The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
ERATTA: Extreme RAG for enterprise-Table To Answers with Large Language Models
Overview
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In the citing paper
“ERATTA [295] generates SQL code to extract table information, integrating it into the prompt to minimize model hallucination.”
From Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024) · cites this paper as [295]
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Citations
Cites
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606763 · Cited by 2,187 · cited 1×
“…applications, such as a chatbot-like experience with a virtual assistant for code, language and content generation [2]; or Gen-AI agents that are capable of automated orchestration to perform specific tasks such as booking tickets, writing blogs, articles and generating software automatically [3].”
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · arXiv · doi:10.48550/arxiv.2304.03442 · Cited by 2,173
Research works printed but not linked: 10
- 10.1109/BigData59044.2023.10386232 · DOI
- 10.1145/3643479.3662055 · DOI
- 10.1109/ACCESS.2020.2977613 · DOI
- 10.1007/s10791-006-9005-5 · DOI
- 10.1109/ICCCBDA.2019.8725626 · DOI
- 10.18653/v1/D18-2002 · DOI · Paper page
- 10.18653/v1/P18-1034 · DOI
- 10.18653/v1/N18-2115 · DOI
- 10.5220/0012204000003584 · DOI
- Datta, 2022, Exploring conceptual soundness with trulens, NeurIPS 2021 Competitions and Demonstrations Track, 302
Cited by
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., Jiang, J., & Cui, B. (2024) · arXiv · doi:10.48550/arxiv.2402.19473 · Cited by 240
A survey on knowledge-oriented retrieval-augmented generation
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., Yu, S., Zhang, B., Cao, J., Ma, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.10677 · Cited by 140
Metadata
- Authors
- Roychowdhury, S., Krema, M., Mahammad, A., Moore, B., Mukherjee, A., & Prakashchandra, P.
- Affiliations
- Accenture,Corporate Data and Analytics Office (CDAO),USA
- Accenture,CDAO,India
- Venue
- 2024 IEEE International Conference on Big Data (BigData)
- Publisher
- IEEE
- Published
- 2024-12-15
- Pages
- 4605-4610
- Cited by
- 11
- References
- 2
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
