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Reducing hallucination in structured outputs via Retrieval-Augmented Generation (2404.08189v1)

Published 12 Apr 2024 in cs.LG, cs.AI, cs.CL, and cs.IR

Abstract: A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While LLMs (LLM) have taken the world by storm, without eliminating or at least reducing hallucinations, real-world GenAI systems may face challenges in user adoption. In the process of deploying an enterprise application that produces workflows based on natural language requirements, we devised a system leveraging Retrieval Augmented Generation (RAG) to greatly improve the quality of the structured output that represents such workflows. Thanks to our implementation of RAG, our proposed system significantly reduces hallucinations in the output and improves the generalization of our LLM in out-of-domain settings. In addition, we show that using a small, well-trained retriever encoder can reduce the size of the accompanying LLM, thereby making deployments of LLM-based systems less resource-intensive.

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Authors (2)
  1. Patrice Béchard (3 papers)
  2. Orlando Marquez Ayala (5 papers)
Citations (15)
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Summary

Reducing Hallucination in Generative AI through Retrieval-Augmented Generation for Structured Output Tasks

Introduction to Retrieval-Augmented Generation (RAG) in Workflow Generation

LLMs are pivotal in transforming natural language inputs into structured outputs such as workflows, which are executed automatically under specified conditions. These advancements are crucial in automating repetitive tasks and improving productivity within enterprise systems. However, the effectiveness of Generative AI (GenAI) applications is marred by the propensity of LLMs to produce hallucinated outputs—generating incorrect or non-existent elements in the structured output. Addressing this challenge, the integration of Retrieval-Augmented Generation (RAG) with LLMs presents a promising solution. By retrieving and incorporating external knowledge before generation, RAG significantly mitigates the occurrence of hallucination, thereby enhancing the trustworthiness and applicability of GenAI systems in real-world settings.

Methodological Overview

The RAG framework introduced in this paper leverages a dual-component approach comprising a retriever model and a generative model. The retriever model is trained to map natural language queries to relevant structured information, such as steps and tables required in the workflow generation task. This mapping facilitates the reduction of hallucinated content by ensuring that the generated outputs are grounded in existing, real-world entities. The generative model, or LLM, is fine-tuned in conjunction with the retrieved content to produce the final structured output in JSON format. This methodology not only curtails hallucination but also allows for the utilization of smaller LLMs without a compromise on performance, presenting a cost-effective solution for deploying GenAI systems.

Results and Contributions

The implementation of RAG in workflow generation yields significant improvements in reducing hallucinations, with a marked decrease in non-existent steps and tables in the generated output. Notably, the fine-tuning of RAG enables the deployment of smaller LLMs alongside a compact retriever model, efficiently managing resource consumption without detracting from model performance. This approach delineates a practical pathway for employing GenAI systems within enterprise applications, ensuring both reliability and scalability.

Implications and Future Directions

The practical implications of this research are manifold, addressing both the theoretical and commercial challenges in the deployment of GenAI systems. The reduction of hallucination in structured output tasks not only enhances the trustworthiness of GenAI applications but also broadens their potential for adoption across various domains. Moreover, the efficiency gains from utilizing smaller models underscore the feasibility of deploying sophisticated AI solutions in resource-constrained settings. Future explorations will focus on refining the synergy between the retriever and the LLM, possibly through joint training or innovative architectural designs, to further optimize the generation process and reduce hallucinations.

Ethical Considerations

While this paper takes significant strides in mitigating the risks associated with hallucination in GenAI systems, it does not eliminate them. Deployed systems incorporate additional safeguards, such as indicating potentially unreliable steps to users, emphasizing the importance of human oversight in AI-generated outputs. Continuous efforts towards understanding and addressing the limitations of GenAI systems are crucial in ensuring their ethical and responsible application.

Conclusion

The integration of Retrieval-Augmented Generation with LLMs presents a compelling approach to reducing hallucinations in structured output tasks, paving the way for more reliable and scalable GenAI systems in enterprise settings. Through methodological innovation and pragmatic application, this paper contributes valuable insights into the ongoing development and deployment of AI technologies, with a clear path forward for future research and implementation.

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