The DOI matches its Crossref record; 8 of the 13 references it cites were found in a registry or an index.
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Open accessAbstract
Assessing foundation models' abilities for human-level tasks is crucial for Artificial General Intelligence (AGI) development.Traditional benchmarks, which rely on artificial datasets, may not accurately represent these capabilities.In this paper, we introduce AGIEval, a novel bilingual benchmark designed to assess foundation models in the context of humancentric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests.We evaluate several state-of-the-art foundation models on our benchmark.Impressively, we show that GPT-4 exceeds the average human performance in SAT, LSAT, and math contests, with 95% accuracy on SAT Math and 92.5% on the Chinese college entrance English exam.This demonstrates the exceptional performance of contemporary foundation models.In contrast, we also find that GPT-4 is less proficient in tasks requiring complex reasoning or specific domain knowledge.Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal their strengths and limitations, providing valuable insights into future directions for enhancing general capabilities.By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a meaningful and robust evaluation of foundation models' performance in real-world scenarios 1 .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The aforementioned factors require a multidisciplinary approach to LLM benchmarking, integrating insights from linguistics, ethics, and social sciences to create robust evaluation frameworks [3], [13], [30].”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [13]
In the citing paper
“Unlike traditional benchmarks with clear numerical metrics, LLM benchmarking often involves subjective judgments and complex evaluation criteria [13], [25].”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 10
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
- Alexis Conneau and Douwe Kiela. 2018. SentEval: An evaluation toolkit for universal sentence representa- tions. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
- Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021. Bold: Dataset and metrics for measuring biases in open-ended language genera- tion. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 862-872.
- Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. Sort, 2(4):0-6.
- Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blun- som. 2017. Program induction by rationale genera- tion: Learning to solve and explain algebraic word problems. In Proceedings of the 55th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158-167.
- Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2021. Logiqa: a challenge dataset for machine reading comprehen- sion with logical reasoning. In Proceedings of the Twenty-Ninth International Conference on Interna- tional Joint Conferences on Artificial Intelligence, pages 3622-3628.
- Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 2383-2392.
- Alex Wang, Yada Pruksachatkun, Nikita Nangia, Aman- preet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019. Superglue: A stick- ier benchmark for general-purpose language under- standing systems. Advances in neural information processing systems, 32.
- Siyuan Wang, Zhongkun Liu, Wanjun Zhong, Ming Zhou, Zhongyu Wei, Zhumin Chen, and Nan Duan. 2022. From lsat: The progress and challenges of com- plex reasoning. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:2201-2216.
- Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2020. Jec- qa: A legal-domain question answering dataset. In Proceedings of AAAI.
Cited by
Sorted by citations
Items 101–200 of 220
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs
Xu, B., Liu, X., Shen, H., Han, Z., Li, Y., Yue, M., Peng, Z., Liu, Y., Yao, Z., & Xu, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2023.emnlp-demo.20 · Cited by 4
ALCUNA: Large Language Models Meet New Knowledge
Yin, X., Huang, B., & Wan, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.87 · Cited by 27
Trippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 5
Liu, P., Chen, Z., Li, Y., & Wong, W. E. (2025) · International Symposium on System Security, Safety, and Reliability (ISSSR) · doi:10.1109/isssr65654.2025.00047 · Cited by 4
Enhancing math reasoning ability of large language models via computation logic graphs
Zhao, D., Han, D., Wu, J. X., He, Z., Ning, B., Yuan, Y., Li, Y., Wang, C., & Song, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113905 · Cited by 4
Zhou, S., Li, T., Wang, K., Huang, Y., Shi, L., Liu, Y., & Wang, H. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00209 · Cited by 18
Yamanaka, J., & 隆, 城. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31254 · Cited by 4
SecureLLAMA: Secure FPGAs Using LLAMA Large Language Models
Alqarni, M., & Azim, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3544590 · Cited by 5
Yang, L., Luo, Y., Gao, H., Fan, Y., Zhang, J., Li, X., Dong, X., Gu, B., Jin, Z. G., & Yang, M. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728560 · Cited by 5
Evaluating LLMs on Kazakhstan's mathematics exam for university admission
Kadyrov, S., Abdrasilov, B. S., Sabyrov, A., Baizhanov, N., Makhmutova, A., & Kyllonen, P. C. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1642570 · Cited by 3
Wu, D., Zhang, R., Zucchelli, E. M., Chen, Y.-C., & Linares, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-91150-5 · Cited by 8
CLEVA: Chinese Language Models EVAluation Platform
Li, Y., Zhao, J., Zheng, D., Hu, Z.-Y., Chen, Z., Su, X., Huang, Y., Huang, S., Lin, D., Lyu, M. R.-T., et al. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.17 · Cited by 4
Wu, H. R., Hong, H., Sun, L., Bai, X., & Pu, M. (2024) · Electronics · doi:10.3390/electronics13234581 · Cited by 4
OpenBA: an open-sourced 15B bilingual asymmetric Seq2Seq model pre-trained from scratch
Li, J., Tang, Z., Ding, Y., Wang, P., Guo, P., You, W., Qiao, D., Wang, C., Chen, W., Fu, G., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-023-4128-3 · Cited by 10
Self-citationEvaluating Language Model Agency through NegotiationsPreprint
Davidson, T. R., Veselovsky, V., Josifoski, M., Peyrard, M., Bosselut, A., Kosiński, M., & West, R. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.04536 · Cited by 39
Navigating the Risks: A Review of Safety Issues in Large Language Models
Wang, H., Li, Y., Wang, Y., Liu, P., & Li, P. (2024) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c63300.2024.00020 · Cited by 2
Self-citationTemporal Extraction and Retrieval
Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2
Tonellotto, N. (2024) · ACM eBooks · doi:10.1145/3674127.3674130 · Cited by 3
AI Development and Innovation: A Comparison of Large Language Models from the U.S. and China
Li, J., Jiang, Z. J., & Liu, Y. (2025) · ACM Transactions on Management Information Systems · doi:10.1145/3769086 · Cited by 5
Wang, H., Qi, Z., Wang, N., Yu, Z., Li, Z., & Lv, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227721 · Cited by 2
Performance of AI Chatbots on the fundamentals of engineering civil exam
Oblitas, L., Adeeb, S., & Cruz-Noguez, C. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00646-6 · Cited by 2
PoETa v2: Toward More Robust Evaluation of Large Language Models in Portuguese
Almeida, T. S., Pires, R., Abonizio, H., Nogueira, R. F., & Pedrini, H. (2025) · IEEE Access · doi:10.1109/access.2025.3645771 · Cited by 10
Can AI See the Unseen? Measuring the Perception Gap for Tibetan Cultural Symbols in AI-Generated Art
Liu, Y., Qiao, Y., Hu, A., Liu, Y., & Bai, L. (2025) · Electronics · doi:10.3390/electronics15010015 · Cited by 2
Generative AI in Assessment and Feedback Generation in Higher Education: A Systematic Review
Yavariabdi, A., Paudel, B., Carleton, T., & de Almeida, C. D. A. (2025) · International Conference on Education Technology and Computer · doi:10.1109/icetc66579.2025.11387416 · Cited by 5
Zhang, Z., Yu, J., Li, J., & Hou, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.111 · Cited by 6
Chinese Stylistic Competence: Evaluation Method and Datasets of Large Language Model's Performance
Zhou, L., & Rao, G.-Q. (2023) · International Conference on Asian Language Processing · doi:10.1109/ialp61005.2023.10337306 · Cited by 2
Toward Immersive Computational Storytelling: Card-Framework for Enhanced Persona-Driven Dialogues
Liao, B., & Vargas, D. V. (2024) · IEEE Transactions on Games · doi:10.1109/tg.2024.3466898 · Cited by 2
Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 5
Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 3
Privacy in Information Retrieval
Soboroff, I. M. (2024) · ACM eBooks · doi:10.1145/3674127.3674139 · Cited by 2
CAST-Eval: A Domain-Specific Benchmark for Large Language Models in Civil Aviation Safety
Qiao, D., Tian, X., Xu, Y.-B., Yang, J., Yang, L., & Wang, X.-H. (2024) · 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE) · doi:10.1109/iceace63551.2024.10898372 · Cited by 4
Fan, Y., Li, R., Zhang, G., Shi, C., & Wang, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888877 · Cited by 4
Zhang, J., He, H., Ma, L., Song, N., He, S., Zhang, S., Qiu, H., Zhou, Z., Li, A., Dai, Y., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130070 · Cited by 2
Oyesanmi, F., & Olukanmi, P. O. (2025) · Applied Sciences · doi:10.3390/app15084459 · Cited by 3
Tang, F., Gao, W., Peng, L., & Zhan, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0316-6_9 · Cited by 4
CK12: A Rounded K12 Knowledge Graph Based Benchmark for Chinese Holistic Cognition Evaluation
You, W., Wang, P., Li, C., Ji, Z., & Bai, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29914 · Cited by 3
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
Quantifying Variance in Evaluation Benchmarks
Madaan, L., Singh, A. K., Schaeffer, R., Poulton, A., Koyejo, S., Stenetorp, P., Narang, S., & Hupkes, D. (2024) · arXiv · doi:10.48550/arxiv.2406.10229 · Cited by 2
Koten, O., Sulistyowati, F., Ahsan, M., & Kuncoro, K. S. (2023) · UNION Jurnal Ilmiah Pendidikan Matematika · doi:10.30738/union.v11i2.14910 · Cited by 2
eCommerce Search and Discovery
Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1
SPRInT: Scaling Programmatic Reasoning for INstruction Tuning in mathematics
Yan, Y., Li, L., & Zhang, B.-W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129868 · Cited by 1
Salim, R. A., Delvin, D., Harefa, J., & Jingga, K. (2024) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis63449.2024.10957638 · Cited by 1
Wu, H. R., Hong, H., Mao, J., Yin, Z., Wu, Y., Bai, X., Sun, L., Pu, M., Liu, J., & Li, Y. (2025) · Applied Sciences · doi:10.3390/app15095041 · Cited by 2
Anthropomorphic Enhancement Model Based on Large Language Models
Li, X., Wu, L., Shang, Y., & Liu, D. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-4016-4_10 · Cited by 1
Perses: Unlocking Privilege Escalation for Small LLMs via Extensible Heterogeneity
Weber, D. M., Tzachristas, I., & Sui, A.-F. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3736189 · Cited by 2
Interpeval: A Multi-Faceted Framework for Assessing Interpretability in Large Language Models
Chen, J., Yao, Z., Hou, L., & Li, J. (2025) · ICON · doi:10.1109/icnlp65360.2025.11108412 · Cited by 1
Exploring the Capabilities of Chinese LLMs in the Legal Field
Wang, Z., Yu, J., & Chu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_3 · Cited by 1
Benchmarking In-Context Learning Strategies of Large Language Models for Math Reasoning Tasks
Rong, Y., Seßler, K., Gözlüklü, E., & Kasneci, E. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3630117 · Cited by 3
System-Aware LLM Arena: Benchmarking Utility, Latency, and Cost
Ma, R.-W., Wang, S., Yu, Y., Ji, T., & Dai, W. (2025) · International Conference on Cloud Computing · doi:10.1109/cloudcom67567.2025.11331464 · Cited by 1
PC-MoE: memory-efficient and privacy-preserving collaborative training for Mixture-of-Experts LLMs
Zhang, Z.-Y., Ding, B.-L., & Low, B. (2025) · Machine-mediated learning · doi:10.1007/s10994-025-06901-2 · Cited by 1
He, Y., Liu, J., Wen, W., Guo, Y., Chen, H., Ma, Y., Ke, W., & Wang, P. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3654446 · Cited by 1
Self-citationLLM-assisted record linkage: A framework for official statistics
Ather, H. (2026) · Statistical Journal of the IAOS · doi:10.1177/18747655261422068 · Cited by 2
Understanding Artificial General Intelligence: Defining Characteristics and Benchmarks
Muhsen, D. K., & Sadiq, A. T. (2025) · Journal of Artificial Intelligence & Control Systems · doi:10.66967/jaics.2025.v1i103 · Cited by 1
Gómez, R., Miranda, C. E., Romero-González, J.-A., Esparza, D. M. C., Alfonso-Francia, G., Chávez‐Urbiola, E. A., Ramírez-Pedraza, A., & Terven, J. R. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8060141 · Cited by 2
Madhavan, M. M., Satyapriya, Sahu, S., Velayudhan, P. K., Smitha, S. C., Verma, A. P., Pawariya, V., Barua, S., Mahra, G. S., Bishnoi, S., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1769064 · Cited by 1
Mo, M., Zhang, H., Tan, Y., Huang, B., & Li, C. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116598 · Cited by 1
Exploring and Improving Consistency in Large Language Models for Multiple-Choice Question Assessment
Zhou, W.-J., & Duan, X.-Y. (2024) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn60899.2024.10650668 · Cited by 1
Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2
Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1
Baeza-Yates, R. A., Murgai, L., & Dudy, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674138 · Cited by 1
Adaptive Composite Accuracy Scoring for Domainspecific LLM Evaluation
Teh, J. S. R., Koay, E. K., Lim, S. W., Lee, K. H., Lai, M. S., Lee, M. S., & Nee, Y. K. (2024) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm63129.2024.10852484 · Cited by 3
Xi, N., Wu, Y., Fan, K., Chen, T., Gu, Q., & Ji, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8186-0_3 · Cited by 1
GATmath and GATLc: Comprehensive benchmarks for evaluating Arabic large language models
AlBallaa, S., Al-Twairesh, N., Al‐Salman, A. S., & Alfarhood, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329129 · Cited by 3
Wang, Y., Wang, Y., Liang, J., Xu, Y., Liu, Y., Xu, Y., Jiang, Z., Li, Z., Lin, C., Li, F., et al. (2025) · Intelligent Data Analysis: An International Journal · doi:10.1177/1088467x251389769
Self-citationMergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
Wang, J., Tian, C., Chen, K., Liu, Z., Mao, J., Zhao, W. X., Zhang, Z., & Zhou, J. (2026) · arXiv · doi:10.48550/arxiv.2601.17858
Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity
Zhang, H., Cui, Z., Chen, J., Wang, X., Zhang, Q., Wang, Z., Wu, D., & Hu, S. (2025) · arXiv · doi:10.48550/arxiv.2502.08788 · Cited by 84
ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning
Xie, Y., Li, G., & Kan, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.268 · Cited by 4
Do Large Language Models Learn to Human-Like Learn?
Roberts, J. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31287
A Merge Sort Based Ranking System for the Evaluation of Large Language Models
Li, C., Shi, L., Zhou, C., Huan, Z., Tang, C., Zhang, X., Wang, X., Zhou, J., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70378-2_15
Improving clinical expertise in large language models using electronic medical recordsPreprint
Zhu, L., Liu, J., Wang, J., Zhang, W., Jiang, S., Yang, H., Wang, C., Ye, Q., Ruan, T., Rui, X., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-5285540/v1
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674128
Searching Personal Collections
Bendersky, M., Metzler, D., Najork, M., & Wang, X. (2024) · ACM eBooks · doi:10.1145/3674127.3674142
Query-driven Search Functionality
Li, L. (2024) · ACM eBooks · doi:10.1145/3674127.3674133
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702962
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674129
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702961
Hersh, W., & Roberts, K. E. (2024) · ACM eBooks · doi:10.1145/3674127.3702960
Bast, H., Kalmbach, J., Klumpp, T., & Korzen, C. (2024) · ACM eBooks · doi:10.1145/3674127.3674134
LoRA Meets Foundation Models: Unlocking Efficient Specialization for Scalable AIPreprint
Shun, J. (2025) · doi:10.31224/4513
Imran, A., & Gopalakrishnan, K. (2025) · Apress eBooks · doi:10.1007/979-8-8688-0989-7_4
A Systematic Evaluation Framework for Cognitive Domain Performance of Large Language Models
Yao, C., Liu, D., Wei, Q., Li, J., Zhou, Y., Wang, S., & Zheng, C. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-4245-8_24
Attacks and Defenses on Aligned Large Language Models
Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_13
„Versteht“ ein System wie ChatGPT seine eigenen Texte zur Mathematik?
Weitz, E. (2025) · Angewandte Wirtschaftsinformatik · doi:10.1007/978-3-658-47311-2_3
Teleeval-Os: Performance Evaluations of Large Language Models for Operations SchedulingPreprint
Wang, Y., Wang, Y., Liang, J., Xu, Y., Liu, Y., Yiming, X., Jiang, Z., Li, Z., Li, F., Zhao, L., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5342996
Self-citationTorres-Moreno, D., & Hermosillo, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5372678
“Less but more efficient ”: Using less data to achieve better logical reasoning
Duan, R., Liu, X., Ding, Z., & Zhang, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131153
Tahir, H., Faisal, F., Alnajjar, F., Taj, M. I., Gordon, L., Khan, A. M., Lwin, M., & Mubin, O. (2025) · International Conference on Activity and Behavior Computing · doi:10.1109/abc64332.2025.11118419 · Cited by 1
Cui, S., He, X., Han, J., Zhang, Z., & Peng, Y. (2025) · Science China Technological Sciences · doi:10.1007/s11431-025-3090-5
TaxBen: Benchmarking the Chinese Tax Knowledge of Large Language Models
Chen, Y., Lv, S., Xia, P., Wang, Z., Qin, Y., Wang, Q., & Hu, G. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_24
Pereyda, C., & Holder, L. B. (2025) · AI · doi:10.3390/ai6120313
Trade-Offs Between Traditional LLM and Chain-of-Thought Reasoning for Mathematical Tasks
Ravanam, D., & Khare, S. (2025) · Ubiquitous Computing, Electronics & Mobile Communication Conference · doi:10.1109/uemcon67449.2025.11267619
Chen, L., Yan, S., Guo, Q., Sun, J., Cheng, F., Liu, Z., Xiao, W., Wang, M., Wang, H., & Zhao, X. (2025) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486115.014
BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning
Santos, J. G. A., Bonás, G. K., & Almeida, T. S. (2025) · doi:10.5753/eniac.2025.14256
RBLU: A Benchmark to Evaluate the Reverse Inference Ability of Large Language Models
Wang, H., Wang, F. B., Deng, S., Liu, L., & Liu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_6
SPPBench: A Comprehensive Sports Psychology Performance Benchmark for LLMs
Guo, X., & Xu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4445-5_8 · Cited by 1
Zhao, W. X., Zhou, K., Li, J., Tang, T., & Wen, J.-R. (2025) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-96-6259-3_12
Standardized Assessment of LLM English ProficiencyPreprint
Min, S., Wang, S., Gao, X., Wang, H., Jin, Z., Ling, C., & Ding, N. (2026) · Research Square · doi:10.21203/rs.3.rs-8820245/v1
Khuwaileh, B. A., & Matesha, P. (2026) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2026.112223 · Cited by 1
Developing a Testing Framework for Applications of LLMs within Islamic Finance
Al-Syed, Q., & Mahomed, Z. (2025) · World Scientific Annual Review of Islamic Finance · doi:10.1142/s2811023425500029
Karp, M., Kubaszewska, A., Król, M., Król, R., Wydmański, W., Smywiński-Pohl, A., & Szymański, M. (2026) · Artificial Intelligence and Law · doi:10.1007/s10506-026-09505-w · Cited by 4
Metadata
- Authors
- Zhong, W., Cui, R., Guo, Y., Liang, Y., Lu, S., Wang, Y., Saied, A., Chen, W., & Duan, N.
- Venue
- Findings of the Association for Computational Linguistics: NAACL 2024
- Published
- 2024
- Pages
- 2299-2314
- OpenAlex
- W4401042689
- Cited by
- 211
- References
- 0
- Citations by year
- 2026: 28 · 2025: 97 · 2024: 68
- FWCI
- 56.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: NAACL (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- standardized testing, college entrance examination, LSAT, GPT-4, artificial general intelligence
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
