Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents
arXiv:2607.00895v1 Announce Type: new Abstract: Hallucination detection for retrieval-augmented generation (RAG) is usually evaluated on natural-language document evidence. However, grounded generation systems increasingly rely on structured inputs: source code, developer-tool output, markdown documents, tables, and repository metadata. We introduce a unified benchmark for span-level hallucination detection over code, tool output, structured documents, and existing natural-language RAG datasets. The benchmark is built by starting from grounded correct answers, injecting localized hallucination
The proliferation of advanced AI models has made hallucination in diverse structured outputs a critical, contemporary challenge requiring robust detection methods.
A strategic reader should care because improving hallucination detection across various data types is fundamental to the reliability and trustworthiness of future AI systems, particularly in critical applications.
This benchmark signifies a shift towards more comprehensive and nuanced hallucination detection beyond natural language, incorporating code and tool outputs into RAG system evaluation.
- · AI developers
- · Software engineering
- · AI safety researchers
- · Financial services
- · Companies relying on unverified AI outputs
- · Generative AI models with high hallucination rates
- · Sectors requiring high AI trustworthiness without robust validation
Improved reliability and wider adoption of AI systems that integrate various structured data types.
Reduced incidence of costly errors or security vulnerabilities stemming from AI-generated code or tool outputs.
Accelerated development of autonomous AI agents capable of higher-stakes operations with reduced risk of 'drift' or 'fabrication'.
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Read at arXiv cs.CL