{"total":54,"rows":[{"id":"2207.07061","title":"Confident Adaptive Language Modeling","created":"2022-07-14","cited_by_count":310,"area":"inference"},{"id":"2404.14294","title":"A Survey on Efficient Inference for Large Language Models","created":"2024-04-22","cited_by_count":205,"area":"inference"},{"id":"2402.16363","title":"LLM Inference Unveiled: Survey and Roofline Model Insights","created":"2024-02-26","cited_by_count":152,"area":"inference"},{"id":"2103.04505","title":"Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges","created":"2021-03-08","cited_by_count":115,"area":"inference"},{"id":"2203.07259","title":"The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models","created":"2022-03-14","cited_by_count":115,"area":"inference"},{"id":"2312.15234","title":"Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems","created":"2023-12-23","cited_by_count":102,"area":"inference"},{"id":"2307.02628","title":"SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference","created":"2023-07-05","cited_by_count":89,"area":"inference"},{"id":"2106.05022","title":"Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions","created":"2021-06-09","cited_by_count":75,"area":"inference"},{"id":"2312.04916","title":"EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism","created":"2023-12-08","cited_by_count":69,"area":"inference"},{"id":"2305.17328","title":"Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers","created":"2023-05-27","cited_by_count":62,"area":"inference"},{"id":"2105.11618","title":"TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference","created":"2021-05-25","cited_by_count":40,"area":"inference"},{"id":"2203.01670","title":"A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation","created":"2022-03-03","cited_by_count":32,"area":"inference"},{"id":"2308.00255","title":"LGViT: Dynamic Early Exiting for Accelerating Vision Transformer","created":"2023-08-01","cited_by_count":31,"area":"inference"},{"id":"2105.13878","title":"Accelerating BERT Inference for Sequence Labeling via Early-Exit","created":"2021-05-28","cited_by_count":29,"area":"inference"},{"id":"2404.03865","title":"FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping","created":"2024-04-05","cited_by_count":23,"area":"inference"},{"id":"2111.00230","title":"Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning","created":"2021-10-30","cited_by_count":16,"area":"inference"},{"id":"2312.05385","title":"Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving","created":"2023-12-08","cited_by_count":15,"area":"inference"},{"id":"2107.00175","title":"Elbert: Fast Albert with Confidence-Window Based Early Exit","created":"2021-07-01","cited_by_count":14,"area":"inference"},{"id":"2305.12228","title":"Dynamic Transformers Provide a False Sense of Efficiency","created":"2023-05-20","cited_by_count":14,"area":"inference"},{"id":"2204.06328","title":"HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition","created":"2022-04-13","cited_by_count":13,"area":"inference"},{"id":"2312.11882","title":"ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference","created":"2023-12-19","cited_by_count":13,"area":"inference"},{"id":"2402.00518","title":"EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models","created":"2024-02-01","cited_by_count":13,"area":"inference"},{"id":"2405.15039","title":"CEEBERT: Cross-Domain Inference in Early Exit BERT","created":"2024-05-23","cited_by_count":12,"area":"inference"},{"id":"2303.09266","title":"SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference","created":"2023-03-16","cited_by_count":9,"area":"inference"},{"id":"2211.00495","title":"Efficient Graph Neural Network Inference at Large Scale","created":"2022-11-01","cited_by_count":7,"area":"inference"},{"id":"2311.08623","title":"DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models","created":"2023-11-15","cited_by_count":7,"area":"inference"},{"id":"2403.14919","title":"Hierarchical Skip Decoding for Efficient Autoregressive Text Generation","created":"2024-03-22","cited_by_count":7,"area":"inference"},{"id":"2204.03276","title":"PALBERT: Teaching ALBERT to Ponder","created":"2022-04-07","cited_by_count":6,"area":"inference"},{"id":"2209.13443","title":"Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs","created":"2022-09-27","cited_by_count":6,"area":"inference"},{"id":"2502.00745","title":"BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts","created":"2025-02-02","cited_by_count":6,"area":"inference"},{"id":"2306.02307","title":"Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings","created":"2023-06-04","cited_by_count":5,"area":"inference"},{"id":"2405.13019","title":"A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models","created":"2024-05-15","cited_by_count":5,"area":"inference"},{"id":"2212.01562","title":"Understanding the Robustness of Multi-Exit Models under Common Corruptions","created":"2022-12-03","cited_by_count":4,"area":"inference"},{"id":"2403.17726","title":"Tiny Models are the Computational Saver for Large Models","created":"2024-03-26","cited_by_count":4,"area":"inference"},{"id":"2410.20022","title":"Dynamic layer selection in decoder-only transformers","created":"2024-10-26","cited_by_count":4,"area":"inference"},{"id":"2309.09546","title":"Training dynamic models using early exits for automatic speech recognition on resource-constrained devices","created":"2023-09-18","cited_by_count":3,"area":"inference"},{"id":"2402.05948","title":"DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks","created":"2024-02-03","cited_by_count":3,"area":"inference"},{"id":"2410.04433","title":"CAPEEN: Image Captioning with Early Exits and Knowledge Distillation","created":"2024-10-06","cited_by_count":3,"area":"inference"},{"id":"2210.15523","title":"COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models","created":"2022-10-27","cited_by_count":2,"area":"inference"},{"id":"2506.17249","title":"Improving Prediction Certainty Estimation for Reliable Early Exiting via Null Space Projection","created":"2025-06-08","cited_by_count":2,"area":"inference"},{"id":"2605.16786","title":"Lever: Speculative LLM Inference on Smartphones","created":"2026-05-16","cited_by_count":2,"area":"inference"},{"id":"2412.13236","title":"COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism","created":"2024-12-17","cited_by_count":1,"area":"inference"},{"id":"2407.21082","title":"Accelerating Large Language Model Inference with Self-Supervised Early Exits","created":"2024-07-30","cited_by_count":0,"area":"inference"},{"id":"2510.12856","title":"Efficient Adaptive Transformer: An Empirical Study and Reproducible Framework","created":"2025-10-14","cited_by_count":0,"area":"inference"},{"id":"2603.21365","title":"TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference","created":"2026-03-22","cited_by_count":0,"area":"inference"},{"id":"2604.15764","title":"When Do Early-Exit Networks Generalize? A PAC-Bayesian Theory of Adaptive Depth","created":"2026-04-17","cited_by_count":0,"area":"inference"},{"id":"2604.18592","title":"Two-dimensional early exit optimisation of LLM inference","created":"2026-03-27","cited_by_count":0,"area":"inference"},{"id":"2605.05358","title":"Balancing Stability and Plasticity in Sequentially Trained Early-Exiting Neural Networks","created":"2026-05-06","cited_by_count":0,"area":"inference"},{"id":"2608.08809","title":"Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers","created":"2026-08-09","cited_by_count":0,"area":"inference"},{"id":"2609.06320","title":"FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation","created":"2026-09-06","cited_by_count":0,"area":"inference"},{"id":"2609.08189","title":"Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference","created":"2026-09-08","cited_by_count":0,"area":"inference"},{"id":"2609.17008","title":"FlexEE: Self-Speculative and KV-Compatible Early Exiting for Offloading-Aware LLM Inference","created":"2026-09-15","cited_by_count":0,"area":"inference"},{"id":"2609.26100","title":"TSS: Target-Side Sparsification for Speculative Decoding in Domain-Specific Large Language Models","created":"2026-08-15","cited_by_count":0,"area":"inference"},{"id":"2609.32898","title":"When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection","created":"2026-09-26","cited_by_count":0,"area":"inference"}],"next_cursor":null}