Enhancing Text Inputs for Improved Language Model Performance via Diverse Textual Augmentations

The study investigates the incorporation of large language models (LLMs) into fine-grained classification tasks, specifically in the context of few-shot learning. The authors explore various approaches to integrate LLMs’ knowledge and demonstrate their effectiveness through ablation studies. They show that even ordinary CLIP can benefit from incorporating LLMs’ knowledge, leading to improved performance across multiple benchmarks.
The study begins by highlighting the challenges of fine-grained classification, particularly in low-data regimens. To address this issue, the authors propose the use of LLMs to provide richer semantic information for fine-grained classification. They introduce two main approaches to integrate LLMs’ knowledge: (1) using the LLM as a base model and fine-tuning it on few-shot learning tasks, and (2) incorporating the LLM’s knowledge into the training strategy.
The authors conduct ablation studies to evaluate the effectiveness of these approaches. The results show that both methods lead to improved performance compared to using CLIP alone. Specifically, the first approach achieves an average improvement of 0.88% over all benchmarks, while the second approach shows consistent improvement across different datasets.
The study also explores the use of textual augmentations to further improve few-shot classification results. The authors find that incorporating LLMs’ knowledge into the training strategy and using textual augmentations can lead to even better performance.
In summary, the study demonstrates the potential of integrating large language models into fine-grained classification tasks, particularly in low-data regimens. By providing richer semantic information, these models can improve few-shot learning performance and help address the challenges of fine-grained classification.