Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost
This addresses the challenge of ensuring accurate and cost-effective hallucination detection for industry deployments of large language models, representing a strong specific gain but incremental in method.
The paper tackles the problem of detecting hallucinations in Retriever Augmented Generation systems, which is crucial for reliability in industry applications, and introduces Luna, a finetuned DeBERTA-large encoder that achieves high accuracy with 97% cost reduction and 91% latency reduction compared to GPT-3.5 and commercial frameworks.
Retriever Augmented Generation (RAG) systems have become pivotal in enhancing the capabilities of language models by incorporating external knowledge retrieval mechanisms. However, a significant challenge in deploying these systems in industry applications is the detection and mitigation of hallucinations: instances where the model generates information that is not grounded in the retrieved context. Addressing this issue is crucial for ensuring the reliability and accuracy of responses generated by large language models (LLMs) in diverse industry settings. Current hallucination detection techniques fail to deliver accuracy, low latency, and low cost simultaneously. We introduce Luna: a DeBERTA-large (440M) encoder, finetuned for hallucination detection in RAG settings. We demonstrate that Luna outperforms GPT-3.5 and commercial evaluation frameworks on the hallucination detection task, with 97% and 91% reduction in cost and latency, respectively. Luna is lightweight and generalizes across multiple industry verticals and out-of-domain data, making it an ideal candidate for industry LLM applications.