Demo Data from VLDB 2026 / reviewers in the wild / expert
Steven H. Wang
dblp:296/4286
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% | |
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › retrieval models › neural retrieval › dense retrieval
bi-encoder retrieval |
0.9 | 1 | 2025 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025 |
Information retrieval › evaluation › test collection
retrieval benchmark |
0.9 | 1 | 2025 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025 |
Information retrieval
retrieval models |
0.9 | 1 | 2025 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025 |
Natural language and speech › Information extraction and text analysis
dataset construction |
0.7 | 1 | 2023 | MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023 |
Natural language and speech › Information extraction and text analysis › document understanding
legal text analysis |
0.7 | 1 | 2023 | MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023 |
Information retrieval
retrieval evaluation |
0.3 | 1 | 2025 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025 |
Computational social science and digital humanities
legal informatics |
0.2 | 1 | 2023 | MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023 |
Methods — techniques the papers use, named apart from their topics
expert annotation · 1.3pointwise ranking · 0.9bi-encoder retriever · 0.9LLM re-ranker · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract DraftingabstractContract clause retrieval is foundational to contract drafting because lawyers rarely draft contracts from scratch; instead, they locate and revise the most relevant precedent clauses. We introduce the Atticus Clause Retrieval Dataset (ACORD), the first expert-annotated benchmark specifically designed for contract clause retrieval to support contract drafting tasks. ACORD focuses on complex contract clauses such as Limitation of Liability, Indemnification, Change of Control, and Most Favored Nation. It includes 114 queries and over 126,000 query-clause pairs, each ranked on a scale from 1 to 5 stars. The task is to find the most relevant precedent clauses to a query. The bi-encoder retriever paired with pointwise LLMs re-rankers shows promising results. However, substantial improvements are still needed to manage the complex legal work typically undertaken by lawyers effectively. As the first expert-annotated benchmark for contract clause retrieval, ACORD can serve as a valuable IR benchmark for the NLP community. Steven H. Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Andreas Plesner, Roger Wattenhofer |
ACL (1) | 1 |
| 2023 | MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement UnderstandingabstractSteven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. Steven H. Wang, Antoine Scardigli, Leonard Tang, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks |
EMNLP | 1 |