Back to papers
Cents: A Flexible and Cost-Effective Framework for LLM-Based Table Understanding
Summary: Unified, cost-aware framework for LLM-based table understanding that compresses tabular inputs to cut input token cost while boosting accuracy. Demonstrated across multiple tasks (e.g., column-type annotation), outperforms LLM baselines at equal or lower cost.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 14068
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,753 | 25.20%
- DOI
-
10.14778/3749646.3749714
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 107 |
WebTables: Exploring the Power of Tables on the Web |
2008 |
VLDB |
0.00048377684 |
| 364 |
Annotating and Searching Web Tables Using Entities, Types and Relationships |
2010 |
VLDB |
0.00025637562 |
| 420 |
InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables |
2012 |
SIGMOD |
0.00023719065 |
| 513 |
TURL: Table Understanding through Representation Learning |
2021 |
VLDB |
0.00021288342 |
| 517 |
Can Foundation Models Wrangle Your Data? |
2023 |
VLDB |
0.00021169035 |
| 1,872 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
0.00010259702 |
| 2,517 |
Annotating Columns with Pre-trained Language Models |
2022 |
SIGMOD |
8.6092139e-05 |
| 2,587 |
Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks |
2024 |
SIGMOD |
8.4924618e-05 |
| 2,836 |
Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning |
2023 |
VLDB |
8.0443826e-05 |
| 2,888 |
Sato: Contextual Semantic Type Detection in Tables |
2020 |
VLDB |
7.9594996e-05 |
| 3,015 |
Chorus: Foundation Models for Unified Data Discovery and Exploration |
2024 |
VLDB |
7.7092391e-05 |
| 3,520 |
GitTables: A Large-Scale Corpus of Relational Tables |
2023 |
SIGMOD |
7.0131061e-05 |
| 4,859 |
Integrating Data Lake Tables |
2023 |
VLDB |
5.8732433e-05 |
| 5,099 |
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models |
2024 |
VLDB |
5.6997784e-05 |
| 6,092 |
Observatory: Characterizing Embeddings of Relational Tables |
2024 |
VLDB |
5.2138566e-05 |
| 7,048 |
Magneto: Combining Small and Large Language Models for Schema Matching |
2025 |
VLDB |
4.8520651e-05 |
| 7,914 |
Efficient Approximate Algorithms for Empirical Entropy and Mutual Information |
2021 |
SIGMOD |
4.6179608e-05 |
| 8,852 |
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation |
2023 |
SIGMOD |
4.4356508e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 10,455 |
Sentence to Model: Cost-Effective Data Collection LLM Agent |
2025 |
SIGMOD |
4.1945683e-05 |
| 2,517 |
Annotating Columns with Pre-trained Language Models |
2022 |
SIGMOD |
8.6092139e-05 |
| 6,800 |
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models |
2024 |
SIGMOD |
4.9231471e-05 |
| 2,587 |
Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks |
2024 |
SIGMOD |
8.4924618e-05 |
| 10,022 |
In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,595 |
Optimized Batch Prompting for Cost-effective LLMs |
2025 |
VLDB |
4.1945683e-05 |
| 9,399 |
TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations |
2025 |
VLDB |
4.3441378e-05 |
| 10,064 |
Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,452 |
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems |
2025 |
SIGMOD |
4.1945683e-05 |
| 9,235 |
ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries |
2025 |
VLDB |
4.3690661e-05 |