Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Free
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Ziyue, Zhou, Tianyi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models
by: Zheng, Chen, et al.
Published: (2025)
by: Zheng, Chen, et al.
Published: (2025)
Geometry-Preserving Aggregation for Mixture-of-Experts Embedding Models
by: Kachuee, Sajjad, et al.
Published: (2026)
by: Kachuee, Sajjad, et al.
Published: (2026)
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024)
by: Zhang, Lingzheng, et al.
Published: (2024)
Rethinking LLM Ensembling from the Perspective of Mixture Models
by: Fu, Jiale, et al.
Published: (2026)
by: Fu, Jiale, et al.
Published: (2026)
Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)
by: Liu, Yilun, et al.
Published: (2026)
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
by: Wang, Zihan, et al.
Published: (2025)
by: Wang, Zihan, et al.
Published: (2025)
It Takes Two: Your GRPO Is Secretly DPO
by: Wu, Yihong, et al.
Published: (2025)
by: Wu, Yihong, et al.
Published: (2025)
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
by: Ou, Jingyang, et al.
Published: (2024)
by: Ou, Jingyang, et al.
Published: (2024)
Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs
by: Li, Zhongyang, et al.
Published: (2025)
by: Li, Zhongyang, et al.
Published: (2025)
Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models
by: Romero, Miguel, et al.
Published: (2025)
by: Romero, Miguel, et al.
Published: (2025)
Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
by: Wang, Lean, et al.
Published: (2024)
by: Wang, Lean, et al.
Published: (2024)
Maximum Score Routing For Mixture-of-Experts
by: Dong, Bowen, et al.
Published: (2025)
by: Dong, Bowen, et al.
Published: (2025)
Mixture of Lookup Experts
by: Jie, Shibo, et al.
Published: (2025)
by: Jie, Shibo, et al.
Published: (2025)
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning
by: Feng, Jinyuan, et al.
Published: (2025)
by: Feng, Jinyuan, et al.
Published: (2025)
Your Transformer is Secretly Linear
by: Razzhigaev, Anton, et al.
Published: (2024)
by: Razzhigaev, Anton, et al.
Published: (2024)
R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
by: Li, Zhongyang, et al.
Published: (2025)
by: Li, Zhongyang, et al.
Published: (2025)
LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation
by: Zhang, Xuan, et al.
Published: (2024)
by: Zhang, Xuan, et al.
Published: (2024)
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models
by: Belenki, Lior, et al.
Published: (2025)
by: Belenki, Lior, et al.
Published: (2025)
Mixture Compressor for Mixture-of-Experts LLMs Gains More
by: Huang, Wei, et al.
Published: (2024)
by: Huang, Wei, et al.
Published: (2024)
MoIN: Mixture of Introvert Experts to Upcycle an LLM
by: Tejankar, Ajinkya, et al.
Published: (2024)
by: Tejankar, Ajinkya, et al.
Published: (2024)
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)
by: Lu, Xudong, et al.
Published: (2024)
Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings
by: Hallee, Logan, et al.
Published: (2024)
by: Hallee, Logan, et al.
Published: (2024)
HMoE: Heterogeneous Mixture of Experts for Language Modeling
by: Wang, An, et al.
Published: (2024)
by: Wang, An, et al.
Published: (2024)
Bayesian Mixture of Experts For Large Language Models
by: Dialameh, Maryam, et al.
Published: (2025)
by: Dialameh, Maryam, et al.
Published: (2025)
How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have)
by: Miyashita, Hisashi
Published: (2026)
by: Miyashita, Hisashi
Published: (2026)
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
by: He, Haoze, et al.
Published: (2026)
by: He, Haoze, et al.
Published: (2026)
When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models
by: Yoon, Youngsik, et al.
Published: (2026)
by: Yoon, Youngsik, et al.
Published: (2026)
A Survey on Mixture of Experts in Large Language Models
by: Cai, Weilin, et al.
Published: (2024)
by: Cai, Weilin, et al.
Published: (2024)
Scaling Laws for Upcycling Mixture-of-Experts Language Models
by: Liew, Seng Pei, et al.
Published: (2025)
by: Liew, Seng Pei, et al.
Published: (2025)
Your Absorbing Discrete Diffusion Secretly Models the Bayesian Posterior
by: Doyle, Cooper
Published: (2025)
by: Doyle, Cooper
Published: (2025)
Slicing and Dicing: Configuring Optimal Mixtures of Experts
by: Li, Margaret, et al.
Published: (2026)
by: Li, Margaret, et al.
Published: (2026)
Binary-Integer-Programming Based Algorithm for Expert Load Balancing in Mixture-of-Experts Models
by: Sun, Yuan
Published: (2025)
by: Sun, Yuan
Published: (2025)
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
by: Lee, Tony, et al.
Published: (2026)
by: Lee, Tony, et al.
Published: (2026)
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
by: Zhao, Zhongyu, et al.
Published: (2024)
by: Zhao, Zhongyu, et al.
Published: (2024)
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection
by: Niu, Tianyi, et al.
Published: (2026)
by: Niu, Tianyi, et al.
Published: (2026)
Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
by: Lv, Ang, et al.
Published: (2025)
by: Lv, Ang, et al.
Published: (2025)
$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)
by: Takashiro, Shota, et al.
Published: (2026)
A Closer Look into Mixture-of-Experts in Large Language Models
by: Lo, Ka Man, et al.
Published: (2024)
by: Lo, Ka Man, et al.
Published: (2024)
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
by: Wang, Ruochen, et al.
Published: (2024)
by: Wang, Ruochen, et al.
Published: (2024)
Similar Items
-
GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models
by: Zheng, Chen, et al.
Published: (2025) -
Geometry-Preserving Aggregation for Mixture-of-Experts Embedding Models
by: Kachuee, Sajjad, et al.
Published: (2026) -
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024) -
Rethinking LLM Ensembling from the Perspective of Mixture Models
by: Fu, Jiale, et al.
Published: (2026) -
Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)