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| 1 |
+
---
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| 2 |
+
license: cc-by-sa-4.0
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| 3 |
+
task_categories:
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| 4 |
+
- question-answering
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| 5 |
+
- text-generation
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| 6 |
+
language:
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| 7 |
+
- en
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| 8 |
+
tags:
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| 9 |
+
- benchmark
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| 10 |
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- professional
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| 11 |
+
- expert
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| 12 |
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- reasoning
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| 13 |
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- law
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| 14 |
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- medicine
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| 15 |
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- finance
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| 16 |
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- cybersecurity
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| 17 |
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- engineering
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| 18 |
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- llm-evaluation
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| 19 |
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- multiple-choice
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| 20 |
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size_categories:
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| 21 |
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- 1K<n<10K
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| 22 |
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---
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| 23 |
+
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| 24 |
+
# ProBench
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| 25 |
+
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| 26 |
+
A 4-choice multiple-choice benchmark of **529 expert-level questions** across five professional domains, sourced entirely from Stack Exchange communities (CC-BY-SA 4.0). Designed to evaluate LLMs where standard benchmarks (MMLU, HumanEval, HellaSwag) are now saturated above 90%.
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| 27 |
+
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| 28 |
+
## Why ProBench?
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| 29 |
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| 30 |
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| Benchmark | Frontier model score | Status |
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| 31 |
+
|---|---|---|
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| 32 |
+
| MMLU | >90% | Saturated |
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| 33 |
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| HellaSwag | >95% | Saturated |
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| 34 |
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| HumanEval | >90% | Saturated |
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| 35 |
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| **ProBench** | TBD | Active |
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| 36 |
+
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| 37 |
+
Questions are sourced from real professional practitioners. Correct answers are community-verified (Stack Exchange accepted answers with high vote scores). Distractors are real expert-written text from the same domain — not synthetically generated.
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| 38 |
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| 39 |
+
## Dataset Structure
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| 40 |
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| 41 |
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### Splits
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| 42 |
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| 43 |
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| Split | Count |
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| 44 |
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|---|---|
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| 45 |
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| train | 370 |
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| 46 |
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| validation | 79 |
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| 47 |
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| test | 80 |
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| 48 |
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| **Total** | **529** |
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| 49 |
+
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| 50 |
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### By Domain
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| 51 |
+
|
| 52 |
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| Domain | Source Community | Items |
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| 53 |
+
|---|---|---|
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| 54 |
+
| Cybersecurity | security.stackexchange.com | 148 |
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| 55 |
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| Law | law.stackexchange.com | 121 |
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| 56 |
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| Medicine | medicalsciences.stackexchange.com | 63 |
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| 57 |
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| Engineering | engineering.stackexchange.com | 113 |
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| 58 |
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| Finance | quant.stackexchange.com | 84 |
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| 59 |
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| 60 |
+
### Record Format
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| 61 |
+
|
| 62 |
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```json
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| 63 |
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{
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| 64 |
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"id": "cybersecurity_29988",
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| 65 |
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"domain": "cybersecurity",
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| 66 |
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"question_title": "What is certificate pinning?",
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| 67 |
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"question_body": "I've recently seen ...",
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| 68 |
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"question_score": 373,
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| 69 |
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"question_tags": ["tls", "certificates", "public-key-infrastructure"],
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| 70 |
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"choices": {
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| 71 |
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"A": "You can roll your own, but you probably will make a major security...",
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| 72 |
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"B": "Software is too complex. This is by far the most important factor...",
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| 73 |
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"C": "The known_hosts file lets the client authenticate the server...",
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| 74 |
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"D": "Typically certificates are validated by checking the signature hierarchy..."
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| 75 |
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},
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| 76 |
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"answer": "D",
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| 77 |
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"distractor_source": "same_domain_answer_pool",
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| 78 |
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"source": "stackexchange",
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| 79 |
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"license": "CC-BY-SA 4.0",
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| 80 |
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"url": "https://security.stackexchange.com/questions/29988/..."
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| 81 |
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}
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| 82 |
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```
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| 83 |
+
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| 84 |
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## Evaluation
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| 85 |
+
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| 86 |
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Evaluation is exact-match (0 or 1 per question) — fully automated, no human judge required.
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| 87 |
+
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| 88 |
+
```python
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| 89 |
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from datasets import load_dataset
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| 90 |
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| 91 |
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ds = load_dataset("lin99/ProBench", split="test")
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| 92 |
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| 93 |
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def evaluate(model, dataset):
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| 94 |
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correct = 0
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| 95 |
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for row in dataset:
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| 96 |
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prompt = f"""Domain: {row['domain']}
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| 97 |
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| 98 |
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Question: {row['question_title']}
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| 99 |
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| 100 |
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{row['question_body']}
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| 101 |
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| 102 |
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A. {row['choices']['A']}
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| 103 |
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B. {row['choices']['B']}
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| 104 |
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C. {row['choices']['C']}
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| 105 |
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D. {row['choices']['D']}
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| 106 |
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| 107 |
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Answer (A/B/C/D):"""
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| 108 |
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prediction = model(prompt).strip()[0].upper()
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| 109 |
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if prediction == row["answer"]:
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| 110 |
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correct += 1
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| 111 |
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return correct / len(dataset)
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| 112 |
+
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| 113 |
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accuracy = evaluate(your_model, ds)
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| 114 |
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print(f"ProBench accuracy: {accuracy:.1%}")
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| 115 |
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```
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| 116 |
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| 117 |
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## Quality Filters
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| 118 |
+
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| 119 |
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| Filter | Threshold |
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| 120 |
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|---|---|
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| 121 |
+
| Minimum question score | 10 upvotes |
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| 122 |
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| Minimum answer score | 5 upvotes |
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| 123 |
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| Accepted answers only | Yes |
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| 124 |
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| Minimum answer length | 30 words |
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| 125 |
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| Excluded question types | lifestyle, resource lists, book recommendations, tooling |
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| 126 |
+
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| 127 |
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## Data Collection
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| 128 |
+
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| 129 |
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- **Source**: Stack Exchange API + data dump (archive.org, 2024-12-31 release)
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| 130 |
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- **License**: CC-BY-SA 4.0 — free to use with attribution
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| 131 |
+
- **Distractors**: Real expert-written answers from other questions in the same domain
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| 132 |
+
- **No synthetic generation**: every word in every answer comes from a real human expert
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| 133 |
+
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| 134 |
+
## Citation
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| 135 |
+
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| 136 |
+
```bibtex
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| 137 |
+
@dataset{probench2025,
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| 138 |
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title = {ProBench: Expert-Level MCQ Benchmark across 5 Professional Domains},
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| 139 |
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year = {2025},
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| 140 |
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note = {Sourced from Stack Exchange communities (CC-BY-SA 4.0)},
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| 141 |
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url = {https://huggingface.co/datasets/lin99/ProBench}
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| 142 |
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}
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| 143 |
+
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| 144 |
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@misc{stackexchange_dump2024,
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| 145 |
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title = {Stack Exchange Data Dump},
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| 146 |
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author = {{Stack Exchange, Inc.}},
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| 147 |
+
year = {2024},
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| 148 |
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url = {https://archive.org/details/stackexchange},
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| 149 |
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note = {Licensed under CC-BY-SA 4.0}
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| 150 |
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}
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| 151 |
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```
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| 152 |
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| 153 |
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## License
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| 154 |
+
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| 155 |
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All content is from Stack Exchange and licensed under **CC-BY-SA 4.0**.
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| 156 |
+
Attribution to Stack Exchange communities is required. Each record includes the original URL.
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