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Reducing hallucination in structured outputs via Retrieval-Augmented Generation
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A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While large language models (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.
Forward citations
Cited by 8 Pith papers
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Visual Token Compression Enhances Robustness of MLLMs
Pruning visual tokens farthest from the text feature space at selected 'robust' layers improves MLLM jailbreak defense (average +13.29% RAR) and slightly reduces hallucination.
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Rethinking Query Optimization for Multi-Agent Systems [Vision]
Agentic data pipelines are built by hand today; this paper sets a research agenda for automatically optimizing their structure, model choices, and execution engines jointly as a new query-optimization problem.
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Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation
A mention-level entity-event knowledge graph for RAG modestly improves temporal-causal question answering on a new narrative benchmark, with gains mostly coming from adding HyDE-style hypothetical answers.
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Hallucination Detection with Small Language Models
A multi-small-model ensemble with sentence splitting, z-score normalization, and harmonic mean detects hallucinations in RAG answers with a reported 10% F1 gain over single-model baselines.
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Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
In a physician-rated crowdsourced study, 76% of LLM responses to everyday health queries were valid, with GPT-4o highest (85%) and Llama3-8b lowest (50%); RAG did not consistently improve responses.
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Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.
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Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
A RAG-based tutoring and support chatbot with hybrid search and an instructor feedback loop is described, but no quantitative evaluation of its accuracy is provided.
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ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms
ThinkTank generalizes scientific collaboration roles, meeting formats, and retrieval-augmented knowledge integration into one reusable multi-agent platform.
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