{"total":20,"rows":[{"id":"2407.06204","title":"A Survey on Mixture of Experts in Large Language Models","created":"2024-06-26","cited_by_count":237,"area":"moe"},{"id":"2402.01739","title":"OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models","created":"2024-01-29","cited_by_count":187,"area":"moe"},{"id":"2305.13230","title":"To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis","created":"2023-05-22","cited_by_count":92,"area":"moe"},{"id":"2206.00277","title":"Task-Specific Expert Pruning for Sparse Mixture-of-Experts","created":"2022-06-01","cited_by_count":72,"area":"moe"},{"id":"2412.14219","title":"A Survey on Inference Optimization Techniques for Mixture of Experts Models","created":"2024-12-18","cited_by_count":47,"area":"moe"},{"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":"2310.07188","title":"Adaptive Gating in Mixture-of-Experts based Language Models","created":"2023-10-11","cited_by_count":16,"area":"moe"},{"id":"2507.11181","title":"Mixture of Experts in Large Language Models","created":"2025-07-15","cited_by_count":15,"area":"moe"},{"id":"2207.09094","title":"MoEC: Mixture of Expert Clusters","created":"2022-07-19","cited_by_count":14,"area":"moe"},{"id":"2308.06093","title":"Experts Weights Averaging: A New General Training Scheme for Vision Transformers","created":"2023-08-11","cited_by_count":12,"area":"moe"},{"id":"2503.06881","title":"ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration","created":"2025-03-10","cited_by_count":12,"area":"moe"},{"id":"2404.15247","title":"XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts","created":"2024-04-23","cited_by_count":7,"area":"moe"},{"id":"2502.12947","title":"Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models","created":"2025-02-18","cited_by_count":6,"area":"moe"},{"id":"2504.01337","title":"Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design","created":"2025-04-02","cited_by_count":6,"area":"moe"},{"id":"2312.16610","title":"Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation","created":"2023-12-27","cited_by_count":5,"area":"moe"},{"id":"2411.10003","title":"Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models","created":"2024-11-15","cited_by_count":4,"area":"moe"},{"id":"2606.09886","title":"SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs","created":"2026-06-03","cited_by_count":3,"area":"moe"},{"id":"2608.23806","title":"Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings","created":"2026-08-24","cited_by_count":0,"area":"moe"},{"id":"2609.00575","title":"Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs","created":"2026-09-01","cited_by_count":0,"area":"moe"},{"id":"2609.18842","title":"Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data","created":"2026-09-16","cited_by_count":0,"area":"moe"}],"next_cursor":null}