LEMoE (Mixture-of-experts routing): superseded — cited as a baseline and beaten by newer methods. 3 paper(s) critique it, 1 beat it on benchmarks — #38 of 1370 most-superseded. Sub-problem: cluster led by HydraLoRA. Newer alternatives in the same sub-problem include PARAMΔ Integration into Upcycled MoE, MEMIT-like framework for MoE, HELLoRA, SDG-MoE, Marco-MoE.

Superseded baseline#38 of 1,370 most-superseded

LEMoE

LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models

Mixture-of-experts routing · first seen Jun 28, 2024

superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 1 beat it on benchmarks

What papers say

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

“However, LEMoE functions as a parameter-preserving framework that attaches external modules to a frozen backbone (typically dense). It addresses routing consistency within the added adaptor rather than the routing distribution shift of the base model itself.”
— MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs
“Additionally, since the routers in these methods are fine-tuned, even if previous experts are frozen, there can still be modifications to past knowledge.”
— LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models
“LEMoE is based on MoE, but its greedy routing harms old experts' influence when integrating new ones.”
— Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts

Beaten on benchmarks

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

LiveEdit beats LEMoE · Average [LLaVA (7B), 1 edit, E-VQA]
95.36 vs 94.52
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
LiveEdit beats LEMoE · Average [LLaVA (7B), 1 edit, VLKEB]
97.08 vs 90.90
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
LiveEdit beats LEMoE · Average [LLaVA (7B), 10 edits, E-VQA]
94.68 vs 78.66
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
LiveEdit beats LEMoE · Average [LLaVA (7B), 10 edits, VLKEB]
96.26 vs 79.43
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.