CONSCENDI: A Contrastive and Scenario-Guided Approach to Generating Diverse Training Data for Guardrail Models in Virtual Assistants

แนวคิดหลัก

CONSCENDI is a data generation pipeline that leverages scenario-guided conversations and contrastive examples to train smaller language models as effective guardrail models for virtual assistants. These guardrail models can identify rule violations in conversations with high accuracy, outperforming larger language models like GPT-4.

บทคัดย่อ

The paper introduces the problem of designing independent guardrail models to ensure virtual assistants operate within specified domain boundaries. It proposes CONSCENDI, a multi-stage data generation pipeline that leverages two key components:

Scenario-augmented generation: CONSCENDI first generates a set of rule-breaking scenarios that enumerate diverse ways a rule can be violated. It then uses these scenarios to generate conversational data that violates the rules.

Contrastive training examples: CONSCENDI also generates non-violating conversations by altering the rule-violating conversations to remove the violation. This provides fine-grained contrastive examples to the model during training.

The generated dataset is used to fine-tune smaller language models like GPT-3. The resulting distilled models outperform larger models like GPT-4 in accurately identifying rule violations, especially on out-of-distribution examples. The paper includes an ablation study demonstrating the importance of both the scenario-guided and contrastive components of the data generation pipeline.
The paper also creates a benchmark dataset across three domains (restaurants, buses, flights) with domain-specific rules inspired by the SGD dataset. Experiments show the distilled models achieve high accuracy in identifying rule violations, even on unseen scenarios.

สถิติ

Generating all 4671 conversations across the 3 domains costs $58.93 total.
Fine-tuning the distilled models costs $0.50 - $1.50 per model.
Inference costs range from $0.0001 per turn for the fastest GPT-3 Ada model to $0.02 per turn for GPT-4.

คำพูด

"We use CONSCENDI to exhaustively generate training data with two key LLM-powered components: scenario-augmented generation and contrastive training examples."
"Contrastive training examples are important in building a model that can deal with contrastive examples, as shown in the results comparing Distilled ✓scenarios and Distilled ✓contrastive ✓scenarios models."

ข้อมูลเชิงลึกที่สำคัญจาก

CONSCENDI

by Albert Yu Su... ที่ arxiv.org 04-05-2024

https://arxiv.org/pdf/2304.14364.pdf

สอบถามเพิ่มเติม

How could CONSCENDI's data generation approach be extended to handle more complex, multi-label rule violations?

CONSCENDI's data generation approach could be extended to handle more complex, multi-label rule violations by incorporating a more sophisticated scenario generation process. Instead of focusing on single rule violations, the system could be designed to generate scenarios that involve multiple rule violations simultaneously. This would require a more intricate rule set and scenario generation mechanism to cover all possible combinations of rule violations. Additionally, the conversation generation step would need to be adapted to create conversations that reflect these multi-label violations accurately. By expanding the scope of scenarios and conversations to include multi-label rule violations, CONSCENDI could provide a more comprehensive training dataset for models to learn from.

How might the performance of CONSCENDI-trained models compare to models fine-tuned on human-annotated data for the same task?

The performance of CONSCENDI-trained models may compare favorably to models fine-tuned on human-annotated data for the same task in certain aspects. While human-annotated data provides a high level of accuracy and reliability, it can be time-consuming and expensive to create. In contrast, CONSCENDI leverages automated data generation processes, which can generate a large volume of training data quickly and cost-effectively. This approach allows for a more extensive and diverse dataset, potentially leading to better generalization and robustness in model performance.
However, human-annotated data is likely to capture nuanced and context-specific rules and scenarios more accurately than automated generation. Human annotators can provide insights and judgments that automated systems may miss. Therefore, in scenarios where precise rule adherence and understanding of complex contexts are crucial, models fine-tuned on human-annotated data may outperform CONSCENDI-trained models. It is essential to consider the specific requirements of the task and the trade-offs between data quality, cost, and scalability when choosing between these approaches.

What other applications beyond virtual assistants could benefit from the CONSCENDI approach of leveraging scenario-guided and contrastive data generation?

The CONSCENDI approach of leveraging scenario-guided and contrastive data generation can benefit various applications beyond virtual assistants. Some potential applications include:

Content Moderation Systems: CONSCENDI could be applied to train models for content moderation tasks, ensuring that generated content adheres to specific guidelines and policies. By generating diverse scenarios and contrastive examples, the system can learn to identify and flag inappropriate or harmful content effectively.

Educational Chatbots: Chatbots used in educational settings could benefit from CONSCENDI's approach to ensure that responses align with educational standards and guidelines. By training models on scenario-guided data with contrastive examples, the chatbots can provide accurate and informative responses to students' queries.

Legal Document Analysis: In the legal domain, CONSCENDI could be used to train models to analyze legal documents and ensure compliance with legal regulations. By generating scenarios that represent different legal scenarios and providing contrastive examples, the system can assist in reviewing and interpreting legal texts accurately.

Customer Support Systems: Customer support chatbots can leverage CONSCENDI to ensure that responses to customer queries are in line with company policies and guidelines. The system can be trained on scenario-guided data to handle various customer scenarios effectively and provide appropriate responses.

Overall, the CONSCENDI approach can be applied to any task that requires adherence to specific rules, guidelines, or policies, making it a versatile and valuable tool in various domains.