Challenge: Large language models (LLMs) can handle tasks such as text generation, translation and summarization with remarkable proficiency.
Approach: They propose a framework that enables scalable PEFT operations to be performed collaboratively between the cloud and user devices.
Outcome: The proposed framework reduces computation and communication workload over user devices while achieving superior accuracy and privacy protection.

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UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)

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Challenge: Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance.
Approach: They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance.
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PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models (2026.eacl-demo)

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Challenge: Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs).
Approach: They propose a framework for efficient fine-tuning Large Language Models (LLMs) they aim to train only a small percentage of the full model's parameters .
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PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark (2026.eacl-long)

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Challenge: Parameter-Efficient Fine-Tuning (PEFT) methods reduce the number of trainable parameters while maintaining strong downstream performance.
Approach: They propose a unified benchmark for evaluating diverse PEFT methods on autoregressive LLMs.
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Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)

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Challenge: Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures.
Approach: They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity.
Outcome: Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost.
VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)

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Challenge: Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks .
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From Bottom to Top: Extending the Potential of Parameter Efficient Fine-Tuning (2024.emnlp-main)

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Challenge: Existing methods to fine-tune large language models primarily focus on the interaction between different layers, ignoring the fact that different layers store different information.
Approach: They propose a Parameter Efficient Fine-Tuning method which freeze pre-trained parameters and fine-tunes only a few task-specific parameters.
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An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models (2024.findings-acl)

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Challenge: Multimodal Large Language Models fine-tuned with multimodal instruction-following data have demonstrated formidable capabilities in multimodal tasks.
Approach: They propose to employ four PEFT methods to fine-tune the LLM component of open-source MLLMs.
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Flexora: Flexible Low-Rank Adaptation for Large Language Models (2025.acl-long)

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Challenge: Large language models (LLMs) have revolutionized artificial intelligence, but performance on specific tasks is limited by knowledge boundaries.
Approach: They propose a method that automatically selects the most critical layers for fine-tuning to optimize performance across diverse downstream tasks.
Outcome: The proposed method outperforms baseline models and natural language tasks.
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models (2024.findings-acl)

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Challenge: Prior work on multilingual evaluation has shown that there is a large gap between the performance of Large Language Models on English and other languages.
Approach: They propose to finetune Llama-2 and Mistral models on two datasets to determine their effect on model performance on six downstream tasks covering forty one languages.
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression (2025.findings-emnlp)

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Challenge: Large language models excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data.
Approach: They propose a Data and Model Compression Framework that categorizes data filtering methodologies into three distinct paradigms: (1) distribution-aware methods, (2) quality-a aware methods, and (3) hybrid approaches considering both dimensions.
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