What does this research mean for the field?

In low-resource sentiment classification tasks, LoRA provides superior in-domain accuracy compared to Prompt Tuning, whereas Prompt Tuning offers comparable cross-domain generalization with a significantly smaller parameter footprint. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to compare the effectiveness of LoRA and Prompt Tuning for fine-tuning large language models under low-resource conditions.

May 31, 2026Open Access

Parameter-Efficient Fine-Tuning of Large Language Models Using LoRA and Prompt-Tuning Under Low-Resource Conditions

Key Points

The research aims to compare the effectiveness of LoRA and Prompt Tuning for fine-tuning large language models under low-resource conditions.
Utilized a controlled experimental setup for comparison of LoRA and Prompt Tuning methods.
Conducted binary sentiment classification tasks using distilbert model with varying training data sizes (1,000 and 100 samples).
Assessed performance metrics, including accuracy and number of trainable parameters.
LoRA achieved 88.7% accuracy with 1,000 training samples, while Prompt Tuning achieved 82.5% with 15,360 parameters.
LoRA maintained 70.0% accuracy with only 100 samples, indicating robustness under low-resource conditions.
In cross-domain tests on Yelp review data, LoRA obtained 86.8% accuracy compared to Prompt Tuning's 84.4%.

Abstract

The process of adapting large pre-trained language models (lms) to tasks with few-labeled data remains a major challenge because of the restrictions on data and computation. This paper mainly discusses two parameter-efficient methods of fine-tuning, Low Rank Adaptation (LoRA) and Prompt Tuning, for binary sentiment classification where the base model is distilbert in low-resource situations. In order to make a fair comparison of the two methods, a controlled experimental setup is used.Experiments have shown that with the size of training data 1,000 LoRA can produce the result 88.7% in terms of accuracy, whereas Prompt Tuning can produce 82.5% but with far fewer trainable parameters (15,360). Even with extremely low-resource conditions (100 samples), LoRA still can have a performance of 70.0%. The results for cross-domain on Yelp review data show that LoRA obtains 86.8% accuracy, whereas Prompt Tuning 84.4%, which is also indicative of a good generalization ability. The result of this work is that LoRA is shown to be a better choice if the performance within the domain is desired, while Prompt Tuning is a better choice in the case of limited parameter budget but will have comparable performance in transfer task. Besides, they shed light on the practical knowledge of choosing fine-tuning methods in resource-constrained environments.

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