STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies show that supervised training is still necessary for complex reasoning tasks. |
| Approach: | They propose a method to integrate uncertainty-based active learning and LoRA to effectively integrate the two methods. |
| Outcome: | The proposed approach outperforms baseline models on three reasoning tasks. |
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Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings (2023.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques . data labeling is notoriously time-consuming and expensive, hindering the development of sizable labeled datasets . |
| Approach: | They propose to use active learning to reduce labeling costs by minimizing label complexity . they find PEFT adapter modules have significant potential in low-resource settings . |
| Outcome: | The proposed model outperforms FFT in low-resource settings and shows that it yields more stable representations of early and middle layers than FFT. |
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models (2022.naacl-main)
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| Challenge: | Existing methods for fine-tuning pre-trained language models ignore the potential of unlabeled data. |
| Approach: | They propose a framework that allows users to unleash the power of unlabeled data via self-training. |
| Outcome: | The proposed framework outperforms active learning and self-training baselines and improves the label efficiency of PLM fine-tuning by 56.2% on average. |
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)
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| Challenge: | balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications. |
| Approach: | They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers . |
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MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter (2024.acl-long)
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| Challenge: | Parameter-Efficient Fine-tuning (PEFT) methods are limited on knowledge-intensive tasks due to the limited number of trainable parameters. |
| Approach: | They propose a mechanism that fine-tunes Large Language Models with larger adapters . they store and update the parameters of larger adapter adapters on the CPU . |
| Outcome: | The proposed method achieves comparable results to those obtained with larger memory capacities over the limited bandwidth of PCI Express (PCIe). |
A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited due to the need for increased computational resources. |
| Approach: | They propose a method to learn PEFT parameters from data by projecting high dimensional parameters onto low dimensional parameter manifolds or identifying PEFT parametrically. |
| Outcome: | The proposed method can be used to identify PEFT parameters on GLUE tasks. |
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. |
| Outcome: | The proposed methods reduce trainable parameters while maintaining strong downstream performance. |
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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Let’s Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are composed of neurons that exhibit diverse behaviors and roles. |
| Approach: | They propose a novel approach that refines the granularity of parameter training down to the individual neuron, enabling a more parameter-efficient fine-tuning model. |
| Outcome: | The proposed approach exceeds the performance of full-parameter fine-tuning and PEFT and provides insights into the analysis of neurons. |
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. |
| Outcome: | The proposed method is the best performing on seven datasets, while fine-tuning the connector layers leads to improved performance in most MLLMs. |
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. |
| Outcome: | The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models. |