Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Test-time scaling improves both reliability and accuracy, especially on tasks that require multistep reasoning, without additional training or fine-tuning.
“Without requiring additional training or fine-tuning, test-time scaling improves both reliability and accuracy, especially on tasks that require multistep reasoning.”
From Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study (2026) · cites this paper as [18]
General-domain test-time scaling findings cannot readily predict the multilayered complexities inherent to clinical AI and fail to capture domain-specific bottlenecks and behavioral shifts in medical environments.
“Crucially, while foundational studies have established test-time scaling paradigms in the general domain by focusing primarily on text-based logic such as mathematics and coding [18-20], their findings cannot readily predict the multilayered complexities inherent to clinical AI.”
From Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study (2026) · cites this paper as [18]
Test-time scaling has emerged as an effective method for enhancing LLM performance during inference without additional training.
“To address these challenges, test-time scaling has recently emerged as an effective method for enhancing LLM performance during inference [17-20].”
From Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study (2026) · cites this paper as [18]
Prior studies on test-time scaling in general domains have established that increasing inference compute can improve reasoning, though the relationship is not strictly monotonic.
“Prior studies on test-time scaling in general domains, such as mathematics and coding, have established that increasing inference compute can improve reasoning, though the relationship is not strictly monotonic.”
From Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study (2026) · cites this paper as [18]
Optimal reasoning lengths exist and excessive self-revision can sometimes degrade performance.
“These works demonstrate that optimal reasoning lengths exist and that excessive self-revision can sometimes degrade performance [18-20].”
From Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study (2026) · cites this paper as [18]
Cited by
Sorted by citations
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407 · see citing works
A review: Knowledge reasoning over knowledge graph
Chen, X., Jia, S., & Xiang, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112948 · Cited by 1,035 · see citing works
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846 · see citing works
Pragmatic Language Interpretation as Probabilistic Inference
Goodman, N. D., & Frank, M. C. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.08.005 · Cited by 594 · see citing works
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585 · see citing works
Public microblogging on climate change: One year of Twitter worldwide
Kirilenko, A. P., & Stepchenkova, S. O. (2014) · Global Environmental Change · doi:10.1016/j.gloenvcha.2014.02.008 · Cited by 254 · see citing works
Martínez-Rojas, M., Pardo-Ferreira, M. D. C., & Rubio-Romero, J. C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.07.008 · Cited by 213 · see citing works
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211 · see citing works
AI as a moral crumple zone: The effects of AI-mediated communication on attribution and trust
Hohenstein, J., & Jung, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106190 · Cited by 212 · see citing works
Using online social networks to track a pandemic: a systematic review
Al-garadi, M. A., Khan, M. S., Varathan, K. D., Mujtaba, G., & Al-Kabsi, A. M. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.05.005 · Cited by 166 · see citing works
Query-by-committee improvement with diversity and density in batch active learning
Kee, S., del Castillo, E., & Runger, G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.014 · Cited by 98 · see citing works
The ripple of fear, sympathy and solidarity during the Boston bombings
Lin, Y.-R., & Margolin, D. (2014) · EPJ Data Science · doi:10.1140/epjds/s13688-014-0031-z · Cited by 72 · see citing works
PDF text classification to leverage information extraction from publication reports
Bui, D. D. A., Del Fiol, G., & Jonnalagadda, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.026 · Cited by 52 · see citing works
Cao, Y. T., & Daumé, H. (2021) · Computational Linguistics · doi:10.1162/coli_a_00413 · Cited by 47 · see citing works
Alsoubai, A., Song, J., Razi, A., Naher, N., De Choudhury, M., & Wisniewski, P. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555136 · Cited by 36 · see citing works
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Chen, J., Zhang, R., Guo, J., de Rijke, M., Liu, Y., Fan, Y., & Cheng, X. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591631 · Cited by 32 · see citing works
AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.
Ghosh, M., Mukherjee, S., Ganguly, A., Basuchowdhuri, P., Naskar, S. K., & Ganguly, D. (2024) · Methods · doi:10.1016/j.ymeth.2024.04.005 · Cited by 27 · see citing works
Yu, J. E., Parde, N., & Chattopadhyay, D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581447 · Cited by 22 · see citing works
Oguine, O. C., Park, J. K., Akter, M., Olesk, J., Alluhidan, A., Wisniewski, P., & Badillo-Urquiola, K. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710995 · Cited by 8 · see citing works
Oh, G., Kim, S., Park, S., & Kim, B.-H. (2026) · Journal of Medical Internet Research · doi:10.2196/90693 · Cited by 1 · see citing works
Quantifying and Mitigating Self-Preference Bias of LLM Judges
Yang, J., Hu, Z., Qiu, C., Deng, Z., Jiao, X., & Zhou, T. (2026) · arXiv · doi:10.48550/arxiv.2604.22891
- Authors
- Woodsend, K., & Lapata, M.
- ORCID
- Mirella Lapata
- Published
- 2025
- OpenAlex
- W1554540371
- Cited by
- 787
- References
- 0
- Citations by year
- 2026: 2 · 2025: 80 · 2024: 79 · 2023: 88 · 2022: 86 · 2021: 64 · 2020: 80 · 2019: 60 · 2018: 52 · 2017: 29 · 2016: 43 · 2015: 52 · 2014: 20 · 2013: 32 · 2012: 17
- Type
- paratext
- Language
- en
- Open access
- closed
- Locations
- The Association for Computational Linguistics
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
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
