zhang",
"Haonan Wang",
"Tianyu Pang",
"Chao Du",
"Bo An"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17421 | https://arxiv.org/pdf/2502.17421v4 | 2502.17421 | null | 11 | 1 | true | https://github.com/sail-sg/LongSpec | null | 0.2698 |
3c6d0366d983fe0aac9a6270707bf3d2f45a5d0a85d4e204b7bc6c7cae3ec2e2 | [
"arxiv",
"semantic_scholar"
] | Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing | Large Language Models (LLMs) have demonstrated human-like instruction-following abilities, particularly those exceeding 100 billion parameters. The combined capability of some smaller, resource-friendly LLMs can address most of the instructions that larger LLMs excel at. In this work, we explore how to route the best-p... | [
"Yi-Kai Zhang",
"De-Chuan Zhan",
"Han-Jia Ye"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17282 | https://arxiv.org/pdf/2502.17282v1 | 2502.17282 | 10.48550/arXiv.2502.17282 | 24 | 3 | true | https://github.com/Now-Join-Us/CIT-LLM-Routing | AAAI Conference on Artificial Intelligence | 0.3495 |
b5c2fbb02569328ef6a10763215a3feb340618baa30203e05d26dcc177fdfa91 | [
"arxiv",
"semantic_scholar"
] | CORAL: Learning Consistent Representations across Multi-step Training with Lighter Speculative Drafter | Speculative decoding is a powerful technique that accelerates Large Language Model (LLM) inference by leveraging a lightweight speculative draft model. However, existing designs suffers in performance due to misalignment between training and inference. Recent methods have tried to solve this issue by adopting a multi-s... | [
"Yepeng Weng",
"Dianwen Mei",
"Huishi Qiu",
"Xujie Chen",
"Li Liu",
"Jiang Tian",
"Zhongchao Shi"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.16880 | https://arxiv.org/pdf/2502.16880v3 | 2502.16880 | 10.48550/arXiv.2502.16880 | 9 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.25 |
a1bd5edafcca4a9bffffa182f00eb7f94731c91888e5fec7648d6a1eff63c632 | [
"arxiv",
"semantic_scholar"
] | TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding | We propose TETRIS, a novel method that optimizes the total throughput of batch speculative decoding in multi-request settings. Unlike existing methods that optimize for a single request or a group of requests as a whole, TETRIS actively selects the most promising draft tokens (for every request in a batch) to be accept... | [
"Zhaoxuan Wu",
"Zijian Zhou",
"Arun Verma",
"Alok Prakash",
"Daniela Rus",
"Bryan Kian Hsiang Low"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-21T00:00:00 | https://arxiv.org/abs/2502.15197 | https://arxiv.org/pdf/2502.15197v2 | 2502.15197 | 10.48550/arXiv.2502.15197 | 7 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2258 |
0d44ff6bbc01fae22dd8f5ed2037e065addd39824e739557cd5d076bf87f62a9 | [
"arxiv",
"semantic_scholar"
] | DReSD: Dense Retrieval for Speculative Decoding | Speculative decoding (SD) accelerates Large Language Model (LLM) generation by using an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. We focus on retrieval-based SD where the draft model retrieves the next tok... | [
"Milan Gritta",
"Huiyin Xue",
"Gerasimos Lampouras"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-21T00:00:00 | https://arxiv.org/abs/2502.15572 | https://arxiv.org/pdf/2502.15572v2 | 2502.15572 | 10.48550/arXiv.2502.15572 | 5 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1945 |
4eab278e79a4757612dc66da38cabda2616e826342f82a6b1e67e316181cac2c | [
"arxiv",
"semantic_scholar"
] | BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM | Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. Although existing work uses LLM to achieve this goal, their method does not use an end-to-end approach and avoids ... | [
"Xiaoyu Chen",
"Changde Du",
"Che Liu",
"Yizhe Wang",
"Huiguang He"
] | [
"cs.HC",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-21T00:00:00 | https://arxiv.org/abs/2502.15172 | https://arxiv.org/pdf/2502.15172v1 | 2502.15172 | 10.1109/ICASSP49660.2025.10890142 | 6 | 0 | true | https://github.com/1994cxy/BP-GPT | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.2113 |
5295c397ed4254f2f9bd8107e17a57f01462fd06dc5a8df7127f2c4df53d4811 | [
"arxiv",
"semantic_scholar"
] | C2T: A Classifier-Based Tree Construction Method in Speculative Decoding | The growing scale of Large Language Models (LLMs) has exacerbated inference latency and computational costs. Speculative decoding methods, which aim to mitigate these issues, often face inefficiencies in the construction of token trees and the verification of candidate tokens. Existing strategies, including chain mode,... | [
"Feiye Huo",
"Jianchao Tan",
"Kefeng Zhang",
"Xunliang Cai",
"Shengli Sun"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-19T00:00:00 | https://arxiv.org/abs/2502.13652 | https://arxiv.org/pdf/2502.13652v1 | 2502.13652 | 10.48550/arXiv.2502.13652 | 7 | 0 | false | null | arXiv.org | 0.2258 |
6e8d4b4315c6cfdde1b0c0d38a09e5adff35cc7c7bf71e46e9b73b1dc7e7b110 | [
"arxiv",
"semantic_scholar"
] | GRIFFIN: Effective Token Alignment for Faster Speculative Decoding | Speculative decoding accelerates inference in large language models (LLMs) by generating multiple draft tokens simultaneously. However, existing methods often struggle with token misalignment between the training and decoding phases, limiting their performance. To address this, we propose GRIFFIN, a novel framework tha... | [
"Shijing Hu",
"Jingyang Li",
"Xingyu Xie",
"Zhihui Lu",
"Kim-Chuan Toh",
"Pan Zhou"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-16T00:00:00 | https://arxiv.org/abs/2502.11018 | https://arxiv.org/pdf/2502.11018v3 | 2502.11018 | 10.48550/arXiv.2502.11018 | 10 | 2 | true | https://github.com/hsj576/GRIFFIN | arXiv.org | 0.2603 |
1f825cf4c9f844e424a1f12d1259bef23b2aeee8014e67ff86bb29117aa1ec6c | [
"arxiv",
"semantic_scholar"
] | CopySpec: Accelerating LLMs with Speculative Copy-and-Paste Without Compromising Quality | We introduce CopySpec, a simple yet effective technique to tackle the inefficiencies LLMs face when generating responses that closely resemble previous outputs or responses that can be verbatim extracted from context. CopySpec identifies repeated sequences in the model's chat history or context and speculates that the ... | [
"Razvan-Gabriel Dumitru",
"Minglai Yang",
"Vikas Yadav",
"Mihai Surdeanu"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-13T00:00:00 | https://arxiv.org/abs/2502.08923 | https://arxiv.org/pdf/2502.08923v2 | 2502.08923 | 10.48550/arXiv.2502.08923 | 5 | 1 | true | https://github.com/RazvanDu/CopySpec | Conference on Empirical Methods in Natural Language Processing | 0.1945 |
d206bd2fdaa2950f31fe3b71cf37e549f4e6405f816fb9733e04ef4e457536f0 | [
"arxiv",
"semantic_scholar"
] | Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding | Large Language Models (LLMs) often excel in specific domains but fall short in others due to the limitations of their training. Thus, enabling LLMs to solve problems collaboratively by integrating their complementary knowledge promises to improve their performance across domains. To realize this potential, we introduce... | [
"Ziyao Wang",
"Muneeza Azmat",
"Ang Li",
"Raya Horesh",
"Mikhail Yurochkin"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-11T00:00:00 | https://arxiv.org/abs/2502.08020 | https://arxiv.org/pdf/2502.08020v2 | 2502.08020 | 10.48550/arXiv.2502.08020 | 8 | 2 | false | null | International Conference on Machine Learning | 0.2386 |
b8317a46b5953116904f8671806482505b9654160e1b47e86ce3348b88a6f007 | [
"arxiv",
"semantic_scholar"
] | Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE | Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where mu... | [
"Haiduo Huang",
"Fuwei Yang",
"Zhenhua Liu",
"Yixing Xu",
"Jinze Li",
"Yang Liu",
"Xuanwu Yin",
"Dong Li",
"Pengju Ren",
"Emad Barsoum"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-10T00:00:00 | https://arxiv.org/abs/2502.06282 | https://arxiv.org/pdf/2502.06282v1 | 2502.06282 | 10.48550/arXiv.2502.06282 | 4 | 0 | true | https://github.com/haiduo/Jakiro | arXiv.org | 0.1747 |
e463e13b91bcd7bae9f0b54e5d4544de938051f27ff8e0ad8bc6675480e8151a | [
"arxiv",
"semantic_scholar"
] | LANTERN++: Enhancing Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models | Speculative decoding has been widely used to accelerate auto-regressive (AR) text generation. However, its effectiveness for visual AR models remains limited due to token selection ambiguity, where multiple tokens share similarly low probabilities and thus reduce acceptance rates. Recently, relaxed speculative decoding... | [
"Sihwan Park",
"Doohyuk Jang",
"Sungyub Kim",
"Souvik Kundu",
"Eunho Yang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-02-10T00:00:00 | https://arxiv.org/abs/2502.06352 | https://arxiv.org/pdf/2502.06352v2 | 2502.06352 | 10.48550/arXiv.2502.06352 | 11 | 3 | true | https://github.com/jadohu/LANTERN | arXiv.org | 0.301 |
368cc9d45aaa33fd610dceed97284283c25b3e056fbe0b321f57087891d5b94d | [
"arxiv",
"semantic_scholar"
] | Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention | Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention... | [
"Zhendong Zhang"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-09T00:00:00 | https://arxiv.org/abs/2502.05947 | https://arxiv.org/pdf/2502.05947v1 | 2502.05947 | 10.48550/arXiv.2502.05947 | 0 | 0 | false | null | arXiv.org | 0.0516 |
ae671a898c0c36ef5f1d48b51eb9b634636be5417f5db2525bb4f1635257ca2a | [
"arxiv",
"semantic_scholar"
] | Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding | Accelerating inference in Large Language Models (LLMs) is critical for real-time interactions, as they have been widely incorporated into real-world services. Speculative decoding, a fully algorithmic solution, has gained attention for improving inference speed by drafting and verifying tokens, thereby generating multi... | [
"Sukmin Cho",
"Sangjin Choi",
"Taeho Hwang",
"Jeongyeon Seo",
"Soyeong Jeong",
"Huije Lee",
"Hoyun Song",
"Jong C. Park",
"Youngjin Kwon"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-08T00:00:00 | https://arxiv.org/abs/2502.05609 | https://arxiv.org/pdf/2502.05609v1 | 2502.05609 | 10.48550/arXiv.2502.05609 | 5 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.1945 |
c5e41008fc1f7af6788ed83e5d935319495ac0bb0073831f53148782bbb3f444 | [
"arxiv",
"semantic_scholar"
] | Speeding up Speculative Decoding via Sequential Approximate Verification | Speculative Decoding (SD) is a recently proposed technique for faster inference using Large Language Models (LLMs). SD operates by using a smaller draft LLM for autoregressively generating a sequence of tokens and a larger target LLM for parallel verification to ensure statistical consistency. However, periodic paralle... | [
"Meiyu Zhong",
"Noel Teku",
"Ravi Tandon"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-06T00:00:00 | https://arxiv.org/abs/2502.04557 | https://arxiv.org/pdf/2502.04557v3 | 2502.04557 | null | 10 | 0 | false | null | null | 0.2603 |
34945b27884ef4ef7fd735328da26b2598beb14bc81ec907b7f7ee3bc2eb7e15 | [
"arxiv",
"semantic_scholar"
] | QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache | Large Language Models (LLMs) are increasingly being deployed on edge devices for long-context settings, creating a growing need for fast and efficient long-context inference. In these scenarios, the Key-Value (KV) cache is the primary bottleneck in terms of both GPU memory and latency, as the full KV cache must be load... | [
"Rishabh Tiwari",
"Haocheng Xi",
"Aditya Tomar",
"Coleman Hooper",
"Sehoon Kim",
"Maxwell Horton",
"Mahyar Najibi",
"Michael W. Mahoney",
"Kurt Keutzer",
"Amir Gholami"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-05T00:00:00 | https://arxiv.org/abs/2502.10424 | https://arxiv.org/pdf/2502.10424v1 | 2502.10424 | 10.48550/arXiv.2502.10424 | 18 | 2 | false | null | International Conference on Machine Learning | 0.3197 |
cdf165dd9931532c8527c1cab3f803fef55d8156e8835fd9dd97ead7701960fc | [
"arxiv",
"semantic_scholar"
] | EasySpec: Layer-Parallel Speculative Decoding for Efficient Multi-GPU Utilization | Speculative decoding is an effective and lossless method for Large Language Model (LLM) inference acceleration. It employs a smaller model to generate a draft token sequence, which is then verified by the original base model. In multi-GPU systems, inference latency can be further reduced through tensor parallelism (TP)... | [
"Yize Wu",
"Ke Gao",
"Ling Li",
"Yanjun Wu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-02-04T00:00:00 | https://arxiv.org/abs/2502.02493 | https://arxiv.org/pdf/2502.02493v2 | 2502.02493 | 10.48550/arXiv.2502.02493 | 1 | 0 | true | https://github.com/Yize-Wu/EasySpec | arXiv.org | 0.0753 |
bce356f4f90edfc82727db7e21d64032fbfdc3d7dc82ee7c19b2e7d27ebdf109 | [
"arxiv",
"semantic_scholar"
] | Fast Large Language Model Collaborative Decoding via Speculation | Large Language Model (LLM) collaborative decoding techniques improve output quality by combining the outputs of multiple models at each generation step, but they incur high computational costs. In this paper, we introduce Collaborative decoding via Speculation (CoS), a novel framework that accelerates collaborative dec... | [
"Jiale Fu",
"Yuchu Jiang",
"Junkai Chen",
"Jiaming Fan",
"Xin Geng",
"Xu Yang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-01T00:00:00 | https://arxiv.org/abs/2502.01662 | https://arxiv.org/pdf/2502.01662v2 | 2502.01662 | null | 10 | 0 | true | https://github.com/Kamichanw/CoS/ | International Conference on Machine Learning | 0.2603 |
fd660812c5e37daa5a00032cac9b1e01b6689591a9ba340f221157a890b61d41 | [
"arxiv",
"semantic_scholar"
] | Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies | Accelerating the inference of large language models (LLMs) is a critical challenge in generative AI. Speculative decoding (SD) methods offer substantial efficiency gains by generating multiple tokens using a single target forward pass. However, existing SD approaches require the drafter and target models to share the s... | [
"Nadav Timor",
"Jonathan Mamou",
"Daniel Korat",
"Moshe Berchansky",
"Gaurav Jain",
"Oren Pereg",
"Moshe Wasserblat",
"David Harel"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-01-31T00:00:00 | https://arxiv.org/abs/2502.05202 | https://arxiv.org/pdf/2502.05202v3 | 2502.05202 | 10.48550/arXiv.2502.05202 | 19 | 3 | false | null | International Conference on Machine Learning | 0.3253 |
08d779b15d93e309f2d6cc1c82db177f07921093606b6c76aec4e15081bf2309 | [
"arxiv",
"semantic_scholar"
] | Reward-Guided Speculative Decoding for Efficient LLM Reasoning | We introduce Reward-Guided Speculative Decoding (RSD), a novel framework aimed at improving the efficiency of inference in large language models (LLMs). RSD synergistically combines a lightweight draft model with a more powerful target model, incorporating a controlled bias to prioritize high-reward outputs, in contras... | [
"Baohao Liao",
"Yuhui Xu",
"Hanze Dong",
"Junnan Li",
"Christof Monz",
"Silvio Savarese",
"Doyen Sahoo",
"Caiming Xiong"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-01-31T00:00:00 | https://arxiv.org/abs/2501.19324 | https://arxiv.org/pdf/2501.19324v3 | 2501.19324 | 10.48550/arXiv.2501.19324 | 104 | 11 | true | https://github.com/BaohaoLiao/RSD | International Conference on Machine Learning | 0.5396 |
16026c717e4c81cffc61c1aa5397b09f339e633bf13be978b11ea7361c0c5935 | [
"arxiv",
"semantic_scholar"
] | Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment | The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are the... | [
"Gregor Bachmann",
"Sotiris Anagnostidis",
"Albert Pumarola",
"Markos Georgopoulos",
"Artsiom Sanakoyeu",
"Yuming Du",
"Edgar Schönfeld",
"Ali Thabet",
"Jonas Kohler"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-01-31T00:00:00 | https://arxiv.org/abs/2501.19309 | https://arxiv.org/pdf/2501.19309v1 | 2501.19309 | 10.48550/arXiv.2501.19309 | 53 | 11 | false | null | International Conference on Learning Representations | 0.5396 |
e9771fbdedaec9c4b0c9315a5c9380800e1deb8888e3c395a65ff585128aaae0 | [
"arxiv",
"semantic_scholar"
] | Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding | Guesstimation -- the task of making approximate quantitative estimates about objects or events -- is a common real-world skill, yet remains underexplored in large language model (LLM) research. We introduce three guesstimation datasets: MARBLES, FUTURE, and ELECPRED, spanning physical estimation (e.g., how many marbles... | [
"Yun-Shiuan Chuang",
"Sameer Narendran",
"Nikunj Harlalka",
"Alexander Cheung",
"Sizhe Gao",
"Siddharth Suresh",
"Junjie Hu",
"Timothy T. Rogers"
] | [
"cs.AI",
"cs.HC"
] | [
"Computer Science"
] | 2025-01-28T00:00:00 | https://arxiv.org/abs/2501.17310 | https://arxiv.org/pdf/2501.17310v4 | 2501.17310 | 10.48550/arXiv.2501.17310 | 1 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.0753 |
1a68a08a344317440f59905cacc3416d121fa2e1f55fdef9124ee2e249ec03a4 | [
"arxiv",
"semantic_scholar"
] | AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding | Modern large language model (LLM) applications exhibit diverse service-level objectives (SLOs), from low-latency requirements in interactive coding assistants to more relaxed constraints in data wrangling tasks. Existing LLM serving systems, which rely on uniform batching and scheduling strategies, often fail to meet t... | [
"Zikun Li",
"Zhuofu Chen",
"Remi Delacourt",
"Gabriele Oliaro",
"Zeyu Wang",
"Qinghan Chen",
"Shuhuai Lin",
"April Yang",
"Zhihao Zhang",
"Zhuoming Chen",
"Sean Lai",
"Xinhao Cheng",
"Xupeng Miao",
"Zhihao Jia"
] | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-01-21T00:00:00 | https://arxiv.org/abs/2501.12162 | https://arxiv.org/pdf/2501.12162v2 | 2501.12162 | null | 0 | 0 | false | null | null | 0.019 |
7d8ad91c4e6b98ddaf46fdc740b4a11992dbcd603685e2efb9a6f5287bc35116 | [
"arxiv",
"semantic_scholar"
] | HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models | Large Language Models (LLMs) have revolutionized natural language processing by understanding and generating human-like text. However, the increasing demand for more sophisticated LLMs presents significant computational challenges due to their scale and complexity. This paper introduces Hardware Accelerated Decoding (H... | [
"Ze Yang",
"Yihong Jin",
"Xinhe Xu"
] | [
"cs.CL",
"cs.AI",
"cs.AR"
] | [
"Computer Science"
] | 2024-12-27T00:00:00 | https://arxiv.org/abs/2412.19925 | https://arxiv.org/pdf/2412.19925v2 | 2412.19925 | 10.1109/ICCEA65460.2025.11103323 | 17 | 0 | false | null | International Conference Civil Engineering and Architecture | 0.3138 |
ec419d860fedf52cfc59d1ace891358321c8f606121f74a22921619e54d752b8 | [
"arxiv",
"semantic_scholar"
] | Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference | With the continuous advancement in the performance of large language models (LLMs), their demand for computational resources and memory has significantly increased, which poses major challenges for efficient inference on consumer-grade devices and legacy servers. These devices typically feature relatively weaker GPUs a... | [
"Libo Zhang",
"Zhaoning Zhang",
"Baizhou Xu",
"Rui Li",
"Zhiliang Tian",
"Songzhu Mei",
"Dongsheng Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-12-25T00:00:00 | https://arxiv.org/abs/2412.18934 | https://arxiv.org/pdf/2412.18934v2 | 2412.18934 | 10.48550/arXiv.2412.18934 | 18 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3197 |
e41a6cc89acea55efca9236afb2a0e711941c20257be26f3917dd1699486306b | [
"arxiv",
"semantic_scholar"
] | AdaEAGLE: Optimizing Speculative Decoding via Explicit Modeling of Adaptive Draft Structures | Speculative Decoding (SD) is a popular lossless technique for accelerating the inference of Large Language Models (LLMs). We show that the decoding speed of SD frameworks with static draft structures can be significantly improved by incorporating context-aware adaptive draft structures. However, current studies on adap... | [
"Situo Zhang",
"Hankun Wang",
"Da Ma",
"Zichen Zhu",
"Lu Chen",
"Kunyao Lan",
"Kai Yu"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-12-25T00:00:00 | https://arxiv.org/abs/2412.18910 | https://arxiv.org/pdf/2412.18910v1 | 2412.18910 | 10.48550/arXiv.2412.18910 | 11 | 0 | false | null | arXiv.org | 0.2698 |
3f51517c30d349c5df2d4080244480584389de5be71577e8b31691a88a807915 | [
"arxiv",
"semantic_scholar"
] | Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree | Striking an optimal balance between minimal drafting latency and high speculation accuracy to enhance the inference speed of Large Language Models remains a significant challenge in speculative decoding. In this paper, we introduce Falcon, an innovative semi-autoregressive speculative decoding framework fashioned to au... | [
"Xiangxiang Gao",
"Weisheng Xie",
"Yiwei Xiang",
"Feng Ji"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-17T00:00:00 | https://arxiv.org/abs/2412.12639 | https://arxiv.org/pdf/2412.12639v3 | 2412.12639 | 10.48550/arXiv.2412.12639 | 24 | 3 | false | null | AAAI Conference on Artificial Intelligence | 0.3495 |
868ba97a8771ccf32395279cee99ea19ddc23f32f04ccec37bdeec3db00b72f5 | [
"arxiv",
"semantic_scholar"
] | Constrained Decoding with Speculative Lookaheads | Constrained decoding with lookahead heuristics (CDLH) is a highly effective method for aligning LLM generations to human preferences. However, the extensive lookahead roll-out operations for each generated token makes CDLH prohibitively expensive, resulting in low adoption in practice. In contrast, common decoding stra... | [
"Nishanth Nakshatri",
"Shamik Roy",
"Rajarshi Das",
"Suthee Chaidaroon",
"Leonid Boytsov",
"Rashmi Gangadharaiah"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.10418 | https://arxiv.org/pdf/2412.10418v2 | 2412.10418 | 10.48550/arXiv.2412.10418 | 8 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.2386 |
099276887181b59b379f2937f19e48d648120e383f9ba00fd89f52aeb4b1194c | [
"arxiv",
"semantic_scholar"
] | PLD+: Accelerating LLM inference by leveraging Language Model Artifacts | To reduce the latency associated with autoretrogressive LLM inference, speculative decoding has emerged as a novel decoding paradigm, where future tokens are drafted and verified in parallel. However, the practical deployment of speculative decoding is hindered by its requirements for additional computational resources... | [
"Shwetha Somasundaram",
"Anirudh Phukan",
"Apoorv Saxena"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-02T00:00:00 | https://arxiv.org/abs/2412.01447 | https://arxiv.org/pdf/2412.01447v1 | 2412.01447 | 10.48550/arXiv.2412.01447 | 13 | 2 | false | null | North American Chapter of the Association for Computational Linguistics | 0.2865 |
e39f7f27fca82d58bcd40d064439cde5481be1150c513714f9a42a8781beff56 | [
"arxiv",
"semantic_scholar"
] | Draft Model Knows When to Stop: Self-Verification Speculative Decoding for Long-Form Generation | Conventional speculative decoding (SD) methods utilize a predefined length policy for proposing drafts, which implies the premise that the target model smoothly accepts the proposed draft tokens. However, reality deviates from this assumption: the oracle draft length varies significantly, and the fixed-length policy ha... | [
"Ziyin Zhang",
"Jiahao Xu",
"Tian Liang",
"Xingyu Chen",
"Zhiwei He",
"Rui Wang",
"Zhaopeng Tu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-27T00:00:00 | https://arxiv.org/abs/2411.18462 | https://arxiv.org/pdf/2411.18462v2 | 2411.18462 | 10.18653/v1/2025.emnlp-main.844 | 9 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.25 |
4e5018ee695bce5c2c8fb3d32c5d822a16c798090e593c4817e3f0888b6f0751 | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration | Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base... | [
"Zhuofan Wen",
"Shangtong Gui",
"Yang Feng"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-11-25T00:00:00 | https://arxiv.org/abs/2412.00061 | https://arxiv.org/pdf/2412.00061v1 | 2412.00061 | 10.48550/arXiv.2412.00061 | 17 | 1 | false | null | Neural Information Processing Systems | 0.3138 |
84cb1691e5cd343e4452297706935345d82e1de5146b800f02374f6f5ba11ebf | [
"arxiv",
"semantic_scholar"
] | A Survey on LLM-as-a-Judge | Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs... | [
"Jiawei Gu",
"Xuhui Jiang",
"Zhichao Shi",
"Hexiang Tan",
"Xuehao Zhai",
"Chengjin Xu",
"Wei Li",
"Yinghan Shen",
"Shengjie Ma",
"Honghao Liu",
"Saizhuo Wang",
"Kun Zhang",
"Yuanzhuo Wang",
"Wen Gao",
"Lionel Ni",
"Jian Guo"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-23T00:00:00 | https://arxiv.org/abs/2411.15594 | https://arxiv.org/pdf/2411.15594v6 | 2411.15594 | 10.48550/arXiv.2411.15594 | 1,447 | 120 | false | null | arXiv.org | 1 |
9a4115a62b6d3a37d0ae39dde6a6c75a7d15ca2c2e69f9a891dcbe0ee5e28770 | [
"arxiv",
"semantic_scholar"
] | Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding | Efficient inference in large language models (LLMs) has become a critical focus as their scale and complexity grow. Traditional autoregressive decoding, while effective, suffers from computational inefficiencies due to its sequential token generation process. Speculative decoding addresses this bottleneck by introducin... | [
"Hyun Ryu",
"Eric Kim"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-20T00:00:00 | https://arxiv.org/abs/2411.13157 | https://arxiv.org/pdf/2411.13157v2 | 2411.13157 | 10.48550/arXiv.2411.13157 | 7 | 0 | false | null | arXiv.org | 0.2258 |
03004d57fd097f502c45b6259e86a65a231638b602dfc21282405a7422117ec0 | [
"arxiv",
"semantic_scholar"
] | Continuous Speculative Decoding for Autoregressive Image Generation | Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation. However, the heavy autoregressive inference burden imposes significant overhead. In Large Language Models (LLMs), speculative decoding has effectively accelerated discrete autoregressive inference. However, the abs... | [
"Zili Wang",
"Robert Zhang",
"Kun Ding",
"Qi Yang",
"Fei Li",
"Shiming Xiang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11925 | https://arxiv.org/pdf/2411.11925v2 | 2411.11925 | 10.48550/arXiv.2411.11925 | 17 | 0 | true | https://github.com/MarkXCloud/CSpD | arXiv.org | 0.3138 |
03fd5745fca7988259118fd262f85a6300cdd2469df9e9929383c9de76df5fdf | [
"arxiv",
"semantic_scholar"
] | SAM Decoding: Speculative Decoding via Suffix Automaton | Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have yielded promising speedup, but they often rely on incomplete retrieval resources, inefficient retrieval methods, and are constrained to certa... | [
"Yuxuan Hu",
"Ke Wang",
"Xiaokang Zhang",
"Fanjin Zhang",
"Cuiping Li",
"Hong Chen",
"Jing Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-16T00:00:00 | https://arxiv.org/abs/2411.10666 | https://arxiv.org/pdf/2411.10666v3 | 2411.10666 | 10.48550/arXiv.2411.10666 | 24 | 4 | true | https://github.com/hyx1999/SAM-Decoding}{repository} | Annual Meeting of the Association for Computational Linguistics | 0.3495 |
cce3697d6a635c2423d92c52ce5bb28bae823675a2dd2919cc409f2203cee5b4 | [
"arxiv",
"semantic_scholar"
] | SSSD: Simply-Scalable Speculative Decoding | Speculative Decoding has emerged as a popular technique for accelerating inference in Large Language Models. However, most existing approaches yield only modest improvements in production serving systems. Methods that achieve substantial speedups typically rely on an additional trained draft model or auxiliary model co... | [
"Michele Marzollo",
"Jiawei Zhuang",
"Niklas Roemer",
"Niklas Zwingenberger",
"Lorenz K. Müller",
"Lukas Cavigelli"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-08T00:00:00 | https://arxiv.org/abs/2411.05894 | https://arxiv.org/pdf/2411.05894v3 | 2411.05894 | 10.48550/arXiv.2411.05894 | 2 | 0 | false | null | arXiv.org | 0.1193 |
3181ff1c3c23fbd3c9cef004c0c3999c575448479bd8b3dad0e4e1dc4601ef63 | [
"arxiv",
"semantic_scholar"
] | SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding | Large Language Models (LLMs) have become essential in advancing natural language processing (NLP) tasks, but their sequential token generation limits inference speed. Multi-Draft Speculative Decoding (MDSD) offers a promising solution by using a smaller draft model to generate multiple token sequences, which the target... | [
"Ryan Sun",
"Tianyi Zhou",
"Xun Chen",
"Lichao Sun"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-08T00:00:00 | https://arxiv.org/abs/2411.05289 | https://arxiv.org/pdf/2411.05289v1 | 2411.05289 | 10.18653/v1/2024.emnlp-main.1148 | 10 | 2 | true | https://github.com/MasterGodzilla/Speculative_decoding_OT} | Conference on Empirical Methods in Natural Language Processing | 0.2603 |
a8369e2dda58650b4b7c97d55e1a9edee70952783152b14d1ddca3ab717599a6 | [
"arxiv",
"semantic_scholar"
] | SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications | Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However, emerging AI applications, such as LLM-based agents, present unique workload characteristics: instead of diverse independent requests, agent... | [
"Gabriele Oliaro",
"Zhihao Jia",
"Daniel Campos",
"Aurick Qiao"
] | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-07T00:00:00 | https://arxiv.org/abs/2411.04975 | https://arxiv.org/pdf/2411.04975v3 | 2411.04975 | null | 17 | 3 | true | https://github.com/snowflakedb/ArcticInference | null | 0.3138 |
5dcabae329dec8df1448551fa0449aee3a85fbe2a7d0b8052411e16e36ee3dd5 | [
"arxiv",
"semantic_scholar"
] | When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMs | Deployed large language models (LLMs) often rely on speculative decoding, a technique that generates and verifies multiple candidate tokens in parallel, to improve throughput and latency. In this work, we reveal a new side-channel whereby input-dependent patterns of correct and incorrect speculations can be inferred by... | [
"Jiankun Wei",
"Abdulrahman Abdulrazzag",
"Tianchen Zhang",
"Adel Muursepp",
"Gururaj Saileshwar"
] | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-01T00:00:00 | https://arxiv.org/abs/2411.01076 | https://arxiv.org/pdf/2411.01076v4 | 2411.01076 | null | 4 | 0 | false | null | null | 0.1747 |
c832770bf60a9e05bc3e1036528826a28be5210da647c61f8c42de8218fd3ae0 | [
"arxiv",
"semantic_scholar"
] | A Theoretical Perspective for Speculative Decoding Algorithm | Transformer-based autoregressive sampling has been the major bottleneck for slowing down large language model inferences. One effective way to accelerate inference is \emph{Speculative Decoding}, which employs a small model to sample a sequence of draft tokens and a large model to validate. Given its empirical effectiv... | [
"Ming Yin",
"Minshuo Chen",
"Kaixuan Huang",
"Mengdi Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2411.00841 | https://arxiv.org/pdf/2411.00841v1 | 2411.00841 | 10.48550/arXiv.2411.00841 | 27 | 3 | false | null | Neural Information Processing Systems | 0.3618 |
2efc7e9d844e87ea5ca0bfda6514a36b1445a67848e8900e0a7a573946edbfe1 | [
"arxiv",
"semantic_scholar"
] | Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding | The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenges in the next-token prediction posed by lengthy sequences of speech tokens. In this work, we introduce VADUSA, one of the first approaches t... | [
"Bohan Li",
"Hankun Wang",
"Situo Zhang",
"Yiwei Guo",
"Kai Yu"
] | [
"eess.AS",
"cs.AI",
"cs.SD"
] | [
"Engineering",
"Computer Science"
] | 2024-10-29T00:00:00 | https://arxiv.org/abs/2410.21951 | https://arxiv.org/pdf/2410.21951v2 | 2410.21951 | 10.1109/ICASSP49660.2025.10888194 | 22 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3404 |
6b9c34c19b56104c44ca32d9f768aa298195c84c7434e25ba1aaafec7c180d70 | [
"arxiv",
"semantic_scholar"
] | Fast Best-of-N Decoding via Speculative Rejection | The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during... | [
"Hanshi Sun",
"Momin Haider",
"Ruiqi Zhang",
"Huitao Yang",
"Jiahao Qiu",
"Ming Yin",
"Mengdi Wang",
"Peter Bartlett",
"Andrea Zanette"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-26T00:00:00 | https://arxiv.org/abs/2410.20290 | https://arxiv.org/pdf/2410.20290v2 | 2410.20290 | 10.48550/arXiv.2410.20290 | 131 | 15 | false | null | Neural Information Processing Systems | 0.6021 |
61a1b36da45fdb72b3ad3ee3ec0fa977dff1a17f5e592b7a532c366666f7c642 | [
"arxiv",
"semantic_scholar"
] | AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability | Speculative decoding is a powerful technique that attempts to circumvent the autoregressive constraint of modern Large Language Models (LLMs). The aim of speculative decoding techniques is to improve the average inference time of a large, target model without sacrificing its accuracy, by using a more efficient draft mo... | [
"Sudhanshu Agrawal",
"Wonseok Jeon",
"Mingu Lee"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-24T00:00:00 | https://arxiv.org/abs/2410.18351 | https://arxiv.org/pdf/2410.18351v1 | 2410.18351 | 10.48550/arXiv.2410.18351 | 17 | 2 | false | null | null | 0.3138 |
22c84d0293840fef668552d3801312ef6db48915c69e8e77f0c7fa9fb0d1dbd9 | [
"arxiv",
"semantic_scholar"
] | POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference | Each request in LLM inference goes through two phases: compute-bound prefill and memory-bandwidth-bound decode. To improve GPU utilization, recent systems use hybrid batching that combines the prefill and decode phases of different requests into the same batch. This approach optimizes linear operations but remains inef... | [
"Aditya K Kamath",
"Ramya Prabhu",
"Jayashree Mohan",
"Simon Peter",
"Ramachandran Ramjee",
"Ashish Panwar"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2024-10-23T00:00:00 | https://arxiv.org/abs/2410.18038 | https://arxiv.org/pdf/2410.18038v2 | 2410.18038 | 10.1145/3676641.3715996 | 76 | 7 | false | null | International Conference on Architectural Support for Programming Languages and Operating Systems | 0.4716 |
1c093c880586c630d3eec1fa16739838534ec398c013a14f92c03fdadfeebb17 | [
"arxiv",
"semantic_scholar"
] | AMUSD: Asynchronous Multi-Device Speculative Decoding for LLM Acceleration | Large language models typically generate tokens autoregressively, using each token as input for the next. Recent work on Speculative Decoding has sought to accelerate this process by employing a smaller, faster draft model to more quickly generate candidate tokens. These candidates are then verified in parallel by the ... | [
"Bradley McDanel"
] | [
"cs.CL",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-22T00:00:00 | https://arxiv.org/abs/2410.17375 | https://arxiv.org/pdf/2410.17375v1 | 2410.17375 | 10.1109/ISCAS56072.2025.11043575 | 13 | 3 | true | https://github.com/BradMcDanel/AMUSD/ | International Symposium on Circuits and Systems | 0.301 |
91445b29ab819d595956f9e9fb5a20fda991cb16c9770b1296bdfee695d6a795 | [
"arxiv",
"semantic_scholar"
] | Progressive Mixed-Precision Decoding for Efficient LLM Inference | In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effective solution by storing weights in reduced precision. However, utilizin... | [
"Hao Mark Chen",
"Fuwen Tan",
"Alexandros Kouris",
"Royson Lee",
"Hongxiang Fan",
"Stylianos I. Venieris"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.13461 | https://arxiv.org/pdf/2410.13461v2 | 2410.13461 | 10.48550/arXiv.2410.13461 | 14 | 2 | false | null | International Conference on Learning Representations | 0.294 |
f02aa6149c1fda7bee6cf7225cbe8e955277d0d744ec66648ed5b88853f8fd1c | [
"arxiv",
"semantic_scholar"
] | Accelerating Codec-based Speech Synthesis with Multi-Token Prediction and Speculative Decoding | The goal of this paper is to accelerate codec-based speech synthesis systems with minimum sacrifice to speech quality. We propose an enhanced inference method that allows for flexible trade-offs between speed and quality during inference without requiring additional training. Our core idea is to predict multiple tokens... | [
"Tan Dat Nguyen",
"Ji-Hoon Kim",
"Jeongsoo Choi",
"Shukjae Choi",
"Jinseok Park",
"Younglo Lee",
"Joon Son Chung"
] | [
"cs.SD",
"cs.AI",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.13839 | https://arxiv.org/pdf/2410.13839v1 | 2410.13839 | 10.1109/ICASSP49660.2025.10887855 | 14 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.294 |
17965272fb0d48fc110b7c1084ccad857bacd8754c3f53f85e5a9f4c56bed98b | [
"arxiv",
"semantic_scholar"
] | Cerberus: Efficient Inference with Adaptive Parallel Decoding and Sequential Knowledge Enhancement | Large language models (LLMs) often face a bottleneck in inference speed due to their reliance on auto-regressive decoding. Recently, parallel decoding has shown significant promise in enhancing inference efficiency. However, we have identified two key issues with existing parallel decoding frameworks: (1) decoding head... | [
"Yuxuan Liu",
"Wenyuan Li",
"Laizhong Cui",
"Hailiang Yang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.13344 | https://arxiv.org/pdf/2410.13344v1 | 2410.13344 | 10.48550/arXiv.2410.13344 | 1 | 0 | false | null | arXiv.org | 0.0753 |
ab684e7d67d4a3784a2dbf32fedc845f9ad7e01e8df98fd80be66a3c208ee77b | [
"arxiv",
"semantic_scholar"
] | QSpec: Speculative Decoding with Complementary Quantization Schemes | Quantization is widely adopted to accelerate inference and reduce memory consumption in large language models (LLMs). While activation-weight joint quantization enables efficient low-precision decoding, it suffers from substantial performance degradation on multi-step reasoning tasks. We propose QSpec, a novel quantiza... | [
"Juntao Zhao",
"Wenhao Lu",
"Sheng Wang",
"Lingpeng Kong",
"Chuan Wu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.11305 | https://arxiv.org/pdf/2410.11305v3 | 2410.11305 | 10.48550/arXiv.2410.11305 | 14 | 3 | true | https://github.com/hku-netexplo-lab/QSpec | Conference on Empirical Methods in Natural Language Processing | 0.301 |
a93b5f3e1e4bd6a3848a063174fa5d114516887d7df869f91610601233788675 | [
"arxiv",
"semantic_scholar"
] | DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure | While speculative decoding has recently appeared as a promising direction for accelerating the inference of large language models (LLMs), the speedup and scalability are strongly bounded by the token acceptance rate. Prevalent methods usually organize predicted tokens as independent chains or fixed token trees, which f... | [
"Yunfan Xiong",
"Ruoyu Zhang",
"Yanzeng Li",
"Tianhao Wu",
"Lei Zou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.11744 | https://arxiv.org/pdf/2410.11744v1 | 2410.11744 | 10.1007/s11280-025-01344-0 | 19 | 2 | false | null | World wide web (Bussum) | 0.3253 |
4b5c1b6f8c51771d1edff1a9435c1d958151e5c62a17aebbdc0e586f96dda66c | [
"arxiv",
"semantic_scholar"
] | Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation | Speculative decoding stands as a pivotal technique to expedite inference in autoregressive (large) language models. This method employs a smaller draft model to speculate a block of tokens, which the target model then evaluates for acceptance. Despite a wealth of studies aimed at increasing the efficiency of speculativ... | [
"Siru Ouyang",
"Shuohang Wang",
"Minhao Jiang",
"Ming Zhong",
"Donghan Yu",
"Jiawei Han",
"Yelong Shen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10141 | https://arxiv.org/pdf/2410.10141v1 | 2410.10141 | 10.48550/arXiv.2410.10141 | 8 | 1 | true | https://github.com/ozyyshr/TempSpec | Conference on Empirical Methods in Natural Language Processing | 0.2386 |
54100a3ee502ecb7f548f7cc5df9ea300ed1e391e306f486483f7be776f8e0fa | [
"arxiv",
"semantic_scholar"
] | Local and Global Decoding in Text Generation | Text generation, a key component in applications such as dialogue systems, relies on decoding algorithms that sample strings from a language model distribution. Traditional methods, such as top-$k$ and top-$π$, apply local normalisation to the model's output distribution, which can distort it. In this paper, we investi... | [
"Daniel Gareev",
"Thomas Hofmann",
"Ezhilmathi Krishnasamy",
"Tiago Pimentel"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10810 | https://arxiv.org/pdf/2410.10810v1 | 2410.10810 | 10.48550/arXiv.2410.10810 | 3 | 1 | true | https://github.com/lowlypalace/global-decoding | Conference on Empirical Methods in Natural Language Processing | 0.1505 |
aeefa6380c28437d1b69e53bc3c92f98d3d5f6ea0e7a3be725a2448026dfd541 | [
"arxiv",
"semantic_scholar"
] | Nudging: Inference-time Alignment of LLMs via Guided Decoding | Large language models (LLMs) require alignment to effectively and safely follow user instructions. This process necessitates training an aligned version for every base model, resulting in significant computational overhead. In this work, we propose NUDGING, a simple, training-free algorithm that aligns any base model a... | [
"Yu Fei",
"Yasaman Razeghi",
"Sameer Singh"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-11T00:00:00 | https://arxiv.org/abs/2410.09300 | https://arxiv.org/pdf/2410.09300v4 | 2410.09300 | 10.18653/v1/2025.acl-long.623 | 23 | 7 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.4515 |
faf30e0935df27e700a08ef92f72991789e531a3d14b7b2b7135627f520a9ae8 | [
"arxiv",
"semantic_scholar"
] | SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration | Speculative decoding (SD) has emerged as a widely used paradigm to accelerate LLM inference without compromising quality. It works by first employing a compact model to draft multiple tokens efficiently and then using the target LLM to verify them in parallel. While this technique has achieved notable speedups, most ex... | [
"Heming Xia",
"Yongqi Li",
"Jun Zhang",
"Cunxiao Du",
"Wenjie Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.06916 | https://arxiv.org/pdf/2410.06916v2 | 2410.06916 | 10.48550/arXiv.2410.06916 | 63 | 7 | true | https://github.com/hemingkx/SWIFT | International Conference on Learning Representations | 0.4515 |
5d2f3d3194aba121f17d6303b217f950bea95cee26872108081b1f39ffc1255d | [
"arxiv",
"semantic_scholar"
] | Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level | Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malicious instruction prompts. While current methods effectively address jailbreak risks, they share common limitations: 1) Judging harmful respon... | [
"Xinyi Zeng",
"Yuying Shang",
"Jiawei Chen",
"Jingyuan Zhang",
"Yu Tian"
] | [
"cs.CL",
"cs.CR"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.06809 | https://arxiv.org/pdf/2410.06809v3 | 2410.06809 | 10.48550/arXiv.2410.06809 | 13 | 0 | false | null | arXiv.org | 0.2865 |
5323256de97396efd50cccfab12e97b271dc6ecb8e46fc91ff97c4441decc897 | [
"arxiv",
"semantic_scholar"
] | ParallelSpec: Parallel Drafter for Efficient Speculative Decoding | Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target model is leveraged to verify them in parallel. However, most existing works still draft tokens auto-regressively to maintain sequential depend... | [
"Zilin Xiao",
"Hongming Zhang",
"Tao Ge",
"Siru Ouyang",
"Vicente Ordonez",
"Dong Yu"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-08T00:00:00 | https://arxiv.org/abs/2410.05589 | https://arxiv.org/pdf/2410.05589v1 | 2410.05589 | 10.48550/arXiv.2410.05589 | 21 | 2 | false | null | arXiv.org | 0.3356 |
0e153fc8151a5930e39e2f20ea16a4e9e93dcc12cd84fb140d4edcb9a0228ad2 | [
"arxiv",
"semantic_scholar"
] | PAD: Personalized Alignment of LLMs at Decoding-Time | Aligning with personalized preferences, which vary significantly across cultural, educational, and political differences, poses a significant challenge due to the computational costs and data demands of traditional alignment methods. In response, this paper presents Personalized Alignment at Decoding-time (PAD), a nove... | [
"Ruizhe Chen",
"Xiaotian Zhang",
"Meng Luo",
"Wenhao Chai",
"Zuozhu Liu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-05T00:00:00 | https://arxiv.org/abs/2410.04070 | https://arxiv.org/pdf/2410.04070v7 | 2410.04070 | null | 55 | 6 | false | null | International Conference on Learning Representations | 0.437 |
75d6d2345b8da07774dfd290af494015f86565b95bc3828a29668718ac3d9319 | [
"arxiv",
"semantic_scholar"
] | LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding | Auto-Regressive (AR) models have recently gained prominence in image generation, often matching or even surpassing the performance of diffusion models. However, one major limitation of AR models is their sequential nature, which processes tokens one at a time, slowing down generation compared to models like GANs or dif... | [
"Doohyuk Jang",
"Sihwan Park",
"June Yong Yang",
"Yeonsung Jung",
"Jihun Yun",
"Souvik Kundu",
"Sung-Yub Kim",
"Eunho Yang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03355 | https://arxiv.org/pdf/2410.03355v3 | 2410.03355 | 10.48550/arXiv.2410.03355 | 48 | 10 | true | https://github.com/jadohu/LANTERN | International Conference on Learning Representations | 0.5207 |
aa0047df85dde3f7bcf7a7a607ec1c0e1ed0f3543f58cc0322c86cd4e4f31d1c | [
"arxiv",
"semantic_scholar"
] | Mixture of Attentions For Speculative Decoding | The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy. Speculative decoding (SD) leverages smaller models to efficiently propose future tokens, which are then verified by the LLM in parallel. Smal... | [
"Matthieu Zimmer",
"Milan Gritta",
"Gerasimos Lampouras",
"Haitham Bou Ammar",
"Jun Wang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03804 | https://arxiv.org/pdf/2410.03804v2 | 2410.03804 | 10.48550/arXiv.2410.03804 | 17 | 1 | false | null | International Conference on Learning Representations | 0.3138 |
7c7483264deeca5cbb6cdf8ceb8ebf881691fbc8d81f8866467895f5f8a54e7c | [
"arxiv",
"semantic_scholar"
] | Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding | The current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction during inference, resulting in substantial time consumption. In existing studies, Jacobi decoding, an iterative parallel decoding algorithm, h... | [
"Yao Teng",
"Han Shi",
"Xian Liu",
"Xuefei Ning",
"Guohao Dai",
"Yu Wang",
"Zhenguo Li",
"Xihui Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-02T00:00:00 | https://arxiv.org/abs/2410.01699 | https://arxiv.org/pdf/2410.01699v2 | 2410.01699 | 10.48550/arXiv.2410.01699 | 61 | 11 | true | https://github.com/tyshiwo1/Accelerating-T2I-AR-with-SJD/ | International Conference on Learning Representations | 0.5396 |
d7f2a64117ff70b7c5f75112b9810d531001e70ae10f8ba868bff7617a8becb0 | [
"arxiv",
"semantic_scholar"
] | Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity | We present a simple on the fly method for faster inference of large language models. Unlike other (self-)speculative decoding techniques, our method does not require fine-tuning or black-box optimization to generate a fixed draft model, relying instead on simple rules to generate varying draft models adapted to the inp... | [
"Michael R. Metel",
"Peng Lu",
"Boxing Chen",
"Mehdi Rezagholizadeh",
"Ivan Kobyzev"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-01T00:00:00 | https://arxiv.org/abs/2410.01028 | https://arxiv.org/pdf/2410.01028v1 | 2410.01028 | 10.48550/arXiv.2410.01028 | 12 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2785 |
57666f2b5c6effd4ffad19c2ac5a8eb9baa6c8cdbb45bfefc8a4794f643d1fa0 | [
"arxiv",
"semantic_scholar"
] | Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference | Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a smaller auxiliary model to draft future tokens, which a... | [
"Zongyue Qin",
"Zifan He",
"Neha Prakriya",
"Jason Cong",
"Yizhou Sun"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2024-09-25T00:00:00 | https://arxiv.org/abs/2409.16560 | https://arxiv.org/pdf/2409.16560v2 | 2409.16560 | 10.48550/arXiv.2409.16560 | 7 | 2 | false | null | arXiv.org | 0.2386 |
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