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Hallucination

Overview​

hallucination is a route-local plugin for fact-checking and response-quality screening after the decision already matched.

Key Advantages​

  • Adds route-local hallucination checks without changing global defaults.
  • Makes response actions explicit when factual confidence is low.
  • Works well for retrieval-heavy or grounded-answer routes.

What Problem Does It Solve?​

Some routes need extra scrutiny after the model answers, especially when they promise factual precision. hallucination lets those routes add response-time verification without forcing every route to pay the cost.

When to Use​

  • a route should fact-check or annotate responses
  • grounded or tool-backed routes need extra response screening
  • the route should warn or annotate instead of silently passing low-confidence answers

Configuration​

Add the plugin under routing.decisions[].plugins:

plugins:
  - type: hallucination
    configuration:
      enabled: true
      use_nli: true
      hallucination_action: header
      unverified_factual_action: header
      include_hallucination_details: true

header preserves the model response and adds warning metadata. body adds a warning to the response body, while none records the result without changing the response.

With a hallucination signal declared under routing.signals.hallucination, detection runs as a response-stage signal and this plugin only enforces on it: it applies hallucination_action when the rule matched, unverified_factual_action when the answer had no grounding context to be checked against, and nothing when the rule was unavailable or did not apply. The rule then decides whether NLI explanations are produced, and the plugin's use_nli is reported as ignored at load. Without a rule the plugin classifies the answer itself, which is the compatibility path and is reported at load as such.

The plugin depends on global.model_catalog.modules.hallucination_mitigation; use_nli: true also uses the configured explainer/NLI model. Model responses and supplied grounding context are processed by those modules. Grounding context longer than the detector's token window is trimmed from the end so the answer always reaches the model. Detection can identify unsupported text, but it cannot establish truth without authoritative evidence.

See a complete example: config/fragments/plugin/hallucination/fact-check.yaml.