L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs

TL;DR

L-Tuning introduces a synchronized label tuning method for LLMs that enhances training efficiency and classification accuracy by fine-tuning label tokens within the NLI framework, leveraging pre-trained semantic knowledge.

Contribution

This paper presents L-Tuning, a novel label tuning approach that improves fine-tuning efficiency and accuracy by focusing on label tokens in LLMs, diverging from traditional prompt or prefix tuning methods.

Findings

Significant improvement in training efficiency

Enhanced classification accuracy

Generation of distinct label embeddings

Abstract

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.