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LLM-as-Judge

Score extractor output, detect hallucinations, compare texts, and run a multi-model judge panel.

How to use the LLM-as-Judge

  1. 1Pick a modeFour ways to check an extractor: compare two texts, find invented fields, validate against a PDF, or ask a panel of judges.
  2. 2Start from a caseEach mode has ready-made cases that say what a person would expect the judge to find.
  3. 3Run the judgeA model reads the case and gives a verdict, a 1 to 5 score and the reason.
  4. 4Read the verdictSee whether it matches the expectation, which words or fields it is about, and what to correct.
  • A judge is a model that decides whether an extracted value is right. It handles paraphrases, word forms and date formats that an exact comparison calls wrong, but it can be wrong too: read the reason, not only the verdict.
  • The score reads: 1 is clearly wrong, 2 to 3 is suspect and worth a look, 4 to 5 looks correct. Several judges together (the panel) show how sure the verdict is.

Start from a case

1. The two texts
Compare an extractor's value with a reference. The judge handles paraphrasing, word forms and date formats, and needs no source document.

The name helps the judge: "quantity" is read as a number, "date" as a date.

No judgement yet

Pick a case or fill in the two texts, then click Judge equivalence.

How the score reads (Likert 1–5):

1wrongClearly wrong
2–3suspectSuspect: review before accepting
4–5okLooks correct
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Generative AI building blocks for knowledge capture, access and synthesis.

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Disclaimer: The demo website uses Microsoft Azure APIs for OpenAI's and Anthropic's models, and Google's Vertex AI for Gemini models. No user data is retained, and neither Azure nor Vertex AI uses any user data for model training.

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