Jazyk AI GUIDE

Hodnocení LLM

LLM evaluation measures a language model or application against defined tasks and failure conditions.

2 minuty čteníNaposledy aktualizováno

Přehled

Relevant dimensions can include factual accuracy, instruction following, retrieval use, robustness, cost, and response time. A single preference score rarely captures all of them.

Klíčové věci

  • Evaluate the full application configuration.
  • Validate grading methods themselves.
  • Include abstention and adversarial cases.

Hluboký ponor

Evaluate the system users actually receive. A model with retrieval, tools, and a particular prompt may behave differently from the same model tested alone. Preserve these settings with the evaluation record, including limits on tool calls and retries. Combine deterministic checks with judgments that require interpretation. Exact matching works for some extracted fields or executable tests, while a summary may need a rubric for evidence and omissions. Write the rubric so different reviewers can apply it consistently, and examine disagreements. A model can assist with grading, but its judgment is another measurement process with possible biases. Check it against independently reviewed examples, vary answer order where appropriate, and inspect whether it rewards verbosity or style more than correctness. Do not treat one model approving another as independent proof. Include unanswerable questions, conflicting sources, long-context cases, and malicious instructions in retrieved material when these are relevant. Report results by task and error severity. Retain failed examples as regression cases while refreshing held-out material so the evaluation does not become a memorized target.

Technický přehled

A refusal may be correct for an unsupported or disallowed request and incorrect for an ordinary answerable question. Scoring must account for the intended behavior of each test case.

Separate helpfulness from factual support

  1. Give a model an invented policy stating only that refunds are available within 14 days.
  2. Ask whether shipping is refunded. A confident answer is unsupported because the policy does not say.
  3. Score an answer that identifies the missing information more highly than an invented policy, even if the invention sounds more helpful.

This constructed case evaluates evidence handling rather than fluency.

Strategický dopad

Rychlost a měřítko

Jazykové pracovní postupy se mohou pohybovat rychleji, aniž by byla obětována konzistentnost.

Přístup a dosah

Rozšiřuje přístup napříč jazyky a komunikačními styly.

Jasnější rozhodnutí

Týmy mohou strávit více času úsudkem, zatímco automatizace zvládne opakování.

Real-World Implementace

Grade a document answer on whether every claim is supported by the supplied passage.

Verify generated code through meaningful behavioral tests and review.

Rizika a zábradlí

Halucinovaná fakta mohou tiše vstupovat do zpráv, podpůrných toků nebo výstupů výzkumu.

Citlivost na výzvy může způsobit nekonzistentní výsledky napříč podobnými požadavky.

Citlivá textová data mohou být vystavena, pokud je řízení přístupu slabé.

Plán implementace

1

Před zavedením definujte výstupní formát, tón a standardy kvality.

2

Pozemní reakce s důvěryhodnými zdroji, kdykoli záleží na přesnosti.

3

Udržujte kontrolní bod lidské kontroly pro vysoce důležité výstupy.

4

Sledujte vzorce selhání a pravidelně opakujte výzvy nebo pracovní postupy.

Zdroje a další čtení

Pokračujte v objevování

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Často kladené otázky

Can an LLM judge replace all human review?

It can help scale some checks, but its reliability needs validation for the rubric and domain. Consequential or ambiguous cases may require independent review.