Leveraging Prompt-Learning with Small Language Models for Text Classification

In today's digital world, there is a large amount of text data generated every day from various sources like social media, customer interactions, and online discussions. Businesses and researchers often need to make sense of this data. One important way to do this is through text classification, which helps sort and understand text content in a meaningful way.

Text classification is especially important in specific areas or industries. For example, in customer support, accurately identifying what a customer is asking can help speed up responses and direct inquiries to the right place. However, getting labeled data, which is necessary for training models, can be very challenging. Labeling requires time and often requires knowledge about the industry, making it hard to gather enough data.

Recently, pre-trained language models have made significant advancements in natural language processing (NLP). These models, which are trained on extensive collections of text, can capture complex language patterns and can be adapted for different tasks. However, the traditional methods of fine-tuning these models often need a lot of labeled data, which is a challenge in specific domains.

This is where a new approach called prompt-learning comes in. This technique takes advantage of the rich knowledge already present in pre-trained models by using specific prompts to guide the models toward the desired output. This can be particularly useful when there is limited labeled data available. There have been some advancements in prompt-learning methods, both in situations where some training data is available and in cases where no training data is provided.

#Few-shot Learning

In cases where only a small number of examples are available, using the right samples becomes key to effective learning. Recent models have shown that using prompt-learning can work well even with limited data. For instance, using techniques like automated template generation, these models can significantly reduce the need for manual input. These improvements are promising, especially for specific applications like customer support, where getting enough labeled data can be difficult.

On the other hand, there are also situations where no training data is available, known as zero-shot settings. In this case, researchers focus on designing prompts that clearly explain the task to the model, allowing it to make predictions without needing training examples.

While large language models (LLMs) currently dominate the field, there is an increasing recognition of the benefits of smaller language models (SLMs), which usually have less than 1 billion parameters. These smaller models can be customized for specific tasks, making them adaptable and more cost-efficient.

#Research Focus

The main aim of this research is to examine how effective prompt-learning can be when used with Small Language Models for text classification, particularly in the context of customer interactions within the retail sector. We look at how the combination of SLMs and prompt-learning can help accurately classify text with less labeled data.

Some key findings include:

1. In few-shot settings, using SLMs with prompt-learning can achieve good results, around 75% accuracy, even with limited labeled data.
2. Strategies like Active Sampling and combining different prompts can improve performance significantly.
3. In zero-shot situations, while larger models like GPT-3.5-turbo achieve high accuracy, smaller models like FLAN-T5-large can still perform well with proper prompts.

Through this study, we provide insights into how prompt-learning can be applied to achieve effective domain-specific text classification, especially when working with limited labeled data.

#Data Collection

For this study, we used a dataset from IKEA's customer support service, which includes text-based conversations between customers and agents. This dataset contains over 7,000 interactions, all cleaned to protect customer privacy. Each conversation was manually categorized into 13 different customer intents, such as product inquiries and billing issues.

The dataset is imbalanced, meaning some intents occur more frequently than others. To ensure fair evaluation, we divided the dataset into three parts: a training and development set, a validation set, and a test set. The training set is used for building the model, while the validation set checks how well the model is performing. The test set is used to provide a final measure of the model's effectiveness.

#The Prompt-Learning Process

The prompt-learning process involves several key steps. First, the input text is transformed using a prompt, which often includes specific questions or instructions. For example, a conversation might be reframed to ask, "What is the topic of this conversation?" This helps the model focus on the important parts of the text.

Next, the model attempts to fill in the gaps in the prompt using its existing knowledge. The highest-scoring option is then mapped to the corresponding label. This mapping is crucial, especially when there might be multiple potential answers for one output.

#Active Few-Shot Sampling

Rather than randomly picking examples for few-shot learning, our research focused on actively selecting the best representative samples for each category. We used a model to analyze the training data and identify the most representative examples. This active sampling approach proved to boost model performance, showing that thoughtful selection of training data has a significant impact.

#Prompt and Verbalizer Ensemble

Different prompts or verbalizers can lead to varying results in text classification. To improve outcomes, we explored combining multiple prompts and verbalizers. By pulling together various templates, we created models that took advantage of different approaches, leading to improved overall performance.

#Zero-shot Learning

In zero-shot settings, we tested different language models and prompt designs to see how well they classified text without any training examples. Simpler prompts often led to poor performance, while more detailed prompts significantly improved results. This underscored the importance of designing effective prompts that provide necessary context and clarity.

#Conclusion and Future Directions

This research highlights the strengths of using prompt-learning with small language models for text classification tasks in specific settings. Our findings suggest that this method can be especially powerful in cases where labeled data is limited. By effectively utilizing small models and optimizing prompt designs, businesses can develop efficient classifiers even with minimal input data.

Moving forward, there are many areas to explore. Future work might include refining prompt designs, applying these techniques in other industries like healthcare or finance, and finding new ways to make the most out of smaller models in a world dominated by larger counterparts. As the field continues to adapt, prompt-learning is likely to play an important role in managing domain-specific text data efficiently.