Papers with mixture-of-experts
Table-based Fact Verification with Self-adaptive Mixture of Experts (2022.findings-acl)
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| Challenge: | Existing research focuses on table-based fact verification, but a new trend is extending the scope to structured evidence. |
| Approach: | They propose a mixture-of-experts neural network to recognize and execute different types of reasoning . they use a management module to decide the contribution of each expert network to the verification result . |
| Outcome: | The proposed method achieves 85.1% accuracy on the TabFact dataset, comparable with the previous state-of-the-art models. |
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated considerable proficiency in general natural language processing tasks. |
| Approach: | They propose a parameter-efficient sparsity crafting method which crafts dense models into sparse models using the mixture-of-experts architecture. |
| Outcome: | The proposed method significantly reduces computational costs and GPU memory requirements, while maintaining the quality of approximation in function space. |
ConstitutionalExperts: Training a Mixture of Principle-based Prompts (2024.acl-short)
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| Challenge: | Large language models (LLMs) are capable at a variety of tasks given the right prompt, but writing one remains a difficult and tedious process. |
| Approach: | They propose a method for learning a prompt consisting of constitutional principles, given a training dataset. |
| Outcome: | The proposed method outperforms other prompt optimization techniques by 10.9% and improves all techniques, suggesting its broad applicability. |
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)
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| Challenge: | Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system. |
| Approach: | They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism. |
| Outcome: | The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism. |
A Lightweight Mixture-of-Experts Neural Machine Translation Model with Stage-wise Training Strategy (2024.findings-naacl)
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| Challenge: | Using mixture-of-experts (MoE) to deal with language heterogeneity is a challenge in neural machine translation (NMT). |
| Approach: | They propose a lightweight MoE-based NMT model that is trained via an elaborate stage-wise training strategy. |
| Outcome: | The proposed model achieves stable improvements in translation tasks by introducing fewer extra parameters compared to baseline models. |
Adaptive Gating in Mixture-of-Experts based Language Models (2023.emnlp-main)
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| Challenge: | Existing models employ a fixed gating network where each token is computed by the same number of experts. |
| Approach: | They propose a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution. |
| Outcome: | The proposed model reduces training time and inference quality while maintaining sparsity while maintaining inference accuracy. |
H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs (2026.eacl-long)
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| Challenge: | Existing approaches to align pre-trained LLMs with instructions for one property are difficult to fine-tune. |
| Approach: | They propose a mixture-of-experts-based fusion mechanism that models alignment as a controllable drift within the subspace, guided by a drift-regularization loss to balance competing alignment dimensions. |
| Outcome: | Extensive evaluations of three benchmark datasets show that H3Fusion outperforms each individually aligned model by 11.37% and provides stronger robustness compared to the state-of-the-art LLM ensemble approaches by 13.77% and model-merging approaches by 6.18 %. |
Neural Transduction for Multilingual Lexical Translation (2020.coling-main)
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| Challenge: | a method for completing multilingual translation dictionaries is proposed . a 27% relative improvement in whole-word accuracy is achieved when multilingual data is unavailable . |
| Approach: | They propose a method for completing multilingual translation dictionaries using multilingual inputs and multilingual decoding objective. |
| Outcome: | The proposed method can synthesize new word forms in multilingual translation dictionaries . it can perform in settings where correct translations have not been observed in text . |
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning (2025.findings-emnlp)
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| Challenge: | Currently, mixture-of-experts (MoE) is underutilized on heterogeneous datasets, ignoring the fact that experts may learn similar knowledge. |
| Approach: | They propose a method to promote modularization and specialization in MoE by specializing functionalities into different experts and sparsely activating them appropriately. |
| Outcome: | The proposed method improves the capacity and specialization of mixture-of-experts (MoE) by sampling from activated and inactivated experts in top-k routing. |
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning (2024.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained language models to various tasks efficiently. |
| Approach: | They propose a parameter-efficient fine-tuning framework that captures transferable knowledge as a weighted combination of adapters trained on source tasks. |
| Outcome: | The proposed method yields stable improvements over full fine-tuning and knowledge transferring methods on a broad range of tasks over 17 datasets. |
Improving Grammatical Error Correction with Multimodal Feature Integration (2023.findings-acl)
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| Challenge: | Experimental results show that multimodal GEC models improve over strong baselines and achieve a new state-of-the-art result on the Falko-MERLIN test set. |
| Approach: | They propose a framework that integrates both speech and text features to enhance GEC by generating audio from text using advanced text-to-speech models. |
| Outcome: | The proposed framework improves on CoNLL14, BEA19 English, and Falko-MERLIN German datasets. |
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)
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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining. |
| Approach: | They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead. |
| Outcome: | The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes. |
Maximum Score Routing For Mixture-of-Experts (2025.findings-acl)
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| Challenge: | Traditional mixture-of-experts (MoE) networks impose an expert capacity constraint to ensure GPU-friendly computation. |
| Approach: | They propose a routing paradigm that dynamically allocates input tokens to top-k experts through differentiable sparse transformations, enabling scalable model capacity while preserving computational efficiency. |
| Outcome: | The proposed model achieves lower training losses and higher evaluation scores at equivalent FLOPs compared to constrained and unconstrained baselines. |
Masks Can be Learned as an Alternative to Experts (2025.acl-long)
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| Challenge: | a recent study shows that sparse activation techniques can reduce inference performance without sacrificing performance. |
| Approach: | They propose to sparsify a pre-trained dense large language model into a mixture-of-experts architecture for faster inference. |
| Outcome: | The proposed approach is more efficient than one-shot sparsification techniques . it achieves 97% performance retention on downstream tasks with only 50% of parameters activated . |
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
| Approach: | They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism. |
| Outcome: | Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets. |
V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment (2026.findings-acl)
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| Challenge: | Current methods for steering large language models rely on prompt engineering or reasoning-time guidance. |
| Approach: | They propose a value-controllable pluralistic alignment framework enhanced with conditioned gating that dynamically directs the flow among multiple experts based on an input value or moral vector. |
| Outcome: | The proposed method outperforms prompt-based steering and multi-task PEFT benchmarks on two 8-billion-parameter backbones. |
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models (2026.findings-acl)
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Mingzi Cao, Xingwei Tan, Mahmud Elahi Akhter, Marco Valentino, Maria Liakata, Xi Wang, Nikolaos Aletras
| Challenge: | Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. |
| Approach: | They propose to use a dataset of symbolic tasks to induce deductive skills into large language models (LLMs) they then use FT to fine-tune models to improve OOD generalization . |
| Outcome: | The proposed approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic out-of-domain tasks. |
Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs’ Hallucinations (2026.acl-long)
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| Challenge: | Existing methods to mitigate hallucinations include prompt engineering and model optimization, but lack domain generalization and potential errors in fine-tuning data may exacerbate the hallucism. |
| Approach: | They propose an expert-aware adaptive contrast decoding that uses expert differences in MoE’s higher layers to mitigate hallucinations on QA tasks. |
| Outcome: | The proposed method outperforms baseline models on four datasets Large language models (LLMs) show strong performance but suffer from hallucinations, limiting their application. |