Fine-tuning large language models (LLM) can be costly. Parameter-efficient fine-tuning (PEFT) addresses the problems by training a fraction of the parameters, whose success reveals the expressiveness and flexibility of pretrained models. This paper studies the limit of PEFT, by further simplifying its design and reducing the number of trainable parameters beyond standard setups. To this end, we use Random Masking to fine-tune the pretrained model. Despite its simplicity, we show that Random Masking is surprisingly effective: with a larger-than-expected learning rate, Random Masking can match the performance of standard PEFT algorithms such as LoRA on various tasks, using fewer trainable parameters. We provide both empirical and theoretical explorations into the success of Random Masking. We show that masking induces a flatter loss landscape and more distant solutions, which allows for and necessitates large learning rates.

Sentiment analysis plays a crucial role in understanding the sentiment expressed in text data. While sentiment analysis research has been extensively conducted in English and other Western languages, there exists a significant gap in research efforts for sentiment analysis in low-resource languages. Limited resources, including datasets and NLP research, hinder the progress in this area. In this work, we present an exhaustive study of data augmentation approaches for the low-resource Indic language Marathi. Although domain-specific datasets for sentiment analysis in Marathi exist, they often fall short when applied to generalized and variable-length inputs. To address this challenge, this research paper proposes four data augmentation techniques for sentiment analysis in Marathi. The paper focuses on augmenting existing datasets to compensate for the lack of sufficient resources. The primary objective is to enhance sentiment analysis model performance in both in-domain and cross-domain scenarios by leveraging data augmentation strategies. The data augmentation approaches proposed showed a significant performance improvement for cross-domain accuracies. The augmentation methods include paraphrasing, back-translation; BERT-based random token replacement, named entity replacement, and pseudo-label generation; GPT-based text and label generation. Furthermore, these techniques can be extended to other low-resource languages and for general text classification tasks.

RGB-thermal semantic segmentation is one potential solution to achieve reliable semantic scene understanding in adverse weather and lighting conditions. However, the previous studies mostly focus on designing a multi-modal fusion module without consideration of the nature of multi-modality inputs. Therefore, the networks easily become over-reliant on a single modality, making it difficult to learn complementary and meaningful representations for each modality. This paper proposes 1) a complementary random masking strategy of RGB-T images and 2) self-distillation loss between clean and masked input modalities. The proposed masking strategy prevents over-reliance on a single modality. It also improves the accuracy and robustness of the neural network by forcing the network to segment and classify objects even when one modality is partially available. Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities. Based on the proposed method, we achieve state-of-the-art performance over three RGB-T semantic segmentation benchmarks. Our source code is available at https://github.com/UkcheolShin/CRM_RGBTSeg.