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Mixture-of-experts routing

LoRAMoE

LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Superseded baseline#11 of 1,370 most-superseded · first seen Dec 15, 2023

Superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 5 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites LoRAMoE as a baseline.

Despite this promise, our empirical results show that MoE Transformers continue to suffer substantial catastrophic forgetting, even when expert utilization is sparse and well-balanced.
— Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
“While effective, this approach introduces three inefficiencies: (i) parameter explosion—with E experts, methods like MoLA or LoRAMoE replicate adapters, causing parameters to grow with E.”
— LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
“These methods were designed for dense backbones.”
— HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models

Beaten on benchmarks

Head-to-head results where a newer method reports beating LoRAMoE. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.