Introduction

In Retrieval-Augmented Generation (RAG) systems, language models generate responses by combining retrieved documents with their pre-trained knowledge. This hybrid approach aims to improve the relevance and accuracy of generated content by grounding it in real data. However, even in RAG settings, hallucinations—instances where the model produces information not supported by the retrieved sources—can still occur. Evaluating and mitigating these hallucinations is crucial for ensuring the reliability of the generated outputs.

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Customizable Hallucination Warning Messages

The platform now offers the ability to customize hallucination warning messages. This feature allows the standard hallucination alert to be replaced with more tailored messages, such as, “Be aware, hallucination detected.” This customization ensures more relevant and informative guidance, helping to maintain accuracy and clarity throughout the platform. See Document Search V2 for configuration.

Importance of Hallucination Evaluation

Context:
In a RAG-based system, the generation of accurate and reliable responses is critical, especially in high-stakes fields such as finance, and legal services. Hallucinations in this context can lead to misinformation, causing potential harm or leading to incorrect decisions. Therefore, evaluating hallucination levels is essential to maintain the integrity of the responses generated by the system.

Challenges:
Hallucinations can arise from various factors:

• Model Bias: The language model may introduce biases or assumptions that are not present in the retrieved documents.

• Incomplete Retrieval: If the retrieved documents do not fully cover the topic, the model might extrapolate or generate additional information to fill gaps.

• Complex Queries: For complex or ambiguous queries, the model might generate more speculative responses that go beyond the content of the retrieved documents.

Hallucination Level Metric

To systematically evaluate and mitigate these hallucinations, a hallucination level metric can be applied to the responses generated by RAG systems. This metric categorizes responses into three levels based on their adherence to the retrieved sources:

1. Low Hallucination: The response is almost entirely grounded in the retrieved documents, with minimal or no deviation. This level indicates that the generated content is highly reliable and closely aligned with the source material.

2. Medium Hallucination: The response generally follows the retrieved documents but includes some additional details or interpretations that are not explicitly supported by the sources. While still useful, these responses require some caution, especially in critical decision-making scenarios.

3. High Hallucination: The response significantly deviates from the retrieved documents, introducing information that is not supported by the source material. This level suggests that the response may be unreliable and should be cross-verified or treated with skepticism.

Implementation in RAG Systems

Workflow:

1. Document Retrieval: The system first retrieves relevant documents based on the user's query.

2. Response Generation: The language model generates a response by synthesizing information from these documents.

3. Hallucination Evaluation: The hallucination level metric is applied to the generated response to assess how closely it aligns with the retrieved content.

Benefits:

• Enhanced Trust: By providing a clear indication of the hallucination level, users can better gauge the trustworthiness of the generated response.

• Improved Decision-Making: In fields requiring high accuracy, the ability to identify and assess hallucination levels can lead to better, more informed decisions.

• Continuous Improvement: Feedback from hallucination evaluations can be used to refine the user prompt.

Conclusion

Hallucination evaluation is a critical component in the effective deployment of RAG-based systems. By categorizing responses into Low, Medium, and High hallucination levels, the system can provide users with valuable insights into the reliability of the generated content. This evaluation process not only enhances user confidence but also supports the responsible use of AI in contexts where accuracy is crucial.