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Penilaian Berpasangan dan Elo
Teknikal
PANDUAN AI Bahasa
Penilaian LLM
Penilaian LLM mengukur model atau aplikasi bahasa terhadap tugas yang ditetapkan dan keadaan kegagalan.
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Gambaran keseluruhan
Relevant dimensions can include factual accuracy, instruction following, retrieval use, robustness, cost, and response time. A single preference score rarely captures all of them.
Pengambilan utama
- Evaluate the full application configuration.
- Validate grading methods themselves.
- Include abstention and adversarial cases.
Menyelam dalam
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.
04Contoh yang dikerjakan
Separate helpfulness from factual support
Give a model an invented policy stating only that refunds are available within 14 days.
Ask whether shipping is refunded. A confident answer is unsupported because the policy does not say.
Score an answer that identifies the missing information more highly than an invented policy, even if the invention sounds more helpful.
Apa yang ditunjukkan
This constructed case evaluates evidence handling rather than fluency.
Pelaksanaan Dunia Sebenar
Grade a document answer on whether every claim is supported by the supplied passage.
Verify generated code through meaningful behavioral tests and review.
Mempraktikkannya
Mengapa ia penting
- Kelajuan dan skala. Aliran kerja bahasa boleh bergerak lebih pantas tanpa mengorbankan konsistensi.
- Akses dan capai. Ia meluaskan akses merentas bahasa dan gaya komunikasi.
- Keputusan yang lebih jelas. Pasukan boleh menghabiskan lebih banyak masa untuk membuat pertimbangan manakala automasi mengendalikan pengulangan.
Berhati-hati untuk
- Fakta halusinasi boleh memasukkan laporan, aliran sokongan atau hasil penyelidikan secara senyap-senyap.
- Sensitiviti segera boleh mencipta hasil yang tidak konsisten merentas permintaan yang serupa.
- Data teks sensitif mungkin terdedah jika kawalan akses lemah.
Langkah seterusnya
- Tentukan format output, nada dan standard kualiti sebelum pelancaran.
- Respons asas dengan sumber yang dipercayai apabila ketepatan penting.
- Simpan pusat pemeriksaan semakan manusia untuk output berkepentingan tinggi.
- Jejaki corak kegagalan dan latih semula gesaan atau aliran kerja dengan kerap.
Sumber dan bacaan lanjut
- Yen and colleaguesHELMET: evaluating long-context language models
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Take the LLM Evaluations quiz
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Soalan lazim
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.
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