{"total":76,"rows":[{"id":"2210.09461","title":"Token Merging: Your ViT But Faster","created":"2022-10-17","cited_by_count":1021,"area":"inference"},{"id":"2310.06839","title":"LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression","created":"2023-10-10","cited_by_count":453,"area":"inference"},{"id":"2207.07061","title":"Confident Adaptive Language Modeling","created":"2022-07-14","cited_by_count":310,"area":"inference"},{"id":"2310.05736","title":"LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models","created":"2023-10-09","cited_by_count":287,"area":null},{"id":"2107.00910","title":"Learned Token Pruning for Transformers","created":"2021-07-02","cited_by_count":139,"area":"inference"},{"id":"2312.01026","title":"Token Fusion: Bridging the Gap between Token Pruning and Token Merging","created":"2023-12-02","cited_by_count":125,"area":"inference"},{"id":"2403.09636","title":"Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference","created":"2024-03-14","cited_by_count":116,"area":null},{"id":"2401.10034","title":"Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap","created":"2024-01-18","cited_by_count":90,"area":null},{"id":"2302.14017","title":"Full Stack Optimization of Transformer Inference: a Survey","created":"2023-02-27","cited_by_count":80,"area":null},{"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":"2111.05193","title":"A Survey on Green Deep Learning","created":"2021-11-08","cited_by_count":52,"area":null},{"id":"2505.18227","title":"Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality","created":"2025-05-23","cited_by_count":50,"area":"inference"},{"id":"2410.08368","title":"ElasticTok: Adaptive Tokenization for Image and Video","created":"2024-10-10","cited_by_count":46,"area":null},{"id":"2202.07105","title":"A Survey on Model Compression and Acceleration for Pretrained Language Models","created":"2022-02-15","cited_by_count":45,"area":null},{"id":"2210.05528","title":"Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems","created":"2022-10-11","cited_by_count":44,"area":null},{"id":"2305.17455","title":"CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers","created":"2023-05-27","cited_by_count":44,"area":"mllm"},{"id":"2412.03248","title":"AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning","created":"2024-12-04","cited_by_count":43,"area":"mllm"},{"id":"2105.11618","title":"TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference","created":"2021-05-25","cited_by_count":40,"area":"inference"},{"id":"2403.12844","title":"MELTing point: Mobile Evaluation of Language Transformers","created":"2024-03-19","cited_by_count":36,"area":null},{"id":"2401.07872","title":"The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey","created":"2024-01-15","cited_by_count":33,"area":null},{"id":"2204.11786","title":"Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications","created":"2022-04-25","cited_by_count":32,"area":null},{"id":"2505.19147","title":"Shifting AI Efficiency From Model-Centric to Data-Centric Compression","created":"2025-05-25","cited_by_count":29,"area":"mllm"},{"id":"2503.01776","title":"Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation","created":"2025-03-03","cited_by_count":26,"area":null},{"id":"2305.12129","title":"Lifting the Curse of Capacity Gap in Distilling Language Models","created":"2023-05-20","cited_by_count":25,"area":"moe"},{"id":"2202.07101","title":"A Survey on Dynamic Neural Networks for Natural Language Processing","created":"2022-02-15","cited_by_count":24,"area":null},{"id":"2205.07324","title":"Transkimmer: Transformer Learns to Layer-wise Skim","created":"2022-05-15","cited_by_count":24,"area":null},{"id":"2407.14439","title":"Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding","created":"2024-07-19","cited_by_count":22,"area":"mllm"},{"id":"2203.08991","title":"AdapLeR: Speeding up Inference by Adaptive Length Reduction","created":"2022-03-16","cited_by_count":19,"area":null},{"id":"2311.09758","title":"OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking","created":"2023-11-16","cited_by_count":19,"area":null},{"id":"2112.08560","title":"Block-Skim: Efficient Question Answering for Transformer","created":"2021-12-16","cited_by_count":18,"area":null},{"id":"2407.08892","title":"Characterizing Prompt Compression Methods for Long Context Inference","created":"2024-07-11","cited_by_count":18,"area":"inference"},{"id":"2112.06540","title":"A Study on Token Pruning for ColBERT","created":"2021-12-13","cited_by_count":15,"area":"inference"},{"id":"2306.16009","title":"Accelerating Transducers through Adjacent Token Merging","created":"2023-06-28","cited_by_count":15,"area":"inference"},{"id":"2203.14380","title":"Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection","created":"2022-03-27","cited_by_count":13,"area":null},{"id":"2212.03597","title":"DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing","created":"2022-12-07","cited_by_count":13,"area":null},{"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":"2211.11586","title":"Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers","created":"2022-11-17","cited_by_count":11,"area":null},{"id":"2412.06263","title":"iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models","created":"2024-12-09","cited_by_count":11,"area":"mllm"},{"id":"2211.12707","title":"Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?","created":"2022-11-23","cited_by_count":10,"area":null},{"id":"2310.13682","title":"Optimizing Retrieval-augmented Reader Models via Token Elimination","created":"2023-10-20","cited_by_count":10,"area":"rag"},{"id":"2311.02851","title":"Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding","created":"2023-11-06","cited_by_count":10,"area":null},{"id":"2502.16782","title":"CipherPrune: Efficient and Scalable Private Transformer Inference","created":"2025-02-24","cited_by_count":10,"area":null},{"id":"2305.17223","title":"Do We Really Need a Large Number of Visual Prompts?","created":"2023-05-26","cited_by_count":9,"area":null},{"id":"2410.11786","title":"Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability","created":"2024-10-15","cited_by_count":9,"area":"inference"},{"id":"2512.09616","title":"Rethinking Chain-of-Thought Reasoning for Videos","created":"2025-12-10","cited_by_count":9,"area":"lr"},{"id":"2203.00748","title":"E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models","created":"2022-03-01","cited_by_count":8,"area":null},{"id":"2507.04636","title":"Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework","created":"2025-07-07","cited_by_count":8,"area":null},{"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":"2406.01283","title":"Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification","created":"2024-06-03","cited_by_count":7,"area":"inference"},{"id":"2509.00419","title":"LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression","created":"2025-08-30","cited_by_count":7,"area":"mllm"},{"id":"2306.14393","title":"Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference","created":"2023-06-26","cited_by_count":6,"area":"inference"},{"id":"2505.12731","title":"Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps","created":"2025-05-19","cited_by_count":6,"area":"rag"},{"id":"2412.12496","title":"Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training","created":"2024-12-17","cited_by_count":5,"area":"ssm"},{"id":"2504.04514","title":"Saliency-driven Dynamic Token Pruning for Large Language Models","created":"2025-04-06","cited_by_count":5,"area":"inference"},{"id":"2111.09645","title":"Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length","created":"2021-11-18","cited_by_count":4,"area":null},{"id":"2412.13437","title":"Deploying Foundation Model Powered Agent Services: A Survey","created":"2024-12-18","cited_by_count":4,"area":"agents"},{"id":"2204.03479","title":"Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention","created":"2022-03-20","cited_by_count":2,"area":null},{"id":"2303.00720","title":"Cross-Modal Entity Matching for Visually Rich Documents","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2305.12458","title":"Infor-Coef: Information Bottleneck-based Dynamic Token Downsampling for Compact and Efficient language model","created":"2023-05-21","cited_by_count":2,"area":null},{"id":"2502.08363","title":"Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding","created":"2025-02-12","cited_by_count":2,"area":null},{"id":"2208.00483","title":"Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers","created":"2022-07-31","cited_by_count":1,"area":null},{"id":"2210.17114","title":"QuaLA-MiniLM: a Quantized Length Adaptive MiniLM","created":"2022-10-31","cited_by_count":1,"area":null},{"id":"2310.15724","title":"Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules","created":"2023-10-24","cited_by_count":1,"area":null},{"id":"2312.09494","title":"No-Skim: Towards Efficiency Robustness Evaluation on Skimming-based Language Models","created":"2023-12-15","cited_by_count":1,"area":null},{"id":"2407.19410","title":"AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering","created":"2024-07-28","cited_by_count":1,"area":"inference"},{"id":"2408.11799","title":"Practical token pruning for foundation models in few-shot conversational virtual assistant systems","created":"2024-08-21","cited_by_count":1,"area":"inference"},{"id":"2410.14888","title":"Self-Satisfied: An end-to-end framework for SAT generation and prediction","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2507.14042","title":"Training-free Token Reduction for Vision Mamba","created":"2025-07-18","cited_by_count":1,"area":"ssm"},{"id":"2605.22476","title":"Structured-Sparse Attention for Entity Tracking with Subquadratic Sequence Complexity","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2208.02169","title":"SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences","created":"2022-08-03","cited_by_count":0,"area":null},{"id":"2310.14837","title":"Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders","created":"2023-10-23","cited_by_count":0,"area":null},{"id":"2311.07204","title":"On Elastic Language Models","created":"2023-11-13","cited_by_count":0,"area":null},{"id":"2411.18092","title":"Training Noise Token Pruning","created":"2024-11-27","cited_by_count":0,"area":"inference"},{"id":"2412.16491","title":"ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2509.24074","title":"ResFormer: All-Time Reservoir Memory for Long Sequence Classification","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2608.20632","title":"Sparse Token Routing in Efficient Transformers","created":"2026-08-21","cited_by_count":0,"area":"inference"}],"next_cursor":null}