Tackling Ambiguity in User Intent for LLM-based Network Configuration Synthesis
24th ACM Workshop on Hot Topics in Networks (HotNets 2025), November 17-18, 2025.
Rajdeep Mondal, Nikolaj Bjørner, Todd Millstein, Alan Tang, George Varghese
Beyond hallucinations, another problem in program synthesis using LLMs is ambiguity in user intent. We illustrate the ambiguity problem in a networking context for LLM-based incremental configuration synthesis of route maps and ACLs. Configuration stanzas frequently overlap in header space, making the relative priority of actions impossible for the LLM to infer without user interaction. Measurements in a large cloud identify complex ACLs with 100s of overlaps, showing ambiguity is a real problem. We propose a prototype system, Clarify, augmenting an LLM with a new module called a disambiguator that helps elicit user intent. On a small synthetic workload, Clarify incrementally synthesizes routing policies and interactively disambiguates user intent to ensure correctness.
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