गाइड से जुड़ी प्रश्नोत्तरी · मध्यम स्तर
एलएलएम मूल्यांकन प्रश्नोत्तरी
कार्य मानदंड, तैनात कॉन्फ़िगरेशन, स्कोरिंग रूब्रिक्स, जज सत्यापन, कठिन मामले, त्रुटि गंभीरता और प्रतिगमन उदाहरणों का आकलन करें।
प्रश्न 1 का 8
What should an LLM evaluation measure?
प्रत्येक प्रश्न का पूर्वावलोकन करें8 प्रश्न
जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।
- What should an LLM evaluation measure?
- A defined task and its relevant failure conditions
- Only whether the response sounds fluent
- The model’s parameter count alone
- Whether a benchmark is popular
- Why evaluate the system configuration users actually receive?
- The base model cannot produce text
- Prompts, retrieval, tools, and call limits can change behavior
- The application always scores higher
- A deployed system cannot be tested
- When can exact matching be an appropriate evaluation check?
- For every open-ended answer
- Only when reviewers disagree
- For some extracted fields or executable tests
- To measure response cost
- What helps different reviewers apply an interpretive rubric consistently?
- Hide the scoring criteria
- Use a single preference score
- Remove examples that are difficult to judge
- Write the rubric clearly and examine reviewer disagreements
- How should a team validate a model used to grade outputs?
- Check it against independently reviewed examples and inspect order or style effects
- Treat its score as independent proof
- Ask the same model to approve itself
- Use only its average score
- Which cases may be useful to include when relevant to the application?
- Only short questions with one obvious answer
- Unanswerable questions, conflicting sources, long-context cases, and malicious retrieved instructions
- Only prompts used during development
- No difficult examples
- How should evaluation results be reported?
- As one preference number without context
- Only by model size
- By task and error severity
- As a claim that uncertainty is eliminated
- What should a team do with failed examples?
- Discard them after calculating a score
- Add them to prompts until every case passes
- Treat them as proof of general performance
- Retain them as regression cases and refresh held-out material over time