seed_data.json
45.1 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
[
{
"id": "seed_001",
"category": "benchmark_framework",
"title": "HELM - Holistic Evaluation of Language Models",
"description": "Stanford CRFM 开发的整体评估框架,覆盖16个核心场景和7大指标类别(准确率、校准、鲁棒性、公平性、偏见、毒性、效率)。",
"source_url": "https://crfm.stanford.edu/helm/lite/latest/#/leaderboard",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["HELM", "LLM", "holistic-evaluation", "stanford"],
"reliability": "A"
},
{
"id": "seed_002",
"category": "benchmark_framework",
"title": "EleutherAI LM Evaluation Harness",
"description": "学术界最广泛使用的开源 benchmark 框架,集成60+任务(MMLU、BigBench、HellaSwag等),支撑 Hugging Face Open LLM Leaderboard。",
"source_url": "https://github.com/EleutherAI/lm-evaluation-harness",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["EleutherAI", "lm-evaluation-harness", "open-source", "python"],
"reliability": "A"
},
{
"id": "seed_003",
"category": "benchmark_framework",
"title": "DeepEval - Pytest for LLMs",
"description": "现代开源 LLM 评估框架,支持 MMLU、BIG-Bench Hard、GSM8K、HumanEval 等 benchmark 的10行代码快速集成,并提供 CI/CD、Red Teaming 功能。",
"source_url": "https://github.com/confident-ai/deepeval",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["DeepEval", "CI-CD", "LLM-evaluation", "red-teaming"],
"reliability": "A"
},
{
"id": "seed_004",
"category": "benchmark_framework",
"title": "OpenAI Evals Framework",
"description": "OpenAI 开源的评估框架,采用 Registry 格式,支持自定义任务扩展,注重结构化的 benchmark 定义。",
"source_url": "https://github.com/openai/evals",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["OpenAI", "evals", "registry", "custom-tasks"],
"reliability": "A"
},
{
"id": "seed_005",
"category": "benchmark_framework",
"title": "OpenCompass 评估体系",
"description": "上海人工智能实验室推出的开源统一评估平台,覆盖大量中英文 benchmark,特别适用于中文大模型评测。",
"source_url": "https://opencompass.org.cn/",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["OpenCompass", "InternLM", "chinese-LLM", "evaluation-platform"],
"reliability": "A"
},
{
"id": "seed_006",
"category": "benchmark_framework",
"title": "RAGAS - RAG Evaluation Framework",
"description": "专为检索增强生成(RAG)设计的评估框架,核心指标包括 Faithfulness、Answer Relevancy、Context Relevancy、Context Recall。",
"source_url": "https://github.com/explodinggradients/ragas",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["RAGAS", "RAG", "retrieval", "LLM-as-judge"],
"reliability": "A"
},
{
"id": "seed_007",
"category": "benchmark_framework",
"title": "BigBench / BigBench-Hard (BBH)",
"description": "Google DeepMind 领衔的大规模协作 benchmark,BigBench 包含数百个多样化的任务;BBH 精选23个复杂推理任务,专门用于测试 LLM 的推理极限。",
"source_url": "https://github.com/google/BIG-bench",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["BigBench", "BBH", "reasoning", "Google"],
"reliability": "A"
},
{
"id": "seed_008",
"category": "benchmark_framework",
"title": "LLM-KG-Bench 3.0",
"description": "面向语义技术与知识图谱工程能力的 benchmark,评估 LLM 生成 RDF/SPARQL 的语法与语义正确性。",
"source_url": "https://arxiv.org/html/2505.13098v1",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["LLM-KG-Bench", "knowledge-graph", "SPARQL", "semantic-web"],
"reliability": "A"
},
{
"id": "seed_009",
"category": "benchmark_framework",
"title": "SimBA - Simplifying Benchmark Analysis",
"description": "基于性能矩阵的元分析框架,能从 HELM、MMLU、BigBenchLite 中发现最小代表性数据集子集,研究表明1.7%-28%的数据集可覆盖95%以上的方差。",
"source_url": "https://arxiv.org/abs/2510.17998",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["SimBA", "meta-analysis", "dataset-reduction", "HELM"],
"reliability": "A"
},
{
"id": "seed_010",
"category": "benchmark_framework",
"title": "Giskard - ML Model Testing",
"description": "开源的 ML 模型测试和 RAG 评估工具,提供偏见、稳健性、数据泄露等自动化检测。",
"source_url": "https://github.com/Giskard-AI/giskard",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["Giskard", "ML-testing", "bias-detection", "robustness"],
"reliability": "B"
},
{
"id": "seed_011",
"category": "benchmark_framework",
"title": "MLflow Benchmarking Orchestrator",
"description": "模块化端到端 ML benchmark 框架,支持 YAML 配置、并发流水线、执行图构建(支持 RAG 等复杂系统的 fork 和 cycle),并与 MLflow 集成记录实验。",
"source_url": "https://robertbayer.github.io/publication/towards-modular-benchmark/towards-modular-benchmark.pdf",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["MLflow", "orchestrator", "modular-benchmark", "YAML"],
"reliability": "A"
},
{
"id": "seed_012",
"category": "benchmark_framework",
"title": "Arize Phoenix / Langfuse / TruLens",
"description": "可观测性优先的 LLM 评估框架组合,用于生产环境的 trace、eval 和 LLM-as-a-judge 评分。",
"source_url": "https://github.com/Arize-ai/phoenix",
"crawl_time": "2026-04-10T08:00:00Z",
"tags": ["Arize-Phoenix", "Langfuse", "TruLens", "observability"],
"reliability": "B"
},
{
"id": "seed_013",
"category": "benchmark_paper",
"title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark",
"description": "MMLU 的增强版本,将选项从4个增加到10个,问题更复杂,降低猜测敏感性和 prompt 敏感性。",
"source_url": "https://arxiv.org/html/2406.01574v4",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["MMLU-Pro", "benchmark", "multi-task", "QA"],
"reliability": "A"
},
{
"id": "seed_014",
"category": "benchmark_paper",
"title": "Automating Benchmark Design (BeTaL)",
"description": "提出使用 LLM-in-the-Loop 自动优化 benchmark 参数的方法,将 benchmark 创建视为可配置的优化问题,能自动调整难度(目标偏差5%-13%)。",
"source_url": "https://arxiv.org/html/2510.25039v1",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["BeTaL", "automated-benchmark", "LLM-designer", "dynamic-benchmark"],
"reliability": "A"
},
{
"id": "seed_015",
"category": "benchmark_paper",
"title": "ReportBench: Evaluating Deep Research Agents",
"description": "从678篇专家撰写的综述论文中自动构建 benchmark,通过解析 LaTeX 源文件提取引用和参考文献,用于评估 AI 生成研究报告的检索精度和事实一致性。",
"source_url": "https://arxiv.org/pdf/2508.15804",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["ReportBench", "research-agents", "LaTeX-parsing", "factuality"],
"reliability": "A"
},
{
"id": "seed_016",
"category": "benchmark_paper",
"title": "TaxoBench-CS: Taxonomy Generation in Computer Science",
"description": "从156篇 CS 综述论文的作者 curated 分类树构建 benchmark,平均74.4篇引用论文、3.1层深度。",
"source_url": "https://arxiv.org/pdf/2509.19125",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["TaxoBench-CS", "taxonomy", "computer-science", "literature"],
"reliability": "A"
},
{
"id": "seed_017",
"category": "benchmark_paper",
"title": "Rethinking Domain-Specific LLM Benchmark Construction",
"description": "关于如何为特定领域(法律、医学等)设计和评估 LLM benchmark 的方法论研究。",
"source_url": "https://arxiv.org/html/2508.07353v1",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["domain-specific", "LLM", "benchmark-design", "methodology"],
"reliability": "A"
},
{
"id": "seed_018",
"category": "benchmark_paper",
"title": "DeepScholar-bench: Generative Research Synthesis",
"description": "活的 benchmark,用于评估 Related Work 部分的生成质量,通过自动筛选 arXiv 论文评估知识综合、检索质量和可验证性。",
"source_url": "https://arxiv.org/pdf/2508.20033",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["DeepScholar", "research-synthesis", "related-work", "generative"],
"reliability": "A"
},
{
"id": "seed_019",
"category": "benchmark_paper",
"title": "Evaluation Metrics and Statistical Tests for Machine Learning",
"description": "Nature Scientific Reports 发表的论文,系统讨论 ML 评估指标和统计检验,强调单测试集 t 检验会低估方差,推荐使用非参数检验。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["statistics", "evaluation-metrics", "nature", "non-parametric"],
"reliability": "A"
},
{
"id": "seed_020",
"category": "benchmark_paper",
"title": "Vendi Score: A Diversity Metric for ML Datasets",
"description": "提出基于成对相似度矩阵 von Neumann 熵的数据集多样性通用指标,适用于图像、文本、分子等多领域。",
"source_url": "https://arxiv.org/pdf/2210.02410",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["Vendi-Score", "diversity", "dataset-quality", "entropy"],
"reliability": "A"
},
{
"id": "seed_021",
"category": "benchmark_paper",
"title": "MAAB: Multimodal AutoML Agent Benchmark",
"description": "评估 agent 处理原始多模态数据的 benchmark,覆盖7种问题类型、5种数据模态,共25个来自 Kaggle/UCI/BEIR 的数据集。",
"source_url": "https://openreview.net/pdf?id=9t7gfYUp0U",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["MAAB", "AutoML", "multimodal", "agent"],
"reliability": "A"
},
{
"id": "seed_022",
"category": "benchmark_paper",
"title": "Datasets, Metrics, Benchmarks and Future Research in Autonomous Driving",
"description": "IEEE/CAA JAS 综述,全面回顾自动驾驶领域的数据集构建、评估指标和 benchmark 演进。",
"source_url": "https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2025.125957",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["autonomous-driving", "survey", "datasets", "metrics"],
"reliability": "A"
},
{
"id": "seed_023",
"category": "benchmark_paper",
"title": "Design Principles for Benchmark Evaluating Multi-agent AI Security Operations",
"description": "明确讨论 multi-agent AI 系统安全运营 benchmark 的构建设计原则。",
"source_url": "https://arxiv.org/abs/2603.28998",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["multi-agent", "security", "benchmark-design", "principles"],
"reliability": "A"
},
{
"id": "seed_024",
"category": "benchmark_paper",
"title": "PCMDE: Physics-Guided Benchmarking for Multimodal Synthetic Images",
"description": "结合物理规则与多模态融合的合成图像评估框架,通过目标级检测、空间/关系规则和轻量 LLM 推理评估结构真实性。",
"source_url": "https://arxiv.org/html/2511.15204v2",
"crawl_time": "2026-04-10T08:05:00Z",
"tags": ["PCMDE", "multimodal", "synthetic-images", "physics-guided"],
"reliability": "A"
},
{
"id": "seed_025",
"category": "annotation_standard",
"title": "Scale AI Quality Control Pipelines",
"description": "Scale AI 提供三种质量流水线:Standard(一次审核)、Consensus(三次审核决出最终结果)、Collection(收集全部标注员响应)。",
"source_url": "https://labelyourdata.com/articles/scale-ai-review",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["Scale-AI", "quality-pipeline", "consensus", "enterprise"],
"reliability": "A"
},
{
"id": "seed_026",
"category": "annotation_standard",
"title": "Appen AI Data Quality Framework",
"description": "Appen 使用全球众包平台 ADAP,核心 QC 包括 Test Questions、Redundancy、Contributor Tiers、Inter-Rater Agreements 和 ISO 9001 合规。",
"source_url": "https://www.appen.com/ai-data-quality",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["Appen", "ADAP", "crowd", "ISO-9001", "IRA"],
"reliability": "A"
},
{
"id": "seed_027",
"category": "annotation_standard",
"title": "Labelbox Ontology Management & Automated QC",
"description": "Labelbox 通过本体管理(Ontology Management)确保标签一致性,提供内置 Benchmark & Consensus 自动化测试,支持 SDK/API 集成。",
"source_url": "https://docs.labelbox.com/",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["Labelbox", "ontology", "automated-QC", "API-first"],
"reliability": "A"
},
{
"id": "seed_028",
"category": "annotation_standard",
"title": "YOLO Dataset & Annotation Best Practices Guide 2024",
"description": "计算机视觉标注最佳实践:锁定 taxonomy、使用紧凑轴对齐框、标注遮挡物、维持类别平衡(每类≥200实例)、冻结数据集划分。",
"source_url": "https://imgmarker.com/blog/yolo-dataset-annotation-best-practices",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["YOLO", "computer-vision", "bounding-box", "best-practices"],
"reliability": "A"
},
{
"id": "seed_029",
"category": "annotation_standard",
"title": "Medical Annotation Best Practices 2024",
"description": "医疗数据标注标准化流程:统一图像尺寸、精确识别类别标签、匿名化 PII、加密通信、确保 HIPAA/GDPR 合规。",
"source_url": "https://research.aimultiple.com/medical-annotation/",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["medical-annotation", "HIPAA", "GDPR", "privacy"],
"reliability": "A"
},
{
"id": "seed_030",
"category": "annotation_standard",
"title": "Label Your Data - Guide to Inhouse Dataset Labeling 2024",
"description": "内部数据集标注指南:收集要求与 golden benchmark、培训标注员、试点标注、多级验证、随机抽样与交叉验证 QA。",
"source_url": "https://labelyourdata.com/pdf/Guide%20to%20Inhouse%20Dataset%20Labeling.pdf",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["guide", "inhouse-labeling", "pilot", "multi-level-QA"],
"reliability": "A"
},
{
"id": "seed_031",
"category": "annotation_standard",
"title": "Data Annotation Best Practices for Successful ML",
"description": "强调将数据标注视为严格的迭代 QA 流程,包含视觉示例、术语表、golden dataset 交叉引用和持续更新的 Living Guidelines。",
"source_url": "https://keylabs.ai/blog/data-annotation-best-practices-for-successful-machine-learning/",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["best-practices", "QA", "living-guidelines", "glossary"],
"reliability": "A"
},
{
"id": "seed_032",
"category": "annotation_standard",
"title": "Appen Quality Assurance in AI Blog",
"description": "Appen 的 AI 质量保证博客,介绍大型 benchmark 项目经验(50万+标注)以及 contributor level 和 inter-rater agreement 的实施细节。",
"source_url": "https://www.appen.com/blog/quality-assurance-in-ai",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["Appen", "QA", "benchmark-project", "500K-annotations"],
"reliability": "A"
},
{
"id": "seed_033",
"category": "annotation_standard",
"title": "Human-in-the-Loop Data Annotation",
"description": "人机协同标注方法:自动化处理简单样本,将低置信度和边界案例路由给专家,降低成本并提高数据集多样性。",
"source_url": "https://xonique.dev/blog/data-annotation-why-it-matters-and-best-practices/",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["HITL", "human-in-the-loop", "active-learning", "diversity"],
"reliability": "B"
},
{
"id": "seed_034",
"category": "annotation_standard",
"title": "Computer Vision for Plant Phenotyping - Annotation Best Practices",
"description": "生物学表型数据注释:在最少的图像数量中最大化样本多样性,强调训练数据中对象实例数量比原始图像数量更重要。",
"source_url": "https://pdfs.semanticscholar.org/46da/25493f3771ca395349b42d86cf67c6aa8b99.pdf",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["plant-phenotyping", "diversity", "instance-count", "biology"],
"reliability": "A"
},
{
"id": "seed_035",
"category": "annotation_standard",
"title": "AI Standards for Data Annotation - Restackio",
"description": "汇总 AI 驱动数据标注的行业标准,涵盖质量控制流程、一致性协议和自动化验证方法。",
"source_url": "https://www.restack.io/p/ai-driven-data-labeling-answer-ai-standards-cat-ai",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["AI-standards", "data-labeling", "automation", "quality-control"],
"reliability": "B"
},
{
"id": "seed_036",
"category": "annotation_standard",
"title": "Datasaur - Krippendorff's Alpha IAA Calculation Guide",
"description": "Datasaur 发布的 Krippendorff's Alpha 与 Cohen's Kappa 对比指南,解释何时使用何种 IAA 指标。",
"source_url": "https://datasaur.ai/blog-posts/inter-annotator-agreement-krippendorff-cohen",
"crawl_time": "2026-04-10T08:10:00Z",
"tags": ["Datasaur", "Krippendorff-Alpha", "Cohen-Kappa", "IAA"],
"reliability": "A"
},
{
"id": "seed_037",
"category": "scoring_rubric",
"title": "LLM Output Quality Analytic Rubric Template",
"description": "推荐的 LLM 输出分析型评分量表模板,包含准确性(30%)、相关性(25%)、连贯性(20%)、完整性(15%)、安全性(10%)五个维度。",
"source_url": "https://openreview.net/pdf?id=rKOJ59LuyI",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["rubric", "analytic", "LLM-evaluation", "weighted-scoring"],
"reliability": "A"
},
{
"id": "seed_038",
"category": "scoring_rubric",
"title": "EduVisBench Pedagogical Visualization Rubric",
"description": "教育可视化 benchmark 的5维评分标准:Context Visualization、Diagram Design、Text-Graphic Integration、Thought Guidance、Interactivity。",
"source_url": "https://arxiv.org/pdf/2505.16832",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["EduVisBench", "visualization", "pedagogy", "GPT-4o-evaluation"],
"reliability": "A"
},
{
"id": "seed_039",
"category": "scoring_rubric",
"title": "ProfBench - Professional LLM Rubrics",
"description": "专业领域 LLM 任务评估 rubric,采用领域特定维度,以任务重要性加权,评估提取召回率和报告生成质量。",
"source_url": "https://www.emergentmind.com/articles/2510.18941",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["ProfBench", "professional-tasks", "rubric", "importance-weighted"],
"reliability": "A"
},
{
"id": "seed_040",
"category": "scoring_rubric",
"title": "Xilankapu Design Evaluation AHP Weights",
"description": "使用层次分析法(AHP)确定的文化遗产设计评估权重:Innovation 0.30、Cultural Relevance 0.30、Artistic Quality 0.20、Practicality 0.20,CR=0.036。",
"source_url": "http://apjcriweb.org/content/vol11no5/26.pdf",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["AHP", "cultural-heritage", "weight-allocation", "consistency-ratio"],
"reliability": "A"
},
{
"id": "seed_041",
"category": "scoring_rubric",
"title": "Teacher Hiring Criteria Point System",
"description": "菲律宾教师 hiring 的7维评分体系示例:Content Knowledge、Learning Environment、Diversity、Curriculum Planning、Assessment、Community、Personal Growth。",
"source_url": "https://www.education.gov.pg/syllabus/secondarysyllabus/teacherguide/Business%20Studies%20Grade%209%20-%20TG.pdf",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["hiring-rubric", "teacher-evaluation", "point-system", "7-dimensions"],
"reliability": "B"
},
{
"id": "seed_042",
"category": "scoring_rubric",
"title": "Rubric Best Practices - NCSU DELTA",
"description": "北卡州立大学教学资源中心总结的 rubric 设计最佳实践,区分 Holistic、Analytic、Single-Point 三种量表类型及其适用场景。",
"source_url": "https://teaching-resources.delta.ncsu.edu/rubric_best-practices-examples-templates/",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["NCSU", "rubric-types", "holistic", "analytic", "teaching"],
"reliability": "A"
},
{
"id": "seed_043",
"category": "scoring_rubric",
"title": "12-Step Rubric Development Process",
"description": "PNG 教育部提出的12步 rubric 开发流程:从列出任务组件、确定 benchmark 目的、拆分次要任务、提取 KSAV 到分配和验证权重。",
"source_url": "https://www.education.gov.pg/syllabus/secondarysyllabus/teacherguide/Business%20Studies%20Grade%209%20-%20TG.pdf",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["rubric-development", "12-step", "KSAV", "validation"],
"reliability": "B"
},
{
"id": "seed_044",
"category": "scoring_rubric",
"title": "Turnitin - Types of Rubrics and Grading Forms",
"description": "Turnitin 系统的 rubric 类型说明:百分比加权(Differential Weighting)要求各维度权重之和严格等于100%。",
"source_url": "https://guides.turnitin.com/hc/en-us/articles/35522022970765--New-Types-of-rubrics-and-grading-forms",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["Turnitin", "percentage-weighting", "grading-forms", "sum-100"],
"reliability": "A"
},
{
"id": "seed_045",
"category": "scoring_rubric",
"title": "OpenReview LLM Evaluation Rubric Instructions",
"description": "OpenReview 对 LLM 评估的严格要求:维度必须与问题强耦合、至少一个维度得分≤8、评分需提供直接引用。",
"source_url": "https://openreview.net/pdf?id=rKOJ59LuyI",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["OpenReview", "strict-rules", "evidence-based", "ceiling-prevention"],
"reliability": "A"
},
{
"id": "seed_046",
"category": "scoring_rubric",
"title": "Sotopia Evaluation Dimension Concepts",
"description": "Sotopia 多智能体社交模拟平台的评估维度概念文档,定义评估维度的设计哲学。",
"source_url": "https://docs.sotopia.world/concepts/evaluation_dimension",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["Sotopia", "multi-agent", "social-simulation", "evaluation-dimension"],
"reliability": "B"
},
{
"id": "seed_047",
"category": "scoring_rubric",
"title": "How to Weight Rubrics - University of Northern Iowa",
"description": " rubric 权重分配系列教程的第四部分,系统讲解如何为分析型量表分配权重。",
"source_url": "https://chfasoa.uni.edu/howtoweightrubrics.pdf",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["rubric-weighting", "University-of-Northern-Iowa", "analytic-rubric"],
"reliability": "A"
},
{
"id": "seed_048",
"category": "scoring_rubric",
"title": "Designing Scoring Rubrics for Your Classroom - Learner.org",
"description": "系统介绍 Holistic、Analytic、Single-Point 三种 rubric 架构,推荐复杂任务使用 Analytic Rubric 以支持差分权重。",
"source_url": "https://www.learner.org/wp-content/uploads/2019/02/s7_rubrics.pdf",
"crawl_time": "2026-04-10T08:15:00Z",
"tags": ["Learner.org", "scoring-rubrics", "classroom", "analytic"],
"reliability": "A"
},
{
"id": "seed_049",
"category": "evaluation_metric",
"title": "Microsoft LLM Evaluation Metrics Guide",
"description": "微软 AI Playbook 中系统总结的 LLM 评估指标列表,涵盖 RAGAS 指标(Faithfulness、Answer Relevancy、Context Relevancy、Context Recall)等。",
"source_url": "https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/evaluation/list-of-eval-metrics",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["Microsoft", "LLM-evaluation", "RAGAS", "metrics-guide"],
"reliability": "A"
},
{
"id": "seed_050",
"category": "evaluation_metric",
"title": "Standard Classification Metrics",
"description": "标准分类评估指标:Confusion Matrix、Accuracy、Precision、Recall、F1-Score、F0.5-Score、AUC-ROC、Log Loss。",
"source_url": "https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["classification", "F1", "AUC-ROC", "confusion-matrix"],
"reliability": "A"
},
{
"id": "seed_051",
"category": "evaluation_metric",
"title": "Standard Regression Metrics",
"description": "标准回归评估指标:MAE、MSE、RMSE、RMSLE、R²、Adjusted R²。",
"source_url": "https://www.geeksforgeeks.org/machine-learning/metrics-for-machine-learning-model/",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["regression", "MAE", "RMSE", "R-squared"],
"reliability": "A"
},
{
"id": "seed_052",
"category": "evaluation_metric",
"title": "RAGAS Metrics for Retrieval-Augmented Generation",
"description": "RAG 系统专用的4大核心指标:Faithfulness(事实一致性)、Answer Relevancy(答案相关性)、Context Relevancy(上下文相关性)、Context Recall(上下文召回)。",
"source_url": "https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/evaluation/list-of-eval-metrics",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["RAGAS", "RAG", "faithfulness", "relevancy", "recall"],
"reliability": "A"
},
{
"id": "seed_053",
"category": "evaluation_metric",
"title": "IoU for Object Detection and Segmentation",
"description": "目标检测和图像分割中广泛使用的交并比(Intersection over Union)指标,用于衡量预测框与真实框的空间重叠度。",
"source_url": "https://ar5iv.labs.arxiv.org/html/1912.10107",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["IoU", "object-detection", "segmentation", "computer-vision"],
"reliability": "A"
},
{
"id": "seed_054",
"category": "evaluation_metric",
"title": "Span-Level F1 for NER and Sequence Labeling",
"description": "命名实体识别和序列标注任务中,使用跨度级别的 Precision 和 Recall 计算 F1,比 Kappa 更适合多标签和边界检测任务。",
"source_url": "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1766899/full",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["span-F1", "NER", "sequence-labeling", "precision-recall"],
"reliability": "A"
},
{
"id": "seed_055",
"category": "evaluation_metric",
"title": "DPP Benchmark Score Scaling",
"description": "Data Preprocessing Benchmark 中采用的评分缩放方法:将方法影响缩放至 -2(最差)到 +2(最好),0 表示无影响,便于跨数据集比较。",
"source_url": "https://d-nb.info/1247171701/34",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["DPP", "preprocessing", "score-scaling", "comparability"],
"reliability": "A"
},
{
"id": "seed_056",
"category": "evaluation_metric",
"title": "Maintainability Benchmarking Metrics",
"description": "软件可维护性 benchmark 中使用的指标:Accuracy、Precision、Recall、F1、F0.5(对假阳性惩罚更重,适用于责任预测)。",
"source_url": "https://arxiv.org/html/2408.10754v1",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["maintainability", "F0.5", "false-positive", "software-engineering"],
"reliability": "A"
},
{
"id": "seed_057",
"category": "evaluation_metric",
"title": "Cross-Validation as Robustness Check",
"description": "将交叉验证作为模型选择和泛化能力的稳健性检查方法,配合非参数统计检验使用效果更佳。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["cross-validation", "robustness", "generalization", "model-selection"],
"reliability": "A"
},
{
"id": "seed_058",
"category": "evaluation_metric",
"title": "Expected Calibration Error (ECE)",
"description": "HELM 框架中使用的校准度指标,衡量模型预测置信度与实际准确率之间的差异。",
"source_url": "https://crfm.stanford.edu/helm/lite/latest/#/leaderboard",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["ECE", "calibration", "HELM", "confidence"],
"reliability": "A"
},
{
"id": "seed_059",
"category": "evaluation_metric",
"title": "BLEU and ROUGE for Generation Tasks",
"description": "文本生成任务的经典 n-gram 重叠指标:BLEU 侧重于精确率,ROUGE 侧重于召回率。",
"source_url": "https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["BLEU", "ROUGE", "text-generation", "n-gram"],
"reliability": "A"
},
{
"id": "seed_060",
"category": "evaluation_metric",
"title": "Gini Coefficient and Log Loss",
"description": "分类任务中的概率评估指标:Log Loss 惩罚错误且自信的预测;Gini Coefficient 在 Kaggle 竞赛中常用于评估排序能力。",
"source_url": "https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/",
"crawl_time": "2026-04-10T08:20:00Z",
"tags": ["Log-Loss", "Gini", "probability", "ranking"],
"reliability": "A"
},
{
"id": "seed_061",
"category": "quality_control_method",
"title": "Gold Standard Benchmark Injection",
"description": "将预先由专家确认正确答案的黄金任务随机插入标注队列,持续测量标注员准确率。Scale AI 和 Labelbox 均将其作为核心 QC 手段。",
"source_url": "https://labelyourdata.com/articles/scale-ai-review",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["gold-standard", "benchmark-injection", "accuracy-monitoring"],
"reliability": "A"
},
{
"id": "seed_062",
"category": "quality_control_method",
"title": "Inter-Annotator Agreement (IAA) - Krippendorff's Alpha",
"description": "最通用的标注一致性统计指标,支持任意数量标注员、缺失数据和多类型数据(名义/序数/区间/比率),接受阈值 α > 0.8。",
"source_url": "https://datasaur.ai/blog-posts/inter-annotator-agreement-krippendorff-cohen",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["IAA", "Krippendorff-Alpha", "missing-data", "multi-rater"],
"reliability": "A"
},
{
"id": "seed_063",
"category": "quality_control_method",
"title": "Cohen's Kappa for Pairwise Agreement",
"description": "纠正偶然一致性的两名标注员配对指标,适用于二分类和名义数据,不适用于序列标注或多标签任务。",
"source_url": "https://datasaur.ai/blog-posts/inter-annotator-agreement-krippendorff-cohen",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["Cohen-Kappa", "pairwise", "chance-correction", "nominal"],
"reliability": "A"
},
{
"id": "seed_064",
"category": "quality_control_method",
"title": "Fleiss' Kappa for Multiple Raters",
"description": "Cohen's Kappa 的多人扩展版本,适用于3名及以上标注员的名义数据一致性评估。",
"source_url": "https://datasaur.ai/blog-posts/inter-annotator-agreement-krippendorff-cohen",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["Fleiss-Kappa", "multiple-raters", "nominal-data"],
"reliability": "A"
},
{
"id": "seed_065",
"category": "quality_control_method",
"title": "Consensus and Majority Voting",
"description": "将同一任务分配给多名标注员,通过投票或一致性算法确定最终标签。Appen 和 Labelbox 都提供自动化 consensus 工具。",
"source_url": "https://www.appen.com/ai-data-quality",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["consensus", "majority-voting", "multi-annotation", "Labelbox"],
"reliability": "A"
},
{
"id": "seed_066",
"category": "quality_control_method",
"title": "Rater Vitality (Vi) Metric",
"description": "从 Krippendorff's Alpha 衍生的个体标注员影响力指标:Vi = Kα - Ki(去掉该标注员后的 Alpha)。正值表示提升共识,负值表示降低共识。",
"source_url": "https://ar5iv.labs.arxiv.org/html/1912.10107",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["Rater-Vitality", "annotator-quality", "Krippendorff", "outlier-detection"],
"reliability": "A"
},
{
"id": "seed_067",
"category": "quality_control_method",
"title": "Class Recognition Difficulty Analysis",
"description": "基于 K-alpha 的类内一致性分析,识别哪些类别 inherently difficult to identify,从而优化标注指南或拆分任务。",
"source_url": "https://ar5iv.labs.arxiv.org/html/1912.10107",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["class-difficulty", "K-alpha", "category-analysis", "guideline-improvement"],
"reliability": "A"
},
{
"id": "seed_068",
"category": "quality_control_method",
"title": "Two-Layer Review Cycle",
"description": "Scale AI 采用的双层审核机制:第一层为初级标注员,第二层为资深审核员,负责补充遗漏标注和修正错误。",
"source_url": "https://labelyourdata.com/articles/scale-ai-review",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["two-layer-review", "Scale-AI", "senior-auditor", "error-correction"],
"reliability": "A"
},
{
"id": "seed_069",
"category": "quality_control_method",
"title": "Continuous QA with Error Trend Analysis",
"description": "Appen 的 AI 辅助 QA 框架,通过 Model Mate 和质量报告框架进行标签级报告和错误趋势分析,实现贯穿训练数据开发生命周期的持续 QA。",
"source_url": "https://www.appen.com/blog/quality-assurance-in-ai",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["continuous-QA", "error-trend", "Appen", "Model-Mate"],
"reliability": "A"
},
{
"id": "seed_070",
"category": "quality_control_method",
"title": "Pilot Annotation and Norming Sessions",
"description": "在正式大规模标注前进行小规模试点,通过同行评审和规范化会议(norming sessions)校准评分标准,修订存在分歧的描述符。",
"source_url": "https://teaching-resources.delta.ncsu.edu/rubric_best-practices-examples-templates/",
"crawl_time": "2026-04-10T08:25:00Z",
"tags": ["pilot", "norming-session", "calibration", "rubric-revision"],
"reliability": "A"
},
{
"id": "seed_071",
"category": "baseline_calculation",
"title": "Random Baseline for Multiple-Choice Benchmarks",
"description": "多选题 benchmark 的随机基线计算方法。以 MMLU 为例,每题4个选项,随机猜测的期望准确率为25%。",
"source_url": "https://arxiv.org/html/2406.01574v4",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["random-baseline", "MMLU", "multiple-choice", "25-percent"],
"reliability": "A"
},
{
"id": "seed_072",
"category": "baseline_calculation",
"title": "Weak Baseline: TF-IDF + Logistic Regression",
"description": "使用简单模型作为弱基线,在 benchmark 上独立训练,用于验证任务的非平凡性和数据的可学习性。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["weak-baseline", "TF-IDF", "logistic-regression", "learnability"],
"reliability": "A"
},
{
"id": "seed_073",
"category": "baseline_calculation",
"title": "Strong Baseline: GPT-4o and Claude 3.5 Sonnet",
"description": "使用当前 SOTA 商业模型作为强基线,采用标准 prompt 和固定温度参数(通常 temperature=0),确保结果可复现。",
"source_url": "https://crfm.stanford.edu/helm/lite/latest/#/leaderboard",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["strong-baseline", "GPT-4o", "Claude", "SOTA"],
"reliability": "A"
},
{
"id": "seed_074",
"category": "baseline_calculation",
"title": "Human Baseline via Expert Annotators",
"description": "招募专业领域的人类标注员完成相同任务,其平均表现作为人类基线。常用于医疗、法律等专业 benchmark。",
"source_url": "https://research.aimultiple.com/medical-annotation/",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["human-baseline", "expert-annotator", "medical", "legal"],
"reliability": "A"
},
{
"id": "seed_075",
"category": "baseline_calculation",
"title": "Relative Gain Formula",
"description": "评估新模型相对提升的标准化公式:Relative Gain = (Model - Baseline) / (Human - Baseline) × 100%,用于消除不同 benchmark 的绝对分差异。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["relative-gain", "normalization", "model-improvement", "formula"],
"reliability": "A"
},
{
"id": "seed_076",
"category": "baseline_calculation",
"title": "Wilcoxon Signed-Rank Test for Model Comparison",
"description": "推荐使用的配对非参数统计检验,避免了单测试集 t 检验低估方差的问题,适用于多次交叉验证后的模型对比。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["Wilcoxon", "non-parametric", "model-comparison", "paired-test"],
"reliability": "A"
},
{
"id": "seed_077",
"category": "baseline_calculation",
"title": "Friedman Test for Multi-Model Comparison",
"description": "当比较3个及以上模型时使用的非参数检验,配合 Nemenyi post-hoc 检验可揭示模型间的显著性差异。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["Friedman-test", "multi-model", "post-hoc", "Nemenyi"],
"reliability": "A"
},
{
"id": "seed_078",
"category": "baseline_calculation",
"title": "Bootstrap Resampling for Confidence Intervals",
"description": "通过有放回重采样构建模型表现的置信区间,不依赖正态分布假设,适用于小样本或复杂分布的 benchmark 结果。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["bootstrap", "confidence-interval", "resampling", "small-sample"],
"reliability": "A"
},
{
"id": "seed_079",
"category": "baseline_calculation",
"title": "Contamination Check and Temporal Cutoff",
"description": "benchmark 基线计算前必须进行的污染检查:记录训练数据截止日期,排除测试集在训练数据中公开出现的情况。",
"source_url": "https://arxiv.org/html/2510.25039v1",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["contamination-check", "temporal-cutoff", "data-leakage", "benchmark-integrity"],
"reliability": "A"
},
{
"id": "seed_080",
"category": "baseline_calculation",
"title": "DPP Benchmark Composite Score",
"description": "数据预处理 benchmark 中的综合得分方法:单独应用每种预处理方法,在固定 Random Forest 上测量相对于基线的性能影响。",
"source_url": "https://d-nb.info/1247171701/34",
"crawl_time": "2026-04-10T08:30:00Z",
"tags": ["DPP", "composite-score", "preprocessing", "Random-Forest"],
"reliability": "A"
},
{
"id": "seed_081",
"category": "dataset_construction",
"title": "Vendi Score as Dataset Diversity Metric",
"description": "基于成对相似度矩阵的 von Neumann 熵的无监督多样性指标,定义为相似度矩阵的熵指数,可跨图像、文本、分子等领域使用。",
"source_url": "https://arxiv.org/pdf/2210.02410",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["Vendi-Score", "dataset-diversity", "von-Neumann-entropy", "reference-free"],
"reliability": "A"
},
{
"id": "seed_082",
"category": "dataset_construction",
"title": "Controlled Sourcing from Kaggle, UCI, and BEIR",
"description": "从权威公开仓库选择数据集,确保缺乏直接开源解决方案,以防止测试集在执行前被污染。MAAB benchmark 的数据选择策略。",
"source_url": "https://openreview.net/pdf?id=9t7gfYUp0U",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["controlled-sourcing", "Kaggle", "UCI", "BEIR", "contamination-prevention"],
"reliability": "A"
},
{
"id": "seed_083",
"category": "dataset_construction",
"title": "Synthetic Data Evaluation with Physics-Guided Rules",
"description": "现代 generative model 评估不仅依赖 BLEU/CLIPScore,还需结合物理规则、上下文关系和基于规则的正确性检查,以识别物理不一致但视觉真实的样本。",
"source_url": "https://arxiv.org/html/2511.15204v2",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["synthetic-data", "physics-guided", "contextual-correctness", "beyond-BLEU"],
"reliability": "A"
},
{
"id": "seed_084",
"category": "dataset_construction",
"title": "Mixed-Methods Design for Complex Domain Benchmarks",
"description": "网络安全等领域的 benchmark 构建采用分阶段混合方法:文献综述 → 数据集选择 → 算法实现 → 模型训练/验证 → 评估 → 框架开发 → 仿真。",
"source_url": "https://ijcaonline.org/archives/volume187/number14/mahmud-2025-ijca-925147.pdf",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["mixed-methods", "cybersecurity", "phased-design", "simulation"],
"reliability": "A"
},
{
"id": "seed_085",
"category": "dataset_construction",
"title": "Metrology Quality Framework for Trustworthy ML",
"description": "INRiM / Mathmet 提出的 ML 训练/验证/测试数据集规范指南,涵盖质量、平衡性、偏见、增强和预处理,以及标准化接口。",
"source_url": "https://www.inrim.it/sites/default/files/2024-02/SRA_EMN_Mathmet_Version_1.0_web_2.pdf",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["metrology", "trustworthy-ML", "standardization", "quality-framework"],
"reliability": "A"
},
{
"id": "seed_086",
"category": "dataset_construction",
"title": "LLM-in-the-Loop for Dynamic Benchmark Refresh",
"description": "动态 benchmark 使用 LLM 自动生成新的问题和任务,再通过人类审核,实现 benchmark 的自动更新和难度调制。",
"source_url": "https://www.emergentmind.com/topics/dynamic-benchmark-methodology",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["LLM-in-the-loop", "dynamic-benchmark", "auto-refresh", "difficulty-control"],
"reliability": "A"
},
{
"id": "seed_087",
"category": "dataset_construction",
"title": "Train/Validation/Test Split Ratio Best Practices",
"description": "常见的数据集拆分比例:70/15/15 适用于中等规模数据;80/10/10 适用于大数据;医学或小样本领域常用 60/20/20 或交叉验证。",
"source_url": "https://www.nature.com/articles/s41598-024-56706-x",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["data-split", "train-test-validation", "cross-validation", "best-practices"],
"reliability": "A"
},
{
"id": "seed_088",
"category": "dataset_construction",
"title": "Class Balance Maintenance in CV Datasets",
"description": "计算机视觉数据集构建中,建议每类至少200个实例。对于稀有类,使用合成增强(synthetic augmentation)来维持类别平衡。",
"source_url": "https://imgmarker.com/blog/yolo-dataset-annotation-best-practices",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["class-balance", "synthetic-augmentation", "computer-vision", "200-instances"],
"reliability": "A"
},
{
"id": "seed_089",
"category": "dataset_construction",
"title": "Maximal Diversity with Minimal Images",
"description": "生物学表型领域的数据集设计原则:ground-truth 数据应代表最大的样本多样性,同时使用最少的图像数量,以减少人工标注和计算成本。",
"source_url": "https://pdfs.semanticscholar.org/46da/25493f3771ca395349b42d86cf67c6aa8b99.pdf",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["maximal-diversity", "minimal-images", "cost-reduction", "phenotyping"],
"reliability": "A"
},
{
"id": "seed_090",
"category": "dataset_construction",
"title": "Versioning and Dataset Artifact Release",
"description": "负责任 benchmark 发布要求:开放代码、标准化评估 split、公开数据集 artifacts,并显式版本化以追踪污染和耗尽指标。",
"source_url": "https://www.emergentmind.com/topics/dynamic-benchmark-methodology",
"crawl_time": "2026-04-10T08:35:00Z",
"tags": ["versioning", "artifact-release", "open-science", "reproducibility"],
"reliability": "A"
}
]