Enhancing Novel Character Role-Playing with KTO Fine-Tuning

Large language models (LLMs) are increasingly used for novel character role-playing, where the AI assumes a specific persona to interact with users. This approach is valuable in entertainment applications like games and novels, enhancing user engagement by providing immersive experiences. The goal is to train models to generate responses that are emotionally resonant, visually descriptive, and consistent with the character's established traits. This article explores how to fine-tune LLMs to achieve these objectives, focusing on the KTO (Keep To Original) training method.

Despite the potential, using generic LLMs for role-playing often falls short of user expectations. Common issues include: 1. **Lack of Authenticity and Logical Inconsistencies:** The AI's responses may sound too robotic, lacking the nuances of human emotion and character. Logical inconsistencies can also arise, where the AI's actions or statements contradict the established character or scenario. 2. **Weak Character Style and Missing Persona:** The AI may fail to capture the unique style and personality of the character, resulting in generic responses that don't reflect the character's identity. 3. **Unstable Output and Persona Confusion:** The AI may produce inconsistent responses, sometimes even confusing the character's persona with that of another character in the story.

KTO (Keep To Original) fine-tuning offers an effective solution to these challenges. KTO is a training method that aligns the model's behavior with user preferences by using positive and negative feedback. By leveraging KTO, LLMs can better understand and embody the nuances of a character, resulting in more authentic and engaging interactions. KTO training helps in: * **Improving Character Consistency:** By training the model on data that reinforces the character's traits and style, KTO ensures that the AI's responses remain consistent with the character's persona. * **Enhancing Emotional Expression:** KTO allows the model to learn from examples of human-like emotional expression, enabling it to generate responses that are more emotionally resonant. * **Reducing Persona Confusion:** By including examples of potential 'bad case' scenarios in the training data, KTO helps the model differentiate between characters and avoid persona confusion.

The core process of model fine-tuning involves several key steps: 1. **Data Preparation:** Creating a high-quality dataset is crucial for effective training. This involves collecting, analyzing, and processing data to ensure it accurately represents the desired character and scenarios. 2. **Model Selection:** Choosing the right base model is essential. Factors to consider include the model's performance, training time, and cost. 3. **Training Configuration:** Selecting the appropriate fine-tuning method and parameters is critical for optimizing the model's performance. 4. **Evaluation:** Assessing the model's performance through manual or automated evaluation methods helps identify areas for improvement. 5. **Deployment:** Deploying the fine-tuned model as a service allows it to be integrated into real-world applications.

Preparing data for KTO training involves several steps: 1. **Collecting Raw Data:** Gather data in the format of Prompt + Chosen/Rejected, where 'Chosen' represents the preferred response and 'Rejected' represents an undesirable response. Multi-turn dialogue formats are also essential for role-playing scenarios. 2. **Data Considerations:** * **Authenticity:** Use real-world data to train the model effectively. * **Quantity:** Aim for a dataset of at least 1000 examples, but be aware that more data isn't always better. * **Balance:** Maintain a balanced ratio of Chosen and Rejected data. * **Quality:** Ensure data is clean, accurate, and free of errors. * **Bad Case Handling:** Include and correct examples of undesirable responses. * **Character Coverage:** Cover a wide range of characters in the dataset. * **Multi-Turn Data:** Use multi-turn dialogue data to simulate realistic conversations. 3. **Processing Raw Data:** Use data annotation tools to improve data quality, ensuring that dialogues are coherent and relevant. 4. **Splitting Datasets:** Divide the dataset into training and evaluation sets, ensuring that the evaluation set covers a range of scenarios and characters.

Selecting the right base model is crucial for effective role-playing. The model should have strong memory, language understanding, and creative capabilities. Consider factors such as performance, training time, and cost when choosing a model. For fine-tuning methods, KTO offers two options: full parameter updates and LoRA (Low-Rank Adaptation). Full parameter updates provide better accuracy and generalization but require more computational resources. LoRA is more efficient and cost-effective but may sacrifice some accuracy. Key parameters to configure include the number of training epochs and the learning rate. Experiment with different values to find the optimal configuration for your specific scenario.

Evaluating the fine-tuned model involves assessing its ability to adhere to the character's persona and the quality of its responses. Evaluation methods include: 1. **Scoring Standards:** Assess the model based on character consistency and response quality. 2. **Scoring Methods:** Use GSB (Good, Same, Bad) scoring to compare different models or parameter configurations. Use absolute scoring to evaluate the overall performance of the model. 3. **Scoring Approaches:** Use manual scoring for accuracy or automated scoring with large language models for efficiency. In the provided example, ERNIE 4.0 was used for automated scoring. The results of the fine-tuning process demonstrate that KTO-trained models significantly outperform the original models. The KTO models generate responses that are more aligned with the character's persona and the context of the conversation, leading to an enhanced user experience.

After fine-tuning and evaluating the model, deploy it as a service for real-world use. Choose a deployment option that suits your needs, such as pay-as-you-go or resource pool-based pricing. In conclusion, fine-tuning LLMs with KTO is an effective approach for enhancing the quality of novel character role-playing. By carefully preparing data, selecting the right model, configuring training parameters, and evaluating the results, you can create AI models that provide immersive and engaging experiences for users. The benefits of KTO fine-tuning include improved character consistency, enhanced emotional expression, and reduced persona confusion, resulting in a superior role-playing experience.