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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Open accessAbstract
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com
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“Both metrics assume short, span-extractable answers, an assumption fundamentally violated by Think-class models that produce reasoning traces often running to several hundred tokens.”
From When Context Overwhelms: A Study of Long-Context vs. Retrieval-Based QA Under Noise (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We use SQuAD 1.1 which excludes the unanswerable questions added in v.2.0 that are deliberately constructed to induce mistakes so they do not in practice cause confabulations to occur.”
From Detecting hallucinations in large language models using semantic entropy (2024) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The top-3 softmax-weighted configuration trades 1 . 6 F1 for multi-chunk fusion”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The empirically optimal k is therefore corpus-dependent: k =1 for single-paragraph evidence (SQuAD), k =2 -3 for multi-hop (HotpotQA).”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Because we use the released SQuAD reference answers verbatim and do not introduce model-assisted question generation, no additional human verification step is required.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We report token-level Exact Match (EM) and F1 against the gold answer set, computed via the official SQuAD v1.1 evaluation script. 1 The script applies the standard SQuAD normalizationlowercasing, stripping of punctuation, removal of articles ( a , an , the ), and whitespace tokenization-to both predictions and references before scoring”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“We use SQuAD (Rajpurkar et al., 2016) and HotpotQA (Yang et al., 2018) as the base datasets, which are two widely used datasets in the field of QA and QG.”
From QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024) · cites this paper as Rajpurkar et al., 2016
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In the citing paper
“Our results show that, following reinforcement learning (RL) training, finetuning on self-generated synthetic data improves question-answering performance on the no-passage-in-context variant of SQuAD [13] from 33.5% to 47.0%.”
From Self-Adapting Language Models (2025) · cites this paper as [13]
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In the citing paper
“Standard NLP models are often evaluated on large datasets of coherent text-based questions and answers [48, 49] or paired written dialogue [68].”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [49]
The cited source says what the claim attributes to it.
In the citing paper
“We evaluate on a randomly sampled 1 , 000 -question subset of the SQuAD v1.1 dev split using the official evaluation script”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“For tractability under the 19 tok/s edge-throughput inference configuration, we sample a fixed random subset of 1 , 000 questions from the SQuAD v1.1 dev split using a fixed random seed.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“Traditional question-answering (QA) benchmarks are typically designed to provide fixed answers to questions, as in datasets like SQuAD (Rajpurkar et al., 2016) and Natural Questions (Kwiatkowski et al., 2019).”
From DebateQA: Evaluating Question Answering on Debatable Knowledge (2026) · cites this paper as Rajpurkar et al., 2016
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In the citing paper
“We found that existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [49]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“PRECOG top-1 matches in-context RAG to within 0 . 2 F1 and 0 . 2 EM on this subset”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The total chunk count is 2 , 067 , producing a state corpus of approximately 0 . 40 GB; the corresponding sentence-encoder key index occupies under 1 MB.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredIn the citing paper
“The type of questions to be generated range from the typical short answer span questions to multiple spans for question generation from the text [42, 47, 106, 107, 112].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [42]
In the citing paper
“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [42]
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Wei, J., Qi, S., Wang, W., Jiang, L., Gao, H., Zhao, F., Al-Bukhaiti, K., & Wan, A. (2025) · Contemporary Mathematics · doi:10.37256/cm.6220256459 · Cited by 15
“Neural networks have demonstrated strong performance in tasks such as language translation, sentiment analysis, and question-answering [72–74].”
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Question Generation With Doubly Adversarial Nets
Bao, J., Gong, Y., Duan, N., Zhou, M., & Zhao, T. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2859777 · Cited by 14
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“In the first round, questions generated based on SQuAD and HotpotQA were presented and scored separately.”
MethodInfluentialFActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv · doi:10.48550/arxiv.2305.14251 · Cited by 14
“Specifically, we check the token-level F1 score (Rajpurkar et al., 2016) comparing the new tokens added by the gold edit G and the new tokens added by the model-generated edit E .”
MethodTrained on 100 million words and still in shape: BERT meets British National Corpus
Samuel, D., Kutuzov, A., Øvrelid, L., & Velldal, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.146 · Cited by 14
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models
Yang, Z., Cui, Y., & Chen, Z. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.4 · Cited by 14
Investigating Educational and Noneducational Answer Selection for Educational Question Generation
Steuer, T., Filighera, A., & Tregel, T. (2022) · IEEE Access · doi:10.1109/access.2022.3180838 · Cited by 13
“Among these commonly used question answering data-sets are SQuAD [5] and Natural Questions (NQA) [7].”
BackgroundMethodInfluentialAdaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Chen, J., Yoon, J., Ebrahimi, S., Arik, S., Pfister, T., & Jha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.345 · Cited by 13
Sun, K., Xu, Y. E., Zha, H., Liu, Y., & Dong, X. L. (2023) · arXiv · doi:10.48550/arxiv.2308.10168 · Cited by 13
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Kang, M., Kwak, J. M., Baek, J., & Hwang, S. J. (2023) · arXiv · doi:10.48550/arxiv.2305.18846 · Cited by 13
QAScore—An Unsupervised Unreferenced Metric for the Question Generation Evaluation
Ji, T., Lyu, C., Jones, G., Zhou, L., & Graham, Y. (2022) · Entropy · doi:10.3390/e24111514 · Cited by 12
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Clement, C., Lu, S., Liu, X., Tufano, M., Drain, D., Duan, N., Sundaresan, N., & Svyatkovskiy, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.387 · Cited by 12
GAIA: a benchmark for General AI Assistants
Mialon, G., Fourrier, C., Swift, C., Wolf, T., LeCun, Y., & Scialom, T. (2023) · arXiv · doi:10.48550/arxiv.2311.12983 · Cited by 12
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
Kumarage, T., Sheth, P., Moraffah, R., Garland, J., & Liu, H. (2023) · arXiv · doi:10.48550/arxiv.2310.05095 · Cited by 11
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
Ben-Zaken, E., Ravfogel, S., & Goldberg, Y. (2021) · arXiv · doi:10.48550/arxiv.2106.10199 · Cited by 11
Proving Test Set Contamination in Black Box Language Models
Oren, Y., Meister, N., Chatterji, N., Ladhak, F., & Hashimoto, T. B. (2023) · arXiv · doi:10.48550/arxiv.2310.17623 · Cited by 11
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Yuan, L., Chen, Y., Wang, X., Fung, Y. R., Peng, H., & Ji, H. (2023) · arXiv · doi:10.48550/arxiv.2309.17428 · Cited by 11
MAGE: Machine-generated Text Detection in the Wild
Li, Y., Li, Q., Cui, L., Bi, W., Wang, Z., Wang, L., Yang, L., Shi, S., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.13242 · Cited by 10
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference
Zadeh, A. H., Edo, I., Awad, O. M., & Moshovos, A. (2020) · 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro50266.2020.00071 · Cited by 10
Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00549 · Cited by 9
In-context learning with retrieved demonstrations for language models: A survey
Luo, M., Xu, X., Liu, Y., Pasupat, P., & Kazemi, M. (2024) · arXiv · doi:10.48550/arxiv.2401.11624 · Cited by 9
Automatic Generation of Multiple-choice Cloze-test Questions for Lao Language Learning
Qiu, X., Xue, H., Liang, L., Xie, Z., Liao, S., & Shi, G. (2021) · 2021 International Conference on Asian Language Processing (IALP) · doi:10.1109/ialp54817.2021.9675153 · Cited by 7
Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning
Nguyen, M., Kishan, K. C., Nguyen, T., Chadha, A., & Vu, T. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43415-0_35 · Cited by 8
Diversify Question Generation with Retrieval-Augmented Style Transfer
Gou, Q., Xia, Z., Yu, B., Yu, H., Huang, F., Li, Y., & Cam-Tu, N. (2023) · arXiv · doi:10.48550/arxiv.2310.14503 · Cited by 8
Optimizing LLM Queries in Relational Data Analytics Workloads
Liu, S., Biswal, A., Kamsetty, A., Cheng, A., Schroeder, L. G., Patel, L., Cao, S., Mo, X., Stoica, I., Gonzalez, J. E., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.05821 · Cited by 8
“) (Rajpurkar et al., 2016), and Fact Extraction and Verification (FEVER) (Thorne et al., 2018).”
Defending Against Indirect Prompt Injection Attacks With Spotlighting
Hines, K., Lopez, G., Hall, M., Zarfati, F., Zunger, Y., & Kiciman, E. (2024) · arXiv · doi:10.48550/arxiv.2403.14720 · Cited by 8
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Ren, L., Liu, Y., Lu, Y., Shen, Y., Liang, C., & Chen, W. (2024) · arXiv · doi:10.48550/arxiv.2406.07522 · Cited by 8
Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge
Schroeder, K., & Wood-Doughty, Z. (2024) · arXiv · doi:10.48550/arxiv.2412.12509 · Cited by 8
Biomedical Abstract Sentence Classification by BERT-Based Reading Comprehension.
Jiang, C.-Y., & Fan, Y.-C. (2023) · SN Computer Science · doi:10.1007/s42979-023-01830-0 · Cited by 5
Li, C., Zhang, R., Wong, J., Gokmen, C., Srivastava, S., Martín-Martín, R., Wang, C., Levine, G., Ai, W., Martinez, B., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.09227 · Cited by 6
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
Kuratov, Y., Bulatov, A., Anokhin, P., Rodkin, I., Sorokin, D., Sorokin, A., & Burtsev, M. (2024) · arXiv · doi:10.48550/arxiv.2406.10149 · Cited by 19
“One notable difference is that RULER’s "needles" (such as adjectives, nouns, numbers, uuids) and long "haystack" contexts are more synthetic, consisting of randomly repeated sentences, except for tasks based on the SQuAD (Rajpurkar et al., 2016) and HotPotQA (Yang et al., 2018) datasets or using Paul Graham essays.”
MethodBenchmarking Benchmark Leakage in Large Language Models
Xu, R., Wang, Z., Fan, R.-Z., & Liu, P. (2024) · arXiv · doi:10.48550/arxiv.2404.18824 · Cited by 6
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Tripto, N., Uchendu, A., Le, T., Setzu, M., Giannotti, F., & Lee, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.916 · Cited by 6
Large language models robustness against perturbation
Alahmari, S. S., Hall, L., Mouton, P. R., & Goldgof, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-29770-0 · Cited by 5
Defense against prompt injection attacks via mixture of encodings
Zhang, R., Sullivan, D., Jackson, K., Xie, P., & Chen, M. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) · doi:10.18653/v1/2025.naacl-short.21 · Cited by 5
How Context Affects Language Models' Factual Predictions
Fabio Petroni, P. L. (2020) · Automated Knowledge Base Construction (AKBC) · doi:10.24432/c5201w · Cited by 4
“1 [Rajpurkar et al., 2016].”
BackgroundMethodInfluentialAnchor Points: Benchmarking Models with Much Fewer Examples
Vivek, R., Ethayarajh, K., Yang, D., & Kiela, D. (2024) · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.eacl-long.95 · Cited by 4
Anchor Points: Benchmarking Models with Much Fewer Examples
Vivek, R., Ethayarajh, K., Yang, D., & Kiela, D. (2023) · arXiv · doi:10.48550/arxiv.2309.08638 · Cited by 4
“Yet these same systems prove to be brittle, spurring the development of larger, more diverse, and more shrewd benchmarks to reliably assess their behavior (Rajpurkar et al., 2016; Hendrycks et al., 2021; Kiela et al., 2021; Wu et al., 2023).”
BackgroundZweiger, A., Pari, J., Guo, H., Akyürek, E., Kim, Y., & Agrawal, P. (2025) · arXiv · Cited by 3
“Our results show that, following reinforcement learning (RL) training, finetuning on self-generated synthetic data improves question-answering performance on the no-passage-in-context variant of SQuAD [13] from 33.5% to 47.0%.”
InfluentialNVLM: Open Frontier-Class Multimodal LLMs
Dai, W., Lee, N., Wang, B., Yang, Z., Liu, Z., Barker, J., Rintamaki, T., Shoeybi, M., Catanzaro, B., & Ping, W. (2024) · arXiv · doi:10.48550/arxiv.2409.11402 · Cited by 3
Investigating Continual Pretraining in Large Language Models: Insights and Implications
Yıldız, Ç., Ravichandran, N. K., Sharma, N., Bethge, M., & Ermis, B. (2024) · arXiv · doi:10.48550/arxiv.2402.17400 · Cited by 3
Think before you speak: Training Language Models With Pause Tokens
Goyal, S., Ji, Z., Rawat, A. S., Menon, A. K., Kumar, S., & Nagarajan, V. (2023) · arXiv · doi:10.48550/arxiv.2310.02226 · Cited by 2
“We consider nine varied downstream tasks: (a) reasoning (GSM8k (Cobbe et al., 2021)), (b) ex-tractive question answering (SQuAD (Rajpurkar et al., 2016), CoQA (Reddy et al., 2019)), (c) general understanding (CommonSenseQA (Talmor et al., 2019), PhysicalIQA (Bisk et al., 2020)), (d) (Berant et al.,…”
BackgroundMethodInfluentialSchuster, T., Kalyan, A., Polozov, O., & Kalai, A. T. (2021) · arXiv · doi:10.48550/arxiv.2106.05784 · Cited by 2
BAGEL: Bootstrapping Agents by Guiding Exploration with Language
Murty, S., Manning, C., Shaw, P., Joshi, M., & Lee, K. (2024) · arXiv · doi:10.48550/arxiv.2403.08140 · Cited by 2
OLMo, T., Walsh, P., Soldaini, L., Groeneveld, D., Lo, K., Arora, S., Bhagia, A., Gu, Y., Huang, S., Jordan, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.00656 · Cited by 2
GraphRouter: A Graph-based Router for LLM Selections
Feng, T., Shen, Y., & You, J. (2024) · arXiv · doi:10.48550/arxiv.2410.03834 · Cited by 2
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Yen, H., Gao, T., Hou, M., Ding, K., Fleischer, D., Izsak, P., Wasserblat, M., & Chen, D. (2024) · arXiv · Cited by 2
“…most similar to the original NIAH), NIAH Single Repeat (variant 1, where the context is repeated phrase instead of Paul Graham essays), and NIAH Single UUID (variant 3, same as variant 2 but with UUIDs as the value to retrieve) Rajpurkar et al., 2016) and HotpotQA (variant 2; Yang et al., 2018).”
MethodTowards a Science of Scaling Agent Systems
Kim, Y., Gu, K., Park, C., Park, C., Schmidgall, S., Heydari, A. A., Yan, Y., Zhang, Z., Zhuang, Y., Liu, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2512.08296 · Cited by 197
Self-citationDo Large Language Model Benchmarks Test Reliability?
Vendrow, J., Vendrow, E., Beery, S., & Madry, A. (2025) · arXiv · doi:10.48550/arxiv.2502.03461 · Cited by 1
SEDM: Selective Self-Evolving Distributed Memory for Agents
Liu, Y., Min, A., Wang, Y., Yao, Y., Han, Y., Zhang, R., He, J., Li, Z., Qi, Y., Xu, H., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.09498 · Cited by 20
“The proposed SEDM is compared with the following baselines: (1) No Memory : the model only relies on the query input without any memory augmentation, serving as the basic performance reference; (2) G-Memory [50]: a memory-augmented method that stores all past information in a global memory pool and…”
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training
von Oswald, J., Scherrer, N., Kobayashi, S., Versari, L., Yang, S., Mittal, S., Schlegel, M., Maile, K., Schimpf, Y., Sieberling, O., et al. (2025) · arXiv
“To gauge the ability to recall in-context information, we follow Arora et al. [4] and Yang et al. [111] and evaluate models on SWDE [65], SQUAD [82], FDA [3], TQA [55], NQ [58] and DROP [29].”
Metadata
- Authors
- Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 2383-2392
- OpenAlex
- W2963748441
- Cited by
- 6,208
- References
- 27
- Citations by year
- 2026: 335 · 2025: 492 · 2024: 506 · 2023: 1093 · 2022: 879 · 2021: 1127 · 2020: 858 · 2019: 592 · 2018: 254 · 2017: 62 · 2016: 3
- FWCI
- 163.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- question answering, reading comprehension, SQuAD, Wikipedia, dependency trees, constituency trees, logistic regression, human performance comparison, natural language understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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