Evaluating the Effectiveness of Large Language Models for Fine-tuning on Chinese Short Text Matching

Core Concepts

Large Language Models (LLMs) can be effectively fine-tuned for the task of Chinese short text matching, outperforming fine-tuned BERT and few-shot GPT-4. The generative modeling approach is superior to the discriminative approach, especially with limited training data. Incorporating Chain of Thought (CoT) into the training samples also improves performance, particularly on more challenging datasets.

Abstract

The paper investigates the effectiveness of fine-tuning Large Language Models (LLMs) for the task of Chinese short text matching. It explores various factors that influence performance, including task modeling methods, prompt formats, and the use of Chain of Thought (CoT).
The key findings are:

Fine-tuned CLLM-7B (a Chinese-enhanced LLM based on LLaMA-2-7B) outperforms both fine-tuned BERT and few-shot GPT-4 on the Chinese short text matching task.

The generative modeling approach, where the model is prompted to generate the target label, outperforms the discriminative approach (using the LLM output for binary classification) when training data is limited.

Prompt design is less impactful in supervised settings compared to zero- and few-shot scenarios. Concise and complex prompts achieve similar performance.

Incorporating CoT into the training samples, obtained using GPT-4, improves performance, especially on more challenging datasets like BQ.

The authors suggest that their observations may be applicable to other NLU tasks beyond text matching, such as text classification.

Stats

The accuracy of 2-shot GPT-4 on the BQ dataset is much worse than supervised models, likely due to the dataset's domain-specific nature and the need for background knowledge about WeBank.
CLLM-7B-GEN trained on the full LCQMC training set outperforms BERT, but fails to outperform BERT on the BQ dataset, suggesting CLLM-7B also lacks the domain-specific knowledge required for the BQ task.

Quotes

"When the number of training samples is less than 20,000, CLLM-GEN significantly outperforms discriminative models, including BERT and CLLM-CLS, on both LCQMC and BQ."
"CLLM-GEN trained on the whole training corpus on LCQMC outperforms BERT. However, it fails on the BQ corpus. We believe the reason is that CLLM-7B, like BERT, also lack knowledge of WeBank, and such knowledge can only be obtained from the training data."
"CoT is also beneficial for supervised text matching. Although our experiments focus on the task of text matching, the observations may be applicable to other NLU tasks, such as text classification."

Key Insights Distilled From

Are LLMs Effective Backbones for Fine-tuning? An Experimental Investigation of Supervised LLMs on Chinese Short Text Matching

by Shulin Liu,C... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19930.pdf

Deeper Inquiries

How can the performance of LLMs on Chinese short text matching be further improved, beyond the techniques explored in this paper

To further enhance the performance of Large Language Models (LLMs) on Chinese short text matching beyond the techniques explored in the paper, several strategies can be considered:

Domain-specific Pre-training: Conduct pre-training on a large corpus of domain-specific data related to short text matching in Chinese. This can help the model capture domain-specific nuances and improve performance on task-specific datasets like BQ and LCQMC.

Multi-task Learning: Implement multi-task learning by training the LLM on related tasks such as paraphrase detection, semantic similarity, or text entailment. This can help the model learn more generalized representations that can benefit short text matching tasks.

Data Augmentation: Introduce data augmentation techniques specific to short text matching, such as synonym replacement, paraphrasing, or adding noise to the input data. This can help the model generalize better and improve performance on unseen data.

Ensemble Methods: Combine multiple fine-tuned LLMs with different architectures or pre-training strategies to create an ensemble model. Ensemble methods often lead to improved performance by leveraging diverse model predictions.

Hyperparameter Tuning: Explore different hyperparameter configurations during fine-tuning, such as learning rates, batch sizes, or optimizer choices, to optimize the model's performance on the specific task of Chinese short text matching.

What are the potential limitations or drawbacks of using LLMs as backbones for fine-tuning on NLU tasks, and how can these be addressed

Using LLMs as backbones for fine-tuning on Natural Language Understanding (NLU) tasks may have some limitations and drawbacks, including:

Data Efficiency: LLMs require large amounts of labeled data for fine-tuning, which can be a challenge for tasks with limited annotated datasets. Addressing this limitation may involve exploring semi-supervised or transfer learning techniques to leverage unlabeled data effectively.

Interpretability: LLMs are often criticized for their lack of interpretability, making it challenging to understand the model's decision-making process. Techniques like attention visualization, saliency maps, or model distillation can help improve interpretability.

Bias and Fairness: LLMs can inherit biases present in the training data, leading to biased predictions. Mitigating bias and ensuring fairness in model predictions require careful data preprocessing, bias detection methods, and fairness-aware training strategies.

Computational Resources: Fine-tuning LLMs can be computationally expensive and time-consuming, especially for large models like GPT-3. Efficient hardware utilization, distributed training, and model compression techniques can help address this issue.

Addressing these limitations involves a combination of algorithmic improvements, data preprocessing strategies, and model evaluation techniques to ensure the effective and ethical use of LLMs in NLU tasks.

Given the domain-specific nature of the BQ dataset, how could the incorporation of external domain knowledge or data augmentation techniques help improve the performance of LLMs on this task

Given the domain-specific nature of the BQ dataset, incorporating external domain knowledge or data augmentation techniques can help improve the performance of LLMs on this task:

Domain-specific Embeddings: Utilize domain-specific word embeddings or knowledge graphs related to the banking domain to enhance the model's understanding of domain-specific terminology and concepts present in the BQ dataset.

Transfer Learning from Related Domains: Fine-tune the LLM on related domains such as finance, customer service, or banking-specific text corpora to transfer knowledge and improve performance on the BQ dataset.

Data Augmentation with Domain-specific Synonyms: Augment the training data with domain-specific synonyms, phrases, or contextually relevant information to help the model generalize better to unseen instances in the banking domain.

Adversarial Training: Introduce adversarial training techniques to make the model robust to domain-specific variations and ensure that it can handle challenging cases specific to the banking domain present in the BQ dataset.

By incorporating external domain knowledge, leveraging transfer learning, and applying data augmentation techniques tailored to the banking domain, the performance of LLMs on the BQ dataset can be enhanced, leading to more accurate and robust predictions.