Generating Synthetic Datasets for Fine-Tuning LLMs on Sentiment Detection

Introduction

Fine-tuning Large Language Models (LLMs) for sentiment detection and text generation is crucial for applications in natural language processing (NLP). Using synthetic datasets like the GoEmotions dataset, which contains over 58,000 sentences labeled with 27 emotions, is a powerful approach. However, ensuring diversity and removing potentially biased information is essential for creating fair and accurate sentiment detection systems.

Leveraging the GoEmotions Dataset

The GoEmotions dataset, developed by Google, provides a rich resource for training LLMs to recognize and generate text with specific emotional tones. Key steps include:

1. Data Preprocessing:
   • Cleaning: Remove redundant or noisy data.
   • Balancing: Ensure an even distribution of emotions to prevent skewed model training.
2. Synthetic Data Generation:
   • Generate sentences representing each emotion to diversify the training data.

Diversifying Prompts

Diverse prompts help models generalize better and avoid overfitting:

• Variety of Contexts: Include prompts from different topics and scenarios to teach the model how emotions manifest in various contexts.
• Balanced Representation: Ensure a mix of neutral, positive, and negative sentiments.

Mitigating Bias in Datasets

Bias can lead to unfair models. Steps to mitigate this include:

1. Remove Sensitive Information:
   • Names: Exclude names to prevent celebrity bias.
   • Religion, Gender, Race, and Minorities: Remove references to avoid reinforcing stereotypes or biases.
2. Anonymization Techniques:
   • Replace sensitive information with neutral placeholders (e.g., “Person A” instead of specific names).
3. Bias Detection and Correction:
   • Regularly evaluate the model for biased outputs and retrain with balanced and anonymized data if needed.

Practical Implementation

To fine-tune an LLM for sentiment detection and generation:

1. Data Preparation:
   • Use the cleaned and anonymized GoEmotions dataset.
   • Generate diverse synthetic prompts to cover a wide range of emotions.
2. Model Training:
   • Fine-tune the LLM with a balanced mix of real and synthetic data representing all sentiment categories.
3. Evaluation:
   • Test the model on a separate validation set to ensure accuracy and fairness.
   • Continuously refine the dataset and retrain the model as necessary.

Conclusion

Creating synthetic datasets for fine-tuning LLMs on sentiment detection involves careful data preparation and bias mitigation. By leveraging the GoEmotions dataset and implementing strategies to diversify prompts and remove sensitive information, we can develop robust models capable of accurately detecting and generating text with specific sentiments. This approach enhances the model’s performance while ensuring fairness and inclusivity in NLP applications.