Improving Text Classification with ChatGPT

ChatGPT is a large language model that has performed well in various language tasks, but it has some limitations. One major issue is that it can't adapt easily to specific tasks because its internal settings are not accessible for changes. Another problem is that it doesn't explain its decision-making process clearly, which is important in areas like healthcare where understanding decisions is crucial. To address these problems, we have developed a new method that uses ChatGPT for text classification while also making its decisions clearer.

#The Method

Our method starts by extracting useful knowledge from text using ChatGPT. This refined information is then organized into a graph that helps in training a straightforward classifier for text predictions. We tested our method on several datasets to see how well it works compared to just using ChatGPT by itself.

#ChatGPT and Its Limitations

ChatGPT has gained a lot of interest because of its strong abilities in handling language tasks like answering questions and summarizing texts. However, it often cannot be trained on specific datasets to improve performance. This means that sometimes it does not perform as well as other models, especially in tasks related to classifying text. Additionally, it does not provide a clear view of how it arrives at decisions, which makes it difficult to trust in critical situations.

#Knowledge Graph Extraction

Our approach includes a two-step process for extracting a knowledge graph using ChatGPT. First, we improve the quality of the text by fixing errors and making it clearer. Then, we extract a knowledge graph from this improved text, which identifies key entities and the relationships between them. This process makes it easier to understand the connections within the text.

#Creating the Text Graph

Once we have the knowledge graph, we convert it into a text graph, which consists of words as nodes and their relationships as edges. This organized format helps us capture essential semantic information and makes the classification task easier. The text graph enables us to track the links between words, which enhances clarity and Interpretability.

#Classifying Text with GCN

We use Graph Convolution Networks (GCNs) for text classification while keeping our model simple. By using a single layer of GCN, we ensure that the model remains transparent and interpretable. This means we can easily see how different words contribute to the predictions made about the text.

#Combining with External Knowledge

To further improve performance, we can incorporate additional information, such as TF-IDF scoring, into our model. This approach helps prioritize words that are more relevant to specific text segments, leading to better classification outcomes.

#Experiment Setup

We conducted experiments on five well-known text classification datasets to evaluate our method. These datasets included various types of texts, such as news articles and medical abstracts. For each dataset, we followed a standard method for splitting data into training and testing sets, ensuring a fair comparison with other models.

#Results of the Experiments

Our results showed that while traditional methods perform decently, using ChatGPT to extract knowledge and train a linear classifier leads to better results. Our proposed method performed better than ChatGPT alone and was competitive with more advanced models like TextGCN.

#Importance of Interpretability

Interpretability is a key aspect of our approach. By using a linear model, we can see which words were most important for making predictions. This is especially valuable in settings where users need to understand the reasoning behind decisions, such as in legal or medical fields.

#Addressing Limited Training Data

Our method also shows that even with limited labeled data, it can achieve solid performance. This is vital for situations where collecting labeled data might be difficult or costly, allowing for effective text classification even when resources are limited.

#Conclusion

In conclusion, our framework demonstrates the potential of using ChatGPT for text classification while ensuring the process remains interpretable. By focusing on clarity and structure, we have developed a method that can be effectively applied in various fields, paving the way for future advancements in natural language processing tasks. We plan to extend this framework to other language tasks in the future, highlighting the versatility of this approach.