DiagFlowBench: Evaluating How Language Models Handle Off-Procedure Inputs in Grounded Diagnostic Dialogue
arXiv:2606.17904v1 Announce Type: new Abstract: Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in procedural documentation to constrain them to approved steps. In practice, however, operator queries frequently stray from this path, requiring models to recognise out-of-scope inputs mid-conversation, a dynamic that current benchmarks rarely prioritise. We introduce DiagFlowBench, a dataset of 50 industrial diagnostic flowcharts from a consumer manufacturer converted into 1,676 multi-turn conversations
The increasing deployment of language models as advisory systems in critical maintenance operations necessitates robust evaluation for out-of-scope interactions that are common in real-world scenarios.
This benchmark directly addresses a significant vulnerability in current AI applications – their inability to reliably handle unscripted input, which is crucial for safety, efficiency, and trust in autonomous systems.
The introduction of DiagFlowBench will likely spur a new wave of research and development focused on improving the robustness of grounded language models against off-procedure inputs, leading to more reliable AI advisory systems.
- · AI safety researchers
- · Developers of industrial AI applications
- · Manufacturers adopting AI advisory systems
- · AI models lacking robustness in out-of-scope handling
- · Benchmarks focused solely on on-procedure interactions
Improved performance and reliability of language models in real-world diagnostic and maintenance tasks.
Increased adoption of AI advisory systems in complex operational environments due to enhanced trust and reduced risk of hallucination.
Potential for a shift in AI development priorities towards grounding, robustness, and context-awareness over sheer scale and generative capabilities.
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Read at arXiv cs.AI