The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Introducing ORKG ASK: An AI-Driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach
Open accessOverview
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?
External stores can be updated immediately without retraining and can maintain explicit provenance and versioning.
“External stores, by contrast, can be updated immediately without retraining and can maintain explicit provenance and versioning [Oelen et al., 2025, Chinthareddy, 2026].”
From Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering (2026) · cites this paper as Oelen et al., 2025
Section 3.1 (p. 4) says new documents can be indexed and added to the vector store without retraining the language model. Section 4.1 (p. 7) describes transparency about the context and settings used to generate results, but the paper does not establish that external stores maintain explicit provenance and versioning. It also says documents can be added easily, not necessarily immediately. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453
NASA-Task Load Index (NASA-TLX); 20 Years Later
Hart, S. G. (2006) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120605000909 · Cited by 4,297
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218
Gusenbauer, M., & Haddaway, N. R. (2020) · Research Synthesis Methods · doi:10.1002/jrsm.1378 · Cited by 2,379
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842
ChatGPT and other large language models are double-edged swords
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023) · Radiology · doi:10.1148/radiol.230163 · Cited by 1,008
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. D. L., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.06825 · Cited by 340
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.04023 · Cited by 337
Scientific literature: Information overload
Landhuis, E. (2016) · Nature · doi:10.1038/nj7612-457a · Cited by 324
UMUX-LITE: when there's no time for the SUS
Lewis, J. R., Utesch, B. S., & Maher, D. E. (2013) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2481287 · Cited by 270
Artificial intelligence for literature reviews: opportunities and challenges
Bolaños, F., Salatino, A., Osborne, F., & Motta, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10902-3 · Cited by 246
Hallucinations in LLMs: Understanding and Addressing Challenges
Perković, G., Drobnjak, A., & Botički, I. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569238 · Cited by 179
National study of information seeking behavior of academic researchers in the United States
Niu, X., Hemminger, B. M., Lown, C., Adams, S. J., Brown, C. M., Level, A. V., McLure, M., Powers, A., Tennant, M. R., & Cataldo, T. T. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21307 · Cited by 139
CORE: A Global Aggregation Service for Open Access Papers
Knoth, P., Herrmannová, D., Cancellieri, M., Anastasiou, L., Pontika, N., Pearce, S., Gyawali, B., & Pride, D. (2023) · Scientific Data · doi:10.1038/s41597-023-02208-w · Cited by 46
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · arXiv · doi:10.48550/arxiv.1903.10676 · Cited by 41
Pride, D., Cancellieri, M., & Knoth, P. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43849-3_13 · Cited by 28
Pride, D., Cancellieri, M., & Knoth, P. (2023) · International Conference on Theory and Practice of Digital Libraries · doi:10.48550/arxiv.2307.04683 · Cited by 27
Artificial Intelligence for Literature Reviews: Opportunities and ChallengesPreprint
Bolanos, F., Salatino, A. A., Osborne, F., & Motta, E. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.08565 · Cited by 14
Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning
Susnjak, T., Hwang, P., Reyes, N. H., Barczak, A. L. C., McIntosh, T. R., & Ranathunga, S. (2024) · arXiv · doi:10.48550/arxiv.2404.08680
Flagged first, then by citations
Research works printed but not linked: 3
- Kusupati, A., et al.: Matryoshka representation learning (2024). https://arxiv.org/abs/2205.13147 · arXiv
- Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474. Curran Associates, Inc. (2020)
- Oelen, A., Jaradeh, M.Y., Auer, S.: ORKG ASK: a neuro-symbolic scholarly search and exploration system. Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems (2024). https://ceur-ws.org/Vol-3759/paper7.pdf
Cited by
Sorted by citations
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Zhou, C., Chai, H., Chen, W., Guo, Z., Shan, R., Song, Y., Xu, T., Yang, Y., Yu, A., Zhang, W., et al. (2026) · arXiv · doi:10.48550/arxiv.2604.08224 · Cited by 1
Baptista, R., & Donadel, G. (2026) · Research Square · doi:10.21203/rs.3.rs-10358339/v1
A Neurosymbolic Scholarly Intelligence System in Use at Springer Nature
Meloni, A., Salatino, A. A., Osborne, F., Vizcaino, A., Birukou, A., Recupero, D. R., & Motta, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-42029-9_20
Metadata
- Authors
- Oelen, A., Jaradeh, M. Y., & Auer, S.
- Affiliations
- Technische Informationsbibliothek (TIB) (DE)
- Leibniz University Hannover (DE)
- Corresponding author
- Oelen, Allard
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2025-10-12
- Pages
- 11-25
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4415081517
- Cited by
- 2
- References
- 13
- Citations by year
- 2026: 2
- FWCI
- 2.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- scholarly literature search, neuro-symbolic AI, large language models, retrieval-augmented generation, vector search, knowledge graphs, information retrieval, usability evaluation, Open Research Knowledge Graph
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