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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
897fefe7dea369540b92914d82dd69bfd413b5ff41923eabe887805f357e141b | [
"arxiv",
"semantic_scholar"
] | Improving Multi-candidate Speculative Decoding | Speculative Decoding (SD) is a technique to accelerate the inference of Large Language Models (LLMs) by using a lower complexity draft model to propose candidate tokens verified by a larger target model. To further improve efficiency, Multi-Candidate Speculative Decoding (MCSD) improves upon this by sampling multiple c... | [
"Xiaofan Lu",
"Yixiao Zeng",
"Feiyang Ma",
"Zixu Yu",
"Marco Levorato"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-09-16T00:00:00 | https://arxiv.org/abs/2409.10644 | https://arxiv.org/pdf/2409.10644v3 | 2409.10644 | 10.48550/arXiv.2409.10644 | 6 | 0 | false | null | null | 0.2113 |
2b80eaed4e9a91740e4a9f421b6dbb92f9ba3dc97b89197ea2dba920a341dc19 | [
"arxiv",
"semantic_scholar"
] | Dynamic Depth Decoding: Faster Speculative Decoding for LLMs | The acceleration of Large Language Models (LLMs) with speculative decoding provides a significant runtime improvement without any loss of accuracy. Currently, EAGLE-2 is the state-of-the-art speculative decoding method, improving on EAGLE with a dynamic draft tree. We introduce Dynamic Depth Decoding (DDD), which optim... | [
"Oscar Brown",
"Zhengjie Wang",
"Andrea Do",
"Nikhil Mathew",
"Cheng Yu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-30T00:00:00 | https://arxiv.org/abs/2409.00142 | https://arxiv.org/pdf/2409.00142v1 | 2409.00142 | 10.48550/arXiv.2409.00142 | 17 | 3 | false | null | arXiv.org | 0.3138 |
2da62c5a97f735d04c021fc624526aa374355314904ac96594a7e5c49cfb9afd | [
"arxiv",
"semantic_scholar"
] | Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation | Lossless speculative decoding accelerates target large language model (LLM) inference by employing a lightweight draft model for generating tree-structured candidates, which are subsequently verified in parallel by the target LLM. Currently, effective approaches leverage feature-level rather than token-level autoregres... | [
"Lujun Gui",
"Bin Xiao",
"Lei Su",
"Weipeng Chen"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-28T00:00:00 | https://arxiv.org/abs/2408.15562 | https://arxiv.org/pdf/2408.15562v1 | 2408.15562 | 10.48550/arXiv.2408.15562 | 9 | 1 | false | null | arXiv.org | 0.25 |
c84e0ec48439b9449dc2da31ce84c2a16274440f66ea6ce54f4f0c72dec07993 | [
"arxiv",
"semantic_scholar"
] | Learning Harmonized Representations for Speculative Sampling | Speculative sampling is a promising approach to accelerate the decoding stage for Large Language Models (LLMs). Recent advancements that leverage target LLM's contextual information, such as hidden states and KV cache, have shown significant practical improvements. However, these approaches suffer from inconsistent con... | [
"Lefan Zhang",
"Xiaodan Wang",
"Yanhua Huang",
"Ruiwen Xu"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-08-28T00:00:00 | https://arxiv.org/abs/2408.15766 | https://arxiv.org/pdf/2408.15766v3 | 2408.15766 | null | 67 | 5 | true | https://github.com/HArmonizedSS/HASS | International Conference on Learning Representations | 0.4581 |
ec9d4b656d8c4e193d8b5a0c921a6ead190da563d3d3da5211f26d9aa727fa16 | [
"arxiv",
"semantic_scholar"
] | Can Unconfident LLM Annotations Be Used for Confident Conclusions? | Large language models (LLMs) have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. In computational social science (CSS), researchers are increasingly leveraging LLM annotations to complement slow and expensive human annotations. ... | [
"Kristina Gligorić",
"Tijana Zrnic",
"Cinoo Lee",
"Emmanuel J. Candès",
"Dan Jurafsky"
] | [
"cs.CL",
"cs.AI",
"cs.HC"
] | [
"Computer Science"
] | 2024-08-27T00:00:00 | https://arxiv.org/abs/2408.15204 | https://arxiv.org/pdf/2408.15204v2 | 2408.15204 | 10.48550/arXiv.2408.15204 | 51 | 2 | false | null | North American Chapter of the Association for Computational Linguistics | 0.429 |
a29b63ac083c472e85fc6558020bb27a52751eb03841f0e1f70d97e041904732 | [
"arxiv",
"semantic_scholar"
] | The Mamba in the Llama: Distilling and Accelerating Hybrid Models | Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment. We demonstrate that it i... | [
"Junxiong Wang",
"Daniele Paliotta",
"Avner May",
"Alexander M. Rush",
"Tri Dao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-27T00:00:00 | https://arxiv.org/abs/2408.15237 | https://arxiv.org/pdf/2408.15237v4 | 2408.15237 | 10.48550/arXiv.2408.15237 | 124 | 21 | true | https://github.com/jxiw/MambaInLlama | Neural Information Processing Systems | 0.6712 |
fe26000d46f6e01a99726870640909867c4242645cb720f7d388c008f34369b8 | [
"arxiv",
"semantic_scholar"
] | Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates | LLM-as-a-Judge has been widely applied to evaluate and compare different LLM alignmnet approaches (e.g., RLHF and DPO). However, concerns regarding its reliability have emerged, due to LLM judges' biases and inconsistent decision-making. Previous research has developed evaluation frameworks to assess reliability of LLM... | [
"Hui Wei",
"Shenghua He",
"Tian Xia",
"Fei Liu",
"Andy Wong",
"Jingyang Lin",
"Mei Han"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-23T00:00:00 | https://arxiv.org/abs/2408.13006 | https://arxiv.org/pdf/2408.13006v2 | 2408.13006 | 10.48550/arXiv.2408.13006 | 81 | 6 | true | null | arXiv.org | 0.4785 |
5f75adcab36fdafaeffd2cadfdbad069f1caaf11b8e2d172026d1b921a2f6425 | [
"arxiv",
"semantic_scholar"
] | MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding | Large Language Models (LLMs) have become more prevalent in long-context applications such as interactive chatbots, document analysis, and agent workflows, but it is challenging to serve long-context requests with low latency and high throughput. Speculative decoding (SD) is a widely used technique to reduce latency los... | [
"Ranajoy Sadhukhan",
"Jian Chen",
"Zhuoming Chen",
"Vashisth Tiwari",
"Ruihang Lai",
"Jinyuan Shi",
"Ian En-Hsu Yen",
"Avner May",
"Tianqi Chen",
"Beidi Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.11049 | https://arxiv.org/pdf/2408.11049v5 | 2408.11049 | 10.48550/arXiv.2408.11049 | 83 | 10 | false | null | International Conference on Learning Representations | 0.5207 |
064a394a74613620e565b507fd44c2eec7f1cf2e039d89799b1d5825dd9dbe87 | [
"arxiv",
"semantic_scholar"
] | Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search | Traditional reinforcement learning and planning typically requires vast amounts of data and training to develop effective policies. In contrast, large language models (LLMs) exhibit strong generalization and zero-shot capabilities, but struggle with tasks that require detailed planning and decision-making in complex ac... | [
"Jonathan Light",
"Min Cai",
"Weiqin Chen",
"Guanzhi Wang",
"Xiusi Chen",
"Wei Cheng",
"Yisong Yue",
"Ziniu Hu"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10635 | https://arxiv.org/pdf/2408.10635v3 | 2408.10635 | null | 14 | 2 | false | null | null | 0.294 |
ed971c5dd045a30c17672f24c34a6829477feb4f08b770fbaf3144b2ae1256ab | [
"arxiv",
"semantic_scholar"
] | KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft Heads with Adversarial Learning | Large Language Models (LLMs) exhibit high inference latency due to their autoregressive decoding nature. While the draft head in speculative decoding mitigates this issue, its full potential remains unexplored. In this paper, we introduce KOALA (K-layer Optimized Adversarial Learning Architecture), an orthogonal approa... | [
"Kaiqi Zhang",
"Jing Zhao",
"Rui Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08146 | https://arxiv.org/pdf/2408.08146v1 | 2408.08146 | 10.1109/CSCWD64889.2025.11033265 | 5 | 0 | false | null | International Conference on Computer Supported Cooperative Work in Design | 0.1945 |
1f7a4cd0af7d7b9ce1dcec72f540aeaef944b909c1210f7c005ab9a2853612f3 | [
"arxiv",
"semantic_scholar"
] | PEARL: Parallel Speculative Decoding with Adaptive Draft Length | Speculative decoding (SD), where an extra draft model is employed to provide multiple draft tokens first, and then the original target model verifies these tokens in parallel, has shown great power for LLM inference acceleration. However, existing SD methods suffer from the mutual waiting problem, i.e., the target mode... | [
"Tianyu Liu",
"Yun Li",
"Qitan Lv",
"Kai Liu",
"Jianchen Zhu",
"Winston Hu",
"Xiao Sun"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-13T00:00:00 | https://arxiv.org/abs/2408.11850 | https://arxiv.org/pdf/2408.11850v3 | 2408.11850 | 10.48550/arXiv.2408.11850 | 74 | 7 | true | https://github.com/smart-lty/ParallelSpeculativeDecoding | International Conference on Learning Representations | 0.4688 |
18c86cd7db58bc39263e38afc6c13f2693fc2e798c8050328a6fdc25ecf378df | [
"arxiv",
"semantic_scholar"
] | Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding | The past few years have witnessed a growing interest in LLM-based recommender systems (RSs), although their industrial deployment remains in a preliminary stage. Most existing deployments leverage LLMs offline as feature enhancers, generating augmented knowledge for downstream tasks. However, in recommendation scenario... | [
"Yunjia Xi",
"Hangyu Wang",
"Bo Chen",
"Jianghao Lin",
"Menghui Zhu",
"Weiwen Liu",
"Ruiming Tang",
"Zhewei Wei",
"Weinan Zhang",
"Yong Yu"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2024-08-11T00:00:00 | https://arxiv.org/abs/2408.05676 | https://arxiv.org/pdf/2408.05676v2 | 2408.05676 | 10.1145/3726302.3729961 | 14 | 0 | true | https://github.com/YunjiaXi/LASER | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval | 0.294 |
7b64842ab54d8e6b89a05d0da83d12278a66987bc33f0344ae34e79d33a612be | [
"arxiv",
"semantic_scholar"
] | Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion | Speculative decoding has emerged as a widely adopted method to accelerate large language model inference without sacrificing the quality of the model outputs. While this technique has facilitated notable speed improvements by enabling parallel sequence verification, its efficiency remains inherently limited by the reli... | [
"Jacob K Christopher",
"Brian R Bartoldson",
"Tal Ben-Nun",
"Michael Cardei",
"Bhavya Kailkhura",
"Ferdinando Fioretto"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-10T00:00:00 | https://arxiv.org/abs/2408.05636 | https://arxiv.org/pdf/2408.05636v4 | 2408.05636 | 10.48550/arXiv.2408.05636 | 47 | 2 | false | null | North American Chapter of the Association for Computational Linguistics | 0.4203 |
a90f81ad13efb3e6dc609d5b3bd739aca0301530705a00c1ff9fc9f6f82da3f0 | [
"arxiv",
"semantic_scholar"
] | CREST: Effectively Compacting a Datastore For Retrieval-Based Speculative Decoding | We present CREST (Compact Retrieval-Based Speculative Decoding), a redesign of REST that allows it to be effectively "compacted". REST is a drafting technique for speculative decoding based on retrieving exact n-gram matches of the most recent n tokens generated by the target LLM from a datastore. The key idea of CREST... | [
"Sophia Ho",
"Jinsol Park",
"Patrick Wang"
] | [
"cs.CL",
"cs.AI",
"cs.DB"
] | [
"Computer Science"
] | 2024-08-08T00:00:00 | https://arxiv.org/abs/2408.04678 | https://arxiv.org/pdf/2408.04678v1 | 2408.04678 | 10.48550/arXiv.2408.04678 | 0 | 0 | false | null | arXiv.org | 0 |
ddb871574bb02d70447dd8b089e6c55acd743758688826735cdca4e2ef78326f | [
"arxiv",
"semantic_scholar"
] | Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding | Large Language Models (LLMs) frequently suffer from inefficiencies, largely attributable to the discord between the requirements of auto-regressive decoding and the architecture of contemporary GPUs. Recently, regressive lightweight speculative decoding has garnered attention for its notable efficiency improvements in ... | [
"Bin Xiao",
"Lujun Gui",
"Lei Su",
"Weipeng Chen"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-01T00:00:00 | https://arxiv.org/abs/2408.00264 | https://arxiv.org/pdf/2408.00264v1 | 2408.00264 | 10.48550/arXiv.2408.00264 | 5 | 0 | true | null | arXiv.org | 0.1945 |
428718b4c19974153cddfdd4f2beab5591a5f3bd22a531cdb196e0658f9d287a | [
"arxiv",
"semantic_scholar"
] | Graph-Structured Speculative Decoding | Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models (LLMs) by employing a small language model to draft a hypothesis sequence, which is then validated by the LLM. The effectiveness of this approach heavily relies on the balance between performance and efficienc... | [
"Zhuocheng Gong",
"Jiahao Liu",
"Ziyue Wang",
"Pengfei Wu",
"Jingang Wang",
"Xunliang Cai",
"Dongyan Zhao",
"Rui Yan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-07-23T00:00:00 | https://arxiv.org/abs/2407.16207 | https://arxiv.org/pdf/2407.16207v1 | 2407.16207 | 10.48550/arXiv.2407.16207 | 7 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2258 |
dadbb611d1d138a6f11d071145604220e650be30d6a7897cbe4583908502ed7c | [
"arxiv",
"semantic_scholar"
] | PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation | Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from CPU speculative execution. These techniques reduce bottlenecks associated with memory bandwidth, but also increase end-to-end latency per inf... | [
"Branden Butler",
"Sixing Yu",
"Arya Mazaheri",
"Ali Jannesari"
] | [
"cs.CL",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.11798 | https://arxiv.org/pdf/2407.11798v2 | 2407.11798 | 10.1109/SC41406.2024.00046 | 27 | 2 | false | null | International Conference for High Performance Computing, Networking, Storage and Analysis | 0.3618 |
306d6d9f9fded19d1a3ca7281bec07eeac0aff5e30c21e5aa6d31c11b7dd5e77 | [
"arxiv",
"semantic_scholar"
] | Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference | Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-token generation at each decoding step. While previous methods such as speculative decoding mitigate these inefficiencies by producing multi... | [
"Zongyue Qin",
"Ziniu Hu",
"Zifan He",
"Neha Prakriya",
"Jason Cong",
"Yizhou Sun"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-07-12T00:00:00 | https://arxiv.org/abs/2407.09722 | https://arxiv.org/pdf/2407.09722v4 | 2407.09722 | null | 12 | 1 | false | null | International Conference on Learning Representations | 0.2785 |
1f783f2b3fd029ca1701073fd121b350dbc6477cc27e408a97abe79a291431ed | [
"arxiv",
"semantic_scholar"
] | FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation | This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for the first time how to train a large-scale binary language model from scratch (not the partial binary or ternary LLM like BitNet b1.58) to match the performance of its full-precision counterparts (e.g., FP16 or BF16) in transformer-ba... | [
"Liqun Ma",
"Mingjie Sun",
"Zhiqiang Shen"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-07-09T00:00:00 | https://arxiv.org/abs/2407.07093 | https://arxiv.org/pdf/2407.07093v1 | 2407.07093 | 10.48550/arXiv.2407.07093 | 15 | 3 | true | https://github.com/LiqunMa/FBI-LLM | arXiv.org | 0.301 |
abe057f735ac280b7723f4fa0f7986ffa76606d2d551e291ba01f34cbdbb7427 | [
"arxiv",
"semantic_scholar"
] | S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models | Deployment of autoregressive large language models (LLMs) is costly, and as these models increase in size, the associated costs will become even more considerable. Consequently, different methods have been proposed to accelerate the token generation process and reduce costs. Speculative decoding (SD) is among the most ... | [
"Parsa Kavehzadeh",
"Mohammadreza Pourreza",
"Mojtaba Valipour",
"Tinashu Zhu",
"Haoli Bai",
"Ali Ghodsi",
"Boxing Chen",
"Mehdi Rezagholizadeh"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-07-02T00:00:00 | https://arxiv.org/abs/2407.01955 | https://arxiv.org/pdf/2407.01955v1 | 2407.01955 | 10.48550/arXiv.2407.01955 | 1 | 0 | false | null | arXiv.org | 0.0753 |
db3aa7aa458b66fd5617181280bdabef432e81fe16d346e8fe5d3503e195b44f | [
"arxiv",
"semantic_scholar"
] | SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding | Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by surpassing the capabilities of chain-of-thought prompting, encouraging exploration of intermediate steps. However, ... | [
"Zhenglin Wang",
"Jialong Wu",
"Yilong Lai",
"Congzhi Zhang",
"Deyu Zhou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-26T00:00:00 | https://arxiv.org/abs/2406.18200 | https://arxiv.org/pdf/2406.18200v2 | 2406.18200 | 10.48550/arXiv.2406.18200 | 13 | 1 | false | null | International Conference on Computational Linguistics | 0.2865 |
ce3dad317ad49b0f63949f786033f1df3dc130c3c458aae6a317a60d2cdf07de | [
"arxiv",
"semantic_scholar"
] | OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure | Autoregressive language models demonstrate excellent performance in various scenarios. However, the inference efficiency is limited by its one-step-one-word generation mode, which has become a pressing problem recently as the models become increasingly larger. Speculative decoding employs a "draft and then verify" mech... | [
"Jikai Wang",
"Yi Su",
"Juntao Li",
"Qingrong Xia",
"Zi Ye",
"Xinyu Duan",
"Zhefeng Wang",
"Min Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-25T00:00:00 | https://arxiv.org/abs/2406.17276 | https://arxiv.org/pdf/2406.17276v4 | 2406.17276 | 10.48550/arXiv.2406.17276 | 56 | 6 | true | https://github.com/Jikai0Wang/OPT-Tree | Transactions of the Association for Computational Linguistics | 0.439 |
ef379ac790a45ee522ae1f4ab05fc54855c7cefd14099389190ee49b7bb256b3 | [
"arxiv",
"semantic_scholar"
] | Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters | Large language models (LLMs) have revolutionized natural language processing and broadened their applicability across diverse commercial applications. However, the deployment of these models is constrained by high inference time in multilingual settings. To mitigate this challenge, this paper explores a training recipe... | [
"Euiin Yi",
"Taehyeon Kim",
"Hongseok Jeung",
"Du-Seong Chang",
"Se-Young Yun"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-24T00:00:00 | https://arxiv.org/abs/2406.16758 | https://arxiv.org/pdf/2406.16758v2 | 2406.16758 | 10.48550/arXiv.2406.16758 | 13 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2865 |
8034281d98b6ffbc0501915288535e43ee0f5b8602f82c1b8241714cf633bec2 | [
"arxiv",
"semantic_scholar"
] | TurboSpec: Closed-loop Speculation Control System for Optimizing LLM Serving Goodput | Large Language Model (LLM) serving systems batch concurrent user requests to achieve efficient serving. However, in real-world deployments, such inter-request parallelism from batching is often limited by external factors such as low request rates or memory constraints. Recent works focus on intra-request parallelism f... | [
"Xiaoxuan Liu",
"Jongseok Park",
"Langxiang Hu",
"Woosuk Kwon",
"Zhuohan Li",
"Chen Zhang",
"Kuntai Du",
"Xiangxi Mo",
"Kaichao You",
"Alvin Cheung",
"Zhijie Deng",
"Ion Stoica",
"Hao Zhang"
] | [
"cs.AI",
"cs.PF"
] | [
"Computer Science"
] | 2024-06-20T00:00:00 | https://arxiv.org/abs/2406.14066 | https://arxiv.org/pdf/2406.14066v3 | 2406.14066 | null | 33 | 3 | false | null | null | 0.3829 |
22e0561929a8c2460264287c414e6f1894c51b738d452f52cd8381d1413867c9 | [
"arxiv",
"semantic_scholar"
] | Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference | Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed. While methods such as Medusa constructs parallelized heads, they lack adequate information interaction across different prediction positions. To overcome this limi... | [
"Zeping Li",
"Xinlong Yang",
"Ziheng Gao",
"Ji Liu",
"Guanchen Li",
"Zhuang Liu",
"Dong Li",
"Jinzhang Peng",
"Lu Tian",
"Emad Barsoum"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13170 | https://arxiv.org/pdf/2406.13170v2 | 2406.13170 | 10.18653/v1/2025.naacl-long.450 | 2 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.1193 |
785c8f41d6f86de01e8a77ecd1765a09b127639d7e6e945e404c5c79645ec71c | [
"arxiv",
"semantic_scholar"
] | Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena | Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after adjustments for factors like length. Unfortunately, LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent bi... | [
"Aidar Myrzakhan",
"Sondos Mahmoud Bsharat",
"Zhiqiang Shen"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-06-11T00:00:00 | https://arxiv.org/abs/2406.07545 | https://arxiv.org/pdf/2406.07545v1 | 2406.07545 | 10.48550/arXiv.2406.07545 | 86 | 2 | true | https://github.com/VILA-Lab/Open-LLM-Leaderboard | arXiv.org | 0.4849 |
5eb4eb7cd49f02bd42c0bf866f40f445d115c2fcad865fd5d686690df1124f9f | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism | The recent advancements in large language models (LLMs) have been extraordinary, yet the escalating inference costs associated with them present challenges in real-world applications. To address these challenges, we propose a novel approach called Early-exiting Speculative Decoding (EESD) with lossless acceleration. Sp... | [
"Jiahao Liu",
"Qifan Wang",
"Jingang Wang",
"Xunliang Cai"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03853 | https://arxiv.org/pdf/2406.03853v1 | 2406.03853 | 10.48550/arXiv.2406.03853 | 37 | 3 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3949 |
ef5db1ddb7159d4aba37bcce453e447b78f8d5c9213b6a510357586cb1688d9d | [
"arxiv",
"semantic_scholar"
] | SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices | As large language models gain widespread adoption, running them efficiently becomes crucial. Recent works on LLM inference use speculative decoding to achieve extreme speedups. However, most of these works implicitly design their algorithms for high-end datacenter hardware. In this work, we ask the opposite question: h... | [
"Ruslan Svirschevski",
"Avner May",
"Zhuoming Chen",
"Beidi Chen",
"Zhihao Jia",
"Max Ryabinin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.02532 | https://arxiv.org/pdf/2406.02532v3 | 2406.02532 | 10.48550/arXiv.2406.02532 | 56 | 5 | false | null | Neural Information Processing Systems | 0.439 |
7abac9e41e114e05fbc1fbeb490ba5b3f3bf45535c9f583bbac3f5c19a5bfb5d | [
"arxiv",
"semantic_scholar"
] | Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller | We propose SelfControl, an inference-time model control method utilizing gradients to control the behavior of large language models (LLMs) without explicit human annotations. Given a desired behavior expressed in a natural language suffix string concatenated to the input prompt, SelfControl computes gradients of the LL... | [
"Min Cai",
"Yuchen Zhang",
"Shichang Zhang",
"Fan Yin",
"Dan Zhang",
"Difan Zou",
"Yisong Yue",
"Ziniu Hu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.02721 | https://arxiv.org/pdf/2406.02721v3 | 2406.02721 | 10.48550/arXiv.2406.02721 | 5 | 0 | false | null | arXiv.org | 0.1945 |
5d510621df1af61993e486569d632a3169160ed273a382ffb3269724f1bcce10 | [
"arxiv",
"semantic_scholar"
] | Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models | Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts. However, deploying these gigantic models efficiently for diverse inference use cases requires carefully designed hardware platforms with ample computing, memory, and network resources. ... | [
"Abhimanyu Bambhaniya",
"Ritik Raj",
"Geonhwa Jeong",
"Souvik Kundu",
"Sudarshan Srinivasan",
"Suvinay Subramanian",
"Midhilesh Elavazhagan",
"Madhu Kumar",
"Tushar Krishna"
] | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01698 | https://arxiv.org/pdf/2406.01698v3 | 2406.01698 | null | 12 | 0 | true | https://github.com/abhibambhaniya/GenZ-LLM-Analyzer | null | 0.2785 |
e73f3105161668b763b4acd4585d5612ad49ff1af88863abc2bb8070eb9fb246 | [
"arxiv",
"semantic_scholar"
] | Snowflake: A Distributed Streaming Decoder | We design Snowflake, a quantum error correction decoder that, for the surface code under circuit-level noise, is roughly 25% more accurate than the Union-Find decoder, with a better mean runtime scaling: subquadratic as opposed to cubic in the code distance. Our decoder runs in a streaming fashion and has a distributed... | [
"Tim Chan"
] | [
"quant-ph"
] | [
"Physics",
"Computer Science"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01701 | https://arxiv.org/pdf/2406.01701v3 | 2406.01701 | 10.22331/q-2026-03-20-2033 | 4 | 0 | false | null | Quantum | 0.1747 |
935b4da18d05ec11f70059c8a9b4e7a8ebca75c36f59561389ef81144faac39e | [
"arxiv",
"semantic_scholar"
] | S3D: A Simple and Cost-Effective Self-Speculative Decoding Scheme for Low-Memory GPUs | Speculative decoding (SD) has attracted a significant amount of research attention due to the substantial speedup it can achieve for LLM inference. However, despite the high speedups they offer, speculative decoding methods often achieve optimal performance on high-end devices or with a substantial GPU memory overhead.... | [
"Wei Zhong",
"Manasa Bharadwaj"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-30T00:00:00 | https://arxiv.org/abs/2405.20314 | https://arxiv.org/pdf/2405.20314v2 | 2405.20314 | 10.48550/arXiv.2405.20314 | 10 | 0 | true | null | arXiv.org | 0.2603 |
356d9d45e8d7e97dd240cd314d1c3c84b5ec0bd11169ac96e41b4e0b6a67260e | [
"arxiv",
"semantic_scholar"
] | SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths | Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model. Its performance depends on a hyperparameter K -- the candidate length, i.e., the number of candidate tokens for the target model to verify in each round. However, previous methods often us... | [
"Kaixuan Huang",
"Xudong Guo",
"Mengdi Wang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-30T00:00:00 | https://arxiv.org/abs/2405.19715 | https://arxiv.org/pdf/2405.19715v3 | 2405.19715 | 10.48550/arXiv.2405.19715 | 56 | 3 | true | https://github.com/Kaffaljidhmah2/SpecDec_pp | arXiv.org | 0.439 |
5fc24943753a416bc170e82fa881004a68ec663c429acf2880295913352ba6ab | [
"arxiv",
"semantic_scholar"
] | Nearest Neighbor Speculative Decoding for LLM Generation and Attribution | Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models of... | [
"Minghan Li",
"Xilun Chen",
"Ari Holtzman",
"Beidi Chen",
"Jimmy Lin",
"Wen-tau Yih",
"Xi Victoria Lin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.19325 | https://arxiv.org/pdf/2405.19325v3 | 2405.19325 | 10.48550/arXiv.2405.19325 | 26 | 3 | true | https://github.com/facebookresearch/NEST/tree/main | Neural Information Processing Systems | 0.3578 |
6acae4981190d44b8a78f2cf32317774fcb65daa0c990c40314f76b15bcd050b | [
"arxiv",
"semantic_scholar"
] | Faster Cascades via Speculative Decoding | Cascades and speculative decoding are two common approaches to improving language models' inference efficiency. Both approaches involve interleaving models of different sizes, but via fundamentally distinct mechanisms: cascades employ a deferral rule that invokes the larger model only for "hard" inputs, while speculati... | [
"Harikrishna Narasimhan",
"Wittawat Jitkrittum",
"Ankit Singh Rawat",
"Seungyeon Kim",
"Neha Gupta",
"Aditya Krishna Menon",
"Sanjiv Kumar"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.19261 | https://arxiv.org/pdf/2405.19261v2 | 2405.19261 | 10.48550/arXiv.2405.19261 | 35 | 2 | false | null | International Conference on Learning Representations | 0.3891 |
53ffe10d24c9bd1a3d69f53a2fe98e676e2c5cad7420b803975f92bab5f3f9a2 | [
"arxiv",
"semantic_scholar"
] | Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference | The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has investigated various speculative decoding techniques for multi-token generation, these efforts have primarily focused on improving processing speed such as throughput. C... | [
"Hao Mark Chen",
"Wayne Luk",
"Ka Fai Cedric Yiu",
"Rui Li",
"Konstantin Mishchenko",
"Stylianos I. Venieris",
"Hongxiang Fan"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-05-28T00:00:00 | https://arxiv.org/abs/2405.18628 | https://arxiv.org/pdf/2405.18628v3 | 2405.18628 | 10.48550/arXiv.2405.18628 | 18 | 2 | true | https://github.com/hmarkc/parallel-prompt-decoding | Conference on Empirical Methods in Natural Language Processing | 0.3197 |
5bbe9626cc1cf15881a604c0999978a672132bba1c5e3e2521bf65a7433d5549 | [
"arxiv",
"semantic_scholar"
] | Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference | This paper introduces distributed speculative inference (DSI), a novel inference algorithm that is provably faster than speculative inference (SI) [leviathan2023, chen2023, miao2024, sun2025, timor2025] and standard autoregressive inference (non-SI). Like other SI algorithms, DSI operates on frozen language models (LMs... | [
"Nadav Timor",
"Jonathan Mamou",
"Daniel Korat",
"Moshe Berchansky",
"Oren Pereg",
"Moshe Wasserblat",
"Tomer Galanti",
"Michal Gordon",
"David Harel"
] | [
"cs.DC",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14105 | https://arxiv.org/pdf/2405.14105v5 | 2405.14105 | null | 9 | 0 | true | null | International Conference on Learning Representations | 0.25 |
3ae87cc8276a115349987a9b658e679ebc310bb5807fb517372f2c4b2bd93424 | [
"arxiv",
"semantic_scholar"
] | EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models | Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficiency, multi-sample speculative decoding has been overlooked due to varying numbers of accepted tokens within a batch in the verification phase... | [
"Yunsheng Ni",
"Chuanjian Liu",
"Yehui Tang",
"Kai Han",
"Yunhe Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07542 | https://arxiv.org/pdf/2405.07542v2 | 2405.07542 | 10.48550/arXiv.2405.07542 | 2 | 0 | true | https://github.com/niyunsheng/EMS-SD | North American Chapter of the Association for Computational Linguistics | 0.1193 |
9c044b20b3f753e2057a7cf0bfbcf3b4942ed264594acd96b0fb0ab7a6da68b5 | [
"arxiv",
"semantic_scholar"
] | Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models | Speculative decoding is commonly used for reducing the inference latency of large language models. Its effectiveness depends highly on the speculation lookahead (SL)-the number of tokens generated by the draft model at each iteration. In this work we show that the common practice of using the same SL for all iterations... | [
"Jonathan Mamou",
"Oren Pereg",
"Daniel Korat",
"Moshe Berchansky",
"Nadav Timor",
"Moshe Wasserblat",
"Roy Schwartz"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-07T00:00:00 | https://arxiv.org/abs/2405.04304 | https://arxiv.org/pdf/2405.04304v5 | 2405.04304 | null | 18 | 1 | false | null | null | 0.3197 |
6e6a31fb0f13afbc376c2f0d03feb04b6190ed5fe638c0456f835644fc662c42 | [
"arxiv",
"semantic_scholar"
] | Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge | Large language models (LLMs) suffer from low efficiency as the mismatch between the requirement of auto-regressive decoding and the design of most contemporary GPUs. Specifically, billions to trillions of parameters must be loaded to the GPU cache through its limited memory bandwidth for computation, but only a small b... | [
"Bin Xiao",
"Chunan Shi",
"Xiaonan Nie",
"Fan Yang",
"Xiangwei Deng",
"Lei Su",
"Weipeng Chen",
"Bin Cui"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-01T00:00:00 | https://arxiv.org/abs/2405.00263 | https://arxiv.org/pdf/2405.00263v1 | 2405.00263 | 10.48550/arXiv.2405.00263 | 11 | 0 | false | null | arXiv.org | 0.2698 |
988206e09b0b62e53fdb357e771c69307ba14973e9457e00ac4eb041a9eaf696 | [
"arxiv",
"semantic_scholar"
] | Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting | Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models while maintaining a consistent sampling distribution. However, the conventional approach of training a separate draft model to achieve a satisfactory token acceptance rate can be costly. Drawing inspiration fr... | [
"Fangcheng Liu",
"Yehui Tang",
"Zhenhua Liu",
"Yunsheng Ni",
"Kai Han",
"Yunhe Wang"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-29T00:00:00 | https://arxiv.org/abs/2404.18911 | https://arxiv.org/pdf/2404.18911v1 | 2404.18911 | 10.48550/arXiv.2404.18911 | 57 | 6 | true | https://github.com/Equationliu/Kangaroo | arXiv.org | 0.4409 |
681e54debd120618f1188d9050fd375d4cc2aba9011575ae6de3f87b025444b7 | [
"arxiv",
"semantic_scholar"
] | LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding | We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during ... | [
"Mostafa Elhoushi",
"Akshat Shrivastava",
"Diana Liskovich",
"Basil Hosmer",
"Bram Wasti",
"Liangzhen Lai",
"Anas Mahmoud",
"Bilge Acun",
"Saurabh Agarwal",
"Ahmed Roman",
"Ahmed A Aly",
"Beidi Chen",
"Carole-Jean Wu"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16710 | https://arxiv.org/pdf/2404.16710v4 | 2404.16710 | 10.18653/v1/2024.acl-long.681 | 269 | 19 | true | https://github.com/facebookresearch/LayerSkip | Annual Meeting of the Association for Computational Linguistics | 0.6505 |
0611f3f579c30fc8e483c693d7710d3991274d1c259a9f7a0ad60cea8101ae8c | [
"arxiv",
"semantic_scholar"
] | Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models | With the increasingly giant scales of (causal) large language models (LLMs), the inference efficiency comes as one of the core concerns along the improved performance. In contrast to the memory footprint, the latency bottleneck seems to be of greater importance as there can be billions of requests to a LLM (e.g., GPT-4... | [
"Chen Zhang",
"Zhuorui Liu",
"Dawei Song"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.14897 | https://arxiv.org/pdf/2404.14897v1 | 2404.14897 | 10.48550/arXiv.2404.14897 | 10 | 1 | false | null | arXiv.org | 0.2603 |
486f06f046f7434f0b6a3518eab5a304f6d019bfc36589f3a92c7eb39bd7e757 | [
"arxiv",
"semantic_scholar"
] | TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding | With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the s... | [
"Hanshi Sun",
"Zhuoming Chen",
"Xinyu Yang",
"Yuandong Tian",
"Beidi Chen"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-18T00:00:00 | https://arxiv.org/abs/2404.11912 | https://arxiv.org/pdf/2404.11912v3 | 2404.11912 | 10.48550/arXiv.2404.11912 | 105 | 5 | true | https://github.com/Infini-AI-Lab/TriForce | arXiv.org | 0.5063 |
38f2329b1ef98400df42f77b25c01a1f73c62d4d5dedc31c9f84aae3b52a2877 | [
"arxiv",
"semantic_scholar"
] | On Speculative Decoding for Multimodal Large Language Models | Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-regressively. In this paper, we explore the application of speculative decoding to enhance the inference efficiency of MLLMs, specifically t... | [
"Mukul Gagrani",
"Raghavv Goel",
"Wonseok Jeon",
"Junyoung Park",
"Mingu Lee",
"Christopher Lott"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-13T00:00:00 | https://arxiv.org/abs/2404.08856 | https://arxiv.org/pdf/2404.08856v1 | 2404.08856 | 10.48550/arXiv.2404.08856 | 32 | 4 | false | null | arXiv.org | 0.3796 |
b0e014a46b5ee6e40ba56180a410ddb821c9b41f5eeb39921eb6ecb4583342b8 | [
"arxiv",
"semantic_scholar"
] | DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference | Large language models (LLMs) are increasingly employed for complex tasks that process multiple generation calls in a tree structure with shared prefixes of tokens, including few-shot prompting, multi-step reasoning, speculative decoding, etc. However, existing inference systems for tree-based applications are inefficie... | [
"Jinwei Yao",
"Kaiqi Chen",
"Kexun Zhang",
"Jiaxuan You",
"Binhang Yuan",
"Zeke Wang",
"Tao Lin"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-03-30T00:00:00 | https://arxiv.org/abs/2404.00242 | https://arxiv.org/pdf/2404.00242v4 | 2404.00242 | null | 14 | 2 | true | https://github.com/LINs-lab/DeFT | International Conference on Learning Representations | 0.294 |
1d4fbe25611f739b2a42a7212b17961281f0d81a44127644286252d422782029 | [
"arxiv",
"semantic_scholar"
] | SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens | We propose an acceleration scheme for large language models (LLMs) through Speculative Decoding with Semantic Adaptive Tokens (SDSAT). The primary objective of this design is to enhance the LLM model's ability to generate draft tokens more accurately without compromising the model's accuracy. The core strategies involv... | [
"Chengbo Liu",
"Yong Zhu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-03-27T00:00:00 | https://arxiv.org/abs/2403.18647 | https://arxiv.org/pdf/2403.18647v2 | 2403.18647 | 10.48550/arXiv.2403.18647 | 2 | 1 | true | https://github.com/hasuoshenyun/SDSAT | arXiv.org | 0.1505 |
27202f7f1e8f0f0b1f7f473545aaed629420bfc2110e52590e1798b175574b0b | [
"arxiv",
"semantic_scholar"
] | Block Verification Accelerates Speculative Decoding | Speculative decoding is an effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then verified in parallel by the target model, and provides a guarantee that the output is distributed identically to a sample from the target model.... | [
"Ziteng Sun",
"Uri Mendlovic",
"Yaniv Leviathan",
"Asaf Aharoni",
"Jae Hun Ro",
"Ahmad Beirami",
"Ananda Theertha Suresh"
] | [
"cs.LG",
"cs.CL",
"cs.DS",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2024-03-15T00:00:00 | https://arxiv.org/abs/2403.10444 | https://arxiv.org/pdf/2403.10444v3 | 2403.10444 | null | 25 | 4 | false | null | International Conference on Learning Representations | 0.3537 |
3d23fefddad90bbce7ee7a548a2a9970ea0da484b3adffaf6c1b83167c1162b1 | [
"arxiv",
"semantic_scholar"
] | Recurrent Drafter for Fast Speculative Decoding in Large Language Models | We present Recurrent Drafter (ReDrafter), an advanced speculative decoding approach that achieves state-of-the-art speedup for large language models (LLMs) inference. The performance gains are driven by three key aspects: (1) leveraging a recurrent neural network (RNN) as the draft model conditioning on LLM's hidden st... | [
"Yunfei Cheng",
"Aonan Zhang",
"Xuanyu Zhang",
"Chong Wang",
"Yi Wang"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-14T00:00:00 | https://arxiv.org/abs/2403.09919 | https://arxiv.org/pdf/2403.09919v5 | 2403.09919 | 10.48550/arXiv.2403.09919 | 33 | 6 | false | null | arXiv.org | 0.4225 |
d09913b1792ce60e9925a92c4df817dbde664fd5342f45bee4e811c0af2ac801 | [
"arxiv",
"semantic_scholar"
] | Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs | Text generation with Large Language Models (LLMs) is known to be memory bound due to the combination of their auto-regressive nature, huge parameter counts, and limited memory bandwidths, often resulting in low token rates. Speculative decoding has been proposed as a solution for LLM inference acceleration. However, si... | [
"Raghavv Goel",
"Mukul Gagrani",
"Wonseok Jeon",
"Junyoung Park",
"Mingu Lee",
"Christopher Lott"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2403.00858 | https://arxiv.org/pdf/2403.00858v4 | 2403.00858 | 10.48550/arXiv.2403.00858 | 15 | 1 | true | null | arXiv.org | 0.301 |
c4e2410b3603dd01c8663b5c3afc115ea094d3fd9080050889f87fb49129bfdf | [
"arxiv",
"semantic_scholar"
] | Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution | Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only one at a time. Although research works apply pruning or quantization t... | [
"Siqi Wang",
"Hailong Yang",
"Xuezhu Wang",
"Tongxuan Liu",
"Pengbo Wang",
"Xuning Liang",
"Kejie Ma",
"Tianyu Feng",
"Xin You",
"Yongjun Bao",
"Yi Liu",
"Zhongzhi Luan",
"Depei Qian"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2024-02-24T00:00:00 | https://arxiv.org/abs/2402.15678 | https://arxiv.org/pdf/2402.15678v2 | 2402.15678 | null | 3 | 0 | false | null | null | 0.1505 |
41151d071dbc3846638ecebb019e55afca3581d45e84ea734cb43fd8179f5011 | [
"arxiv"
] | Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding | Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality. Current approaches often rely on the model's token likelihoods or other internal information, instruction tuning on ad... | [
"Ailin Deng",
"Zhirui Chen",
"Bryan Hooi"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM"
] | [] | 2024-02-23T00:00:00 | https://arxiv.org/abs/2402.15300 | https://arxiv.org/pdf/2402.15300v2 | 2402.15300 | null | 0 | 0 | true | https://github.com/d-ailin/CLIP-Guided-Decoding | null | 0 |
d1dacab66163f4ef1e9d64269e5ab32b32fd603165eca5fc5693a79fe426aa68 | [
"arxiv",
"semantic_scholar"
] | Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement | Speculative decoding is an inference-acceleration method for large language models (LLMs) where a small language model generates a draft-token sequence which is further verified by the target LLM in parallel. Recent works have advanced this method by establishing a draft-token tree, achieving superior performance over ... | [
"Wonseok Jeon",
"Mukul Gagrani",
"Raghavv Goel",
"Junyoung Park",
"Mingu Lee",
"Christopher Lott"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-02-21T00:00:00 | https://arxiv.org/abs/2402.14160 | https://arxiv.org/pdf/2402.14160v2 | 2402.14160 | 10.48550/arXiv.2402.14160 | 30 | 4 | false | null | arXiv.org | 0.3728 |
bd4d8d65a9924c0431db4e9c75daac6f0b52302d226efbf600978c094845c269 | [
"arxiv",
"semantic_scholar"
] | Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding | Speculative decoding is a widely used method that accelerates the generation process of large language models (LLMs) with no compromise in model performance. It achieves this goal by using an existing smaller model for drafting and then employing the target LLM to verify the draft in a low-cost parallel manner. Under s... | [
"Weilin Zhao",
"Yuxiang Huang",
"Xu Han",
"Wang Xu",
"Chaojun Xiao",
"Xinrong Zhang",
"Yewei Fang",
"Kaihuo Zhang",
"Zhiyuan Liu",
"Maosong Sun"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-21T00:00:00 | https://arxiv.org/abs/2402.13720 | https://arxiv.org/pdf/2402.13720v3 | 2402.13720 | 10.18653/v1/2024.emnlp-main.742 | 27 | 5 | true | https://github.com/thunlp/Ouroboros | Conference on Empirical Methods in Natural Language Processing | 0.3891 |
d47bafaa2e9fc61df6f7e08a7ef3dcf7be4f6986318aa9bab2d15f95e9df8cf9 | [
"arxiv",
"semantic_scholar"
] | Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation | We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference. In contrast to previous approaches that relied on smaller amateur models or analysis of hidden state differences, DCD employs Contrastive Chain-of-... | [
"Phuc Phan",
"Hieu Tran",
"Long Phan"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-21T00:00:00 | https://arxiv.org/abs/2402.14874 | https://arxiv.org/pdf/2402.14874v2 | 2402.14874 | 10.48550/arXiv.2402.14874 | 17 | 0 | false | null | arXiv.org | 0.3138 |
768003fca07b400151cd526e84da9febe2d2beb31fe9044a8be540b68a5ff7c4 | [
"arxiv",
"semantic_scholar"
] | Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding | As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged as a promising direction for speeding up inference, existing methods are limited in their ability to scale to larger speculation budgets, a... | [
"Zhuoming Chen",
"Avner May",
"Ruslan Svirschevski",
"Yuhsun Huang",
"Max Ryabinin",
"Zhihao Jia",
"Beidi Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-19T00:00:00 | https://arxiv.org/abs/2402.12374 | https://arxiv.org/pdf/2402.12374v3 | 2402.12374 | 10.48550/arXiv.2402.12374 | 95 | 11 | false | null | arXiv.org | 0.5396 |
67ef487bb6d1c263e7e18ebf9b01a0015d7139fb1d21d27bee057a62d5181125 | [
"arxiv",
"semantic_scholar"
] | Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding | This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{C}orrect d\textbf{E}coding (SPACE), an innovative approach designed for achieving lossless acceleration of LLMs. By integrating semi-autoreg... | [
"Hanling Yi",
"Feng Lin",
"Hongbin Li",
"Peiyang Ning",
"Xiaotian Yu",
"Rong Xiao"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-19T00:00:00 | https://arxiv.org/abs/2402.11809 | https://arxiv.org/pdf/2402.11809v3 | 2402.11809 | 10.48550/arXiv.2402.11809 | 26 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3578 |
f7bfd7fd916df004684bfad76c94639ef631a600067f0f3b0a59341d5db7264c | [
"arxiv",
"semantic_scholar"
] | Speculative Streaming: Fast LLM Inference without Auxiliary Models | Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of down... | [
"Nikhil Bhendawade",
"Irina Belousova",
"Qichen Fu",
"Henry Mason",
"Mohammad Rastegari",
"Mahyar Najibi"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-16T00:00:00 | https://arxiv.org/abs/2402.11131 | https://arxiv.org/pdf/2402.11131v1 | 2402.11131 | 10.48550/arXiv.2402.11131 | 44 | 2 | false | null | null | 0.4133 |
dff0644adcdb708365e373b19ad2f33f82ce9be491fe7db1c504f6221ba5e466 | [
"arxiv",
"semantic_scholar"
] | Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs | Recently, considerable efforts have been directed towards compressing Large Language Models (LLMs), which showcase groundbreaking capabilities across diverse applications but entail significant deployment costs due to their large sizes. Meanwhile, much less attention has been given to mitigating the costs associated wi... | [
"Yeonhong Park",
"Jake Hyun",
"SangLyul Cho",
"Bonggeun Sim",
"Jae W. Lee"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-02-16T00:00:00 | https://arxiv.org/abs/2402.10517 | https://arxiv.org/pdf/2402.10517v4 | 2402.10517 | 10.48550/arXiv.2402.10517 | 54 | 11 | true | https://github.com/SNU-ARC/any-precision-llm | International Conference on Machine Learning | 0.5396 |
546d766e1e4387ef530e3151eb52bd42a1c79f069847f3f24de216c474c5d889 | [
"arxiv",
"semantic_scholar"
] | GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding | Speculative decoding is a relatively new decoding framework that leverages small and efficient draft models to reduce the latency of LLMs. In this study, we introduce GliDe and CaPE, two low-hassle modifications to vanilla speculative decoding to further improve the decoding speed of a frozen LLM. Specifically, GliDe i... | [
"Cunxiao Du",
"Jing Jiang",
"Xu Yuanchen",
"Jiawei Wu",
"Sicheng Yu",
"Yongqi Li",
"Shenggui Li",
"Kai Xu",
"Liqiang Nie",
"Zhaopeng Tu",
"Yang You"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02082 | https://arxiv.org/pdf/2402.02082v1 | 2402.02082 | 10.48550/arXiv.2402.02082 | 78 | 4 | false | null | International Conference on Machine Learning | 0.4744 |
5afeb01a5e46fba807f459ae1e691d411dd2127eb024b6e49a25152ef04caefe | [
"arxiv",
"semantic_scholar"
] | Break the Sequential Dependency of LLM Inference Using Lookahead Decoding | Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded, resulting in high latency and significant wastes of the parallel processing power of modern accelerators. Existing methods for accelerating LLM decoding often require a draft model (e.g., speculative decoding), which is nontrivial to o... | [
"Yichao Fu",
"Peter Bailis",
"Ion Stoica",
"Hao Zhang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02057 | https://arxiv.org/pdf/2402.02057v1 | 2402.02057 | 10.48550/arXiv.2402.02057 | 315 | 35 | true | https://github.com/hao-ai-lab/LookaheadDecoding | International Conference on Machine Learning | 0.7782 |
4d4e11f693ae1f37f62f30b2b926422543110fa1dd5f341bd0bff2b0728d2534 | [
"arxiv",
"semantic_scholar"
] | Decoding Speculative Decoding | Speculative Decoding is a widely used technique to speed up inference for Large Language Models (LLMs) without sacrificing quality. When performing inference, speculative decoding uses a smaller draft model to generate speculative tokens and then uses the target LLM to verify those draft tokens. The speedup provided by... | [
"Minghao Yan",
"Saurabh Agarwal",
"Shivaram Venkataraman"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-02T00:00:00 | https://arxiv.org/abs/2402.01528 | https://arxiv.org/pdf/2402.01528v4 | 2402.01528 | 10.48550/arXiv.2402.01528 | 39 | 2 | false | null | North American Chapter of the Association for Computational Linguistics | 0.4005 |
dfd9d429a28a236017b071e728afd8feee8abc64132fd4bd0b116dc6c0b17581 | [
"arxiv",
"semantic_scholar"
] | Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads | Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as ... | [
"Tianle Cai",
"Yuhong Li",
"Zhengyang Geng",
"Hongwu Peng",
"Jason D. Lee",
"Deming Chen",
"Tri Dao"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-01-19T00:00:00 | https://arxiv.org/abs/2401.10774 | https://arxiv.org/pdf/2401.10774v3 | 2401.10774 | 10.48550/arXiv.2401.10774 | 753 | 147 | true | https://github.com/FasterDecoding/Medusa | International Conference on Machine Learning | 1 |
3739a5bf6bc169148adb1c108e3f1338f2c82f67c08799ecc6cef741383ee896 | [
"arxiv",
"semantic_scholar"
] | Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding | To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. Unlike auto... | [
"Heming Xia",
"Zhe Yang",
"Qingxiu Dong",
"Peiyi Wang",
"Yongqi Li",
"Tao Ge",
"Tianyu Liu",
"Wenjie Li",
"Zhifang Sui"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-01-15T00:00:00 | https://arxiv.org/abs/2401.07851 | https://arxiv.org/pdf/2401.07851v3 | 2401.07851 | 10.48550/arXiv.2401.07851 | 283 | 50 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.8538 |
3b5e717fc383731e127e738c5842430f05d1b04a76cc8beb48d2e9c575923e4d | [
"arxiv",
"semantic_scholar"
] | Small LLMs Are Weak Tool Learners: A Multi-LLM Agent | Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use demands that LLMs not only understand user queries and generate answers acc... | [
"Weizhou Shen",
"Chenliang Li",
"Hongzhan Chen",
"Ming Yan",
"Xiaojun Quan",
"Hehong Chen",
"Ji Zhang",
"Fei Huang"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-01-14T00:00:00 | https://arxiv.org/abs/2401.07324 | https://arxiv.org/pdf/2401.07324v3 | 2401.07324 | 10.48550/arXiv.2401.07324 | 124 | 6 | true | https://github.com/X-PLUG/Multi-LLM-Agent | Conference on Empirical Methods in Natural Language Processing | 0.5242 |
9289d4a06c883c5d803b3c4a71e69c87d4c36977648eb360689f1b92910b96af | [
"arxiv",
"semantic_scholar"
] | Multi-Candidate Speculative Decoding | Large language models have shown impressive capabilities across a variety of NLP tasks, yet their generating text autoregressively is time-consuming. One way to speed them up is speculative decoding, which generates candidate segments (a sequence of tokens) from a fast draft model that is then verified in parallel by t... | [
"Sen Yang",
"Shujian Huang",
"Xinyu Dai",
"Jiajun Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-01-12T00:00:00 | https://arxiv.org/abs/2401.06706 | https://arxiv.org/pdf/2401.06706v1 | 2401.06706 | 10.48550/arXiv.2401.06706 | 37 | 3 | false | null | Natural Language Processing and Chinese Computing | 0.3949 |
172cdfcaed73f43ec06d4e8d3885d211bc109844b8346580d8b9003583c9170a | [
"arxiv",
"semantic_scholar"
] | Cascade Speculative Drafting for Even Faster LLM Inference | Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then reviews this draft to align with its output, and any acceptance by the target model results in a reduction of the number of the target model ... | [
"Ziyi Chen",
"Xiaocong Yang",
"Jiacheng Lin",
"Chenkai Sun",
"Kevin Chen-Chuan Chang",
"Jie Huang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2023-12-18T00:00:00 | https://arxiv.org/abs/2312.11462 | https://arxiv.org/pdf/2312.11462v5 | 2312.11462 | 10.48550/arXiv.2312.11462 | 96 | 8 | false | null | Neural Information Processing Systems | 0.4967 |
418682902e811217ef1402456ecf3ad7ba498f0194e64f4f76d6592a981b8d61 | [
"arxiv",
"semantic_scholar"
] | Speculative Contrastive Decoding | 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. Inspired by speculative decoding and contrastive decoding, we introduce Speculative Contrastive Decoding~(SCD... | [
"Hongyi Yuan",
"Keming Lu",
"Fei Huang",
"Zheng Yuan",
"Chang Zhou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-11-15T00:00:00 | https://arxiv.org/abs/2311.08981 | https://arxiv.org/pdf/2311.08981v2 | 2311.08981 | 10.48550/arXiv.2311.08981 | 8 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2386 |
2d0641d2877fb244ffa00863f126db4b56cb63a10035476a394e51ee41f0d0e3 | [
"arxiv",
"semantic_scholar"
] | REST: Retrieval-Based Speculative Decoding | We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm designed to speed up language model generation. The key insight driving the development of REST is the observation that the process of text generation often includes certain common phases and patterns. Unlike previous methods that rely on a dra... | [
"Zhenyu He",
"Zexuan Zhong",
"Tianle Cai",
"Jason D. Lee",
"Di He"
] | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | [
"Computer Science"
] | 2023-11-14T00:00:00 | https://arxiv.org/abs/2311.08252 | https://arxiv.org/pdf/2311.08252v2 | 2311.08252 | 10.48550/arXiv.2311.08252 | 162 | 16 | true | https://github.com/FasterDecoding/REST | North American Chapter of the Association for Computational Linguistics | 0.6152 |
005924600d1d561a41e58f127eb55b2a4c77af234b6386ae573eba5a025ee598 | [
"arxiv",
"semantic_scholar"
] | Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO | Inference optimizations are critical for improving user experience and reducing infrastructure costs and power consumption. In this article, we illustrate a form of dynamic execution known as speculative sampling to reduce the overall latency of text generation and compare it with standard autoregressive sampling. This... | [
"Haim Barad",
"Ekaterina Aidova",
"Yury Gorbachev"
] | [
"cs.LG",
"cs.AI",
"cs.PF"
] | [
"Computer Science"
] | 2023-11-08T00:00:00 | https://arxiv.org/abs/2311.04951 | https://arxiv.org/pdf/2311.04951v2 | 2311.04951 | 10.48550/arXiv.2311.04951 | 1 | 0 | true | https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/speculative-sampling | arXiv.org | 0.0753 |
59426672d48f139b9674b376fb424828577342832d3a069035d365dd21f86da9 | [
"arxiv",
"semantic_scholar"
] | The Synergy of Speculative Decoding and Batching in Serving Large Language Models | Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines. However, LLM execution is inherently sequential, since they only produce one token at a time, thus incurring low hardware utilization on modern GPUs. Batching and speculative decoding... | [
"Qidong Su",
"Christina Giannoula",
"Gennady Pekhimenko"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2023-10-28T00:00:00 | https://arxiv.org/abs/2310.18813 | https://arxiv.org/pdf/2310.18813v1 | 2310.18813 | 10.48550/arXiv.2310.18813 | 21 | 2 | false | null | arXiv.org | 0.3356 |
c7a03c3f5d42878235e16256009225ab25138f3e54853397f3fae81b3a993f5b | [
"arxiv",
"semantic_scholar"
] | SpecTr: Fast Speculative Decoding via Optimal Transport | Autoregressive sampling from large language models has led to state-of-the-art results in several natural language tasks. However, autoregressive sampling generates tokens one at a time making it slow, and even prohibitive in certain tasks. One way to speed up sampling is $\textit{speculative decoding}$: use a small mo... | [
"Ziteng Sun",
"Ananda Theertha Suresh",
"Jae Hun Ro",
"Ahmad Beirami",
"Himanshu Jain",
"Felix Yu"
] | [
"cs.LG",
"cs.CL",
"cs.DS",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2023-10-23T00:00:00 | https://arxiv.org/abs/2310.15141 | https://arxiv.org/pdf/2310.15141v2 | 2310.15141 | 10.48550/arXiv.2310.15141 | 151 | 18 | false | null | Neural Information Processing Systems | 0.6394 |
4a5e48eaf98f79409c10cbbc00f3eba9bf1e1fe42857d9571ff0f2454c36c96b | [
"arxiv",
"semantic_scholar"
] | SPEED: Speculative Pipelined Execution for Efficient Decoding | Generative Large Language Models (LLMs) based on the Transformer architecture have recently emerged as a dominant foundation model for a wide range of Natural Language Processing tasks. Nevertheless, their application in real-time scenarios has been highly restricted due to the significant inference latency associated ... | [
"Coleman Hooper",
"Sehoon Kim",
"Hiva Mohammadzadeh",
"Hasan Genc",
"Kurt Keutzer",
"Amir Gholami",
"Sophia Shao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-10-18T00:00:00 | https://arxiv.org/abs/2310.12072 | https://arxiv.org/pdf/2310.12072v2 | 2310.12072 | 10.48550/arXiv.2310.12072 | 52 | 6 | false | null | arXiv.org | 0.4311 |
0a0048d6d75dc0f0af4f2cac013af9142883a3c23daf8f01dd01ea43ca72e28f | [
"arxiv",
"semantic_scholar"
] | DistillSpec: Improving Speculative Decoding via Knowledge Distillation | Speculative decoding (SD) accelerates large language model inference by employing a faster draft model for generating multiple tokens, which are then verified in parallel by the larger target model, resulting in the text generated according to the target model distribution. However, identifying a compact draft model th... | [
"Yongchao Zhou",
"Kaifeng Lyu",
"Ankit Singh Rawat",
"Aditya Krishna Menon",
"Afshin Rostamizadeh",
"Sanjiv Kumar",
"Jean-François Kagy",
"Rishabh Agarwal"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08461 | https://arxiv.org/pdf/2310.08461v2 | 2310.08461 | 10.48550/arXiv.2310.08461 | 159 | 12 | false | null | International Conference on Learning Representations | 0.557 |
8f8b1b6229e18341df51a3b2a856ff108a87eba39bf21ebd3324ce8f0c01549a | [
"arxiv",
"semantic_scholar"
] | Online Speculative Decoding | Speculative decoding is a pivotal technique to accelerate the inference of large language models (LLMs) by employing a smaller draft model to predict the target model's outputs. However, its efficacy can be limited due to the low predictive accuracy of the draft model, particularly when faced with diverse text inputs a... | [
"Xiaoxuan Liu",
"Lanxiang Hu",
"Peter Bailis",
"Alvin Cheung",
"Zhijie Deng",
"Ion Stoica",
"Hao Zhang"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07177 | https://arxiv.org/pdf/2310.07177v4 | 2310.07177 | 10.48550/arXiv.2310.07177 | 112 | 9 | true | https://github.com/LiuXiaoxuanPKU/OSD | International Conference on Machine Learning | 0.5133 |
80046499ae7fe34b7d3ceeeb93e6c84cb79fd7a37e04b7e1c0346ec84371dfe2 | [
"arxiv"
] | Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding | We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Models (LLMs) without the need for an auxiliary model. This approach is characterized by a two-stage process: drafting and verification. The drafting stage generates draft tokens at a slightly lower quality but more quickly,... | [
"Jun Zhang",
"Jue Wang",
"Huan Li",
"Lidan Shou",
"Ke Chen",
"Gang Chen",
"Sharad Mehrotra"
] | [
"cs.CL"
] | [] | 2023-09-15T00:00:00 | https://arxiv.org/abs/2309.08168 | https://arxiv.org/pdf/2309.08168v2 | 2309.08168 | null | 0 | 0 | false | null | null | 0 |
49216a6b94fcca5887cbfa932cb17e60ca294b4a213b49b3e9b1a6269de650e5 | [
"arxiv",
"semantic_scholar"
] | SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills | Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it... | [
"Amey Agrawal",
"Ashish Panwar",
"Jayashree Mohan",
"Nipun Kwatra",
"Bhargav S. Gulavani",
"Ramachandran Ramjee"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2023-08-31T00:00:00 | https://arxiv.org/abs/2308.16369 | https://arxiv.org/pdf/2308.16369v1 | 2308.16369 | 10.48550/arXiv.2308.16369 | 231 | 19 | false | null | arXiv.org | 0.6505 |
8114250c7ba56938a075f69d04d50662d43f8a0a16cc9c40f32b7e0785db9223 | [
"arxiv",
"semantic_scholar"
] | Accelerating LLM Inference with Staged Speculative Decoding | Recent advances with large language models (LLM) illustrate their diverse capabilities. We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios. We address the low arithmetic intensity of small-batch inference by improving upon previous work in speculat... | [
"Benjamin Spector",
"Chris Re"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-08-08T00:00:00 | https://arxiv.org/abs/2308.04623 | https://arxiv.org/pdf/2308.04623v1 | 2308.04623 | 10.48550/arXiv.2308.04623 | 178 | 9 | false | null | arXiv.org | 0.5632 |
6e04394cce44d057e3bcbd2217a25675868387cf0c1bc36a6cd6b8435d406631 | [
"arxiv",
"semantic_scholar"
] | RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit | Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to user requests. This problem can be alleviated by augmenting LLMs with information retrieval (IR) systems (also known as retrieval-augmented L... | [
"Jiongnan Liu",
"Jiajie Jin",
"Zihan Wang",
"Jiehan Cheng",
"Zhicheng Dou",
"Ji-Rong Wen"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2023-06-08T00:00:00 | https://arxiv.org/abs/2306.05212 | https://arxiv.org/pdf/2306.05212v1 | 2306.05212 | 10.48550/arXiv.2306.05212 | 61 | 2 | true | https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM | arXiv.org | 0.4481 |
ae9d74b24d12158c81e74194281d8cce682e379fc765f2defb62ff138cab96a0 | [
"arxiv",
"semantic_scholar"
] | AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration | Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud computing cost and protect users' privacy. However, the astronomical model size and the limited hardware resource pose significant deploymen... | [
"Ji Lin",
"Jiaming Tang",
"Haotian Tang",
"Shang Yang",
"Wei-Ming Chen",
"Wei-Chen Wang",
"Guangxuan Xiao",
"Xingyu Dang",
"Chuang Gan",
"Song Han"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-06-01T00:00:00 | https://arxiv.org/abs/2306.00978 | https://arxiv.org/pdf/2306.00978v6 | 2306.00978 | 10.1145/3714983.3714987 | 1,454 | 217 | true | https://github.com/mit-han-lab/llm-awq | Conference on Machine Learning and Systems | 1 |
3e23452c9fc523b106c8710f61fc345800751af58ed7ce7f0fa2af1b0a718721 | [
"arxiv",
"semantic_scholar"
] | SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification | This paper introduces SpecInfer, a system that accelerates generative large language model (LLM) serving with tree-based speculative inference and verification. The key idea behind SpecInfer is leveraging small speculative models to predict the LLM's outputs; the predictions are organized as a token tree, whose nodes e... | [
"Xupeng Miao",
"Gabriele Oliaro",
"Zhihao Zhang",
"Xinhao Cheng",
"Zeyu Wang",
"Zhengxin Zhang",
"Rae Ying Yee Wong",
"Alan Zhu",
"Lijie Yang",
"Xiaoxiang Shi",
"Chunan Shi",
"Zhuoming Chen",
"Daiyaan Arfeen",
"Reyna Abhyankar",
"Zhihao Jia"
] | [
"cs.CL",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2023-05-16T00:00:00 | https://arxiv.org/abs/2305.09781 | https://arxiv.org/pdf/2305.09781v4 | 2305.09781 | 10.1145/3620666.3651335 | 368 | 55 | true | https://github.com/flexflow/FlexFlow/ | International Conference on Architectural Support for Programming Languages and Operating Systems | 0.8741 |
fb6008aa313ec9941aa1e1a1e593086f2dccf2fcedde4e9dbee3ce4576e8af0b | [
"arxiv",
"semantic_scholar"
] | Accelerating Large Language Model Decoding with Speculative Sampling | We present speculative sampling, an algorithm for accelerating transformer decoding by enabling the generation of multiple tokens from each transformer call. Our algorithm relies on the observation that the latency of parallel scoring of short continuations, generated by a faster but less powerful draft model, is compa... | [
"Charlie Chen",
"Sebastian Borgeaud",
"Geoffrey Irving",
"Jean-Baptiste Lespiau",
"Laurent Sifre",
"John Jumper"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-02-02T00:00:00 | https://arxiv.org/abs/2302.01318 | https://arxiv.org/pdf/2302.01318v1 | 2302.01318 | 10.48550/arXiv.2302.01318 | 908 | 108 | false | null | arXiv.org | 1 |
1adec7fc5b299c3e07eba043ad02b9090d6e8249d04ad85781ab58da756bcca6 | [
"arxiv",
"semantic_scholar"
] | Fast Inference from Transformers via Speculative Decoding | Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model. In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by computing several tokens in parallel. At the heart o... | [
"Yaniv Leviathan",
"Matan Kalman",
"Yossi Matias"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2022-11-30T00:00:00 | https://arxiv.org/abs/2211.17192 | https://arxiv.org/pdf/2211.17192v2 | 2211.17192 | 10.48550/arXiv.2211.17192 | 1,646 | 279 | false | null | International Conference on Machine Learning | 1 |
da28afc96931be423a3c8200370b6484947e29f2a8f0b3fde8a1a79ee9ff0ace | [
"arxiv",
"semantic_scholar"
] | Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation | We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. Speculative Decoding has two innovations: Spec-Drafter -- an independent model specially optimized for efficient and accurate drafting -- and Spe... | [
"Heming Xia",
"Tao Ge",
"Peiyi Wang",
"Si-Qing Chen",
"Furu Wei",
"Zhifang Sui"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-03-30T00:00:00 | https://arxiv.org/abs/2203.16487 | https://arxiv.org/pdf/2203.16487v6 | 2203.16487 | 10.18653/v1/2023.findings-emnlp.257 | 183 | 12 | true | https://github.com/hemingkx/SpecDec | Conference on Empirical Methods in Natural Language Processing | 0.5662 |
e965fbcd433831dc0def0d0120e14b36cb2dd4d0b53cff938308d76ba597b58a | [
"arxiv",
"semantic_scholar"
] | Causal Inference Using Tractable Circuits | The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was reported recently to facilitate model-based supervised learning but it can be interp... | [
"Adnan Darwiche"
] | [
"cs.AI",
"cs.CC",
"cs.LG",
"cs.LO",
"stat.ME"
] | [
"Computer Science",
"Mathematics"
] | 2022-02-07T00:00:00 | https://arxiv.org/abs/2202.02891 | https://arxiv.org/pdf/2202.02891v1 | 2202.02891 | null | 23 | 3 | false | null | arXiv.org | 0.3451 |
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