L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs
This addresses the problem of prolonged training times and generalized token use in fine-tuning LLMs for NLP tasks like Natural Language Inference, representing an incremental advancement.
The paper tackles the challenge of efficiently fine-tuning Large Language Models for classification tasks by introducing L-Tuning, which fine-tunes label tokens to leverage pre-existing semantic knowledge, resulting in improved training efficiency and classification accuracy compared to traditional methods.
Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language Inference (NLI) framework. Diverging from conventional methods, L-Tuning focuses on the fine-tuning of label tokens processed through a pre-trained LLM, thereby harnessing its pre-existing semantic knowledge. This technique not only improves the fine-tuning accuracy and efficiency but also facilitates the generation of distinct label embeddings for each class, enhancing the model's training nuance. Our experimental results indicate a significant improvement in training efficiency and classification accuracy with L-Tuning compared to traditional approaches, marking a promising advancement in fine-tuning LLMs for complex language tasks.