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Joe Lilien

dblp:336/2621 · also Joseph Lilien · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Theory of computation · 1 · 1 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.

Artificial intelligence
2 papers
Information extraction and text analysis · 41% Trustworthy machine learning · 41% Language models and text generation · 18%
Theoretical computer science
1 paper
Automated reasoning and model checking · 100%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking › automated reasoning › mathematical reasoning
autoformalization
1.012026
A Neurosymbolic Approach to Natural Language Formalization and Verification · CAV (2) 2026
Machine learning › Trustworthy machine learning
robustness evaluation
0.712023
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness · ICLR 2023
Natural language and speech › Information extraction and text analysis › semantic parsing
text-to-SQL
0.712023
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness · ICLR 2023
Data models and query languages › natural language interface
natural language interface to database
0.712023
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness · ICLR 2023
Natural language and speech › Language models and text generation › large language model safety
LLM guardrail
0.312026
A Neurosymbolic Approach to Natural Language Formalization and Verification · CAV (2) 2026

Methods — techniques the papers use, named apart from their topics

large language model · 2.0automated reasoning · 2.0diagnostic benchmark construction · 1.3
YearPublicationVenuePosition
2026 A Neurosymbolic Approach to Natural Language Formalization and Verification
abstract
Abstract Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and healthcare that operate under strict policies. To address this limitation, we launched Automated Reasoning checks (ARc) : a public service that (1) uses LLMs with optional human guidance to formalize natural language policies, allowing fine-grained control of the formalization process, and (2) uses inference-time autoformalization to validate logical correctness of natural language statements against those policies. ARc performs multiple redundant formalization steps at inference time, checking the formalizations for semantic equivalence. Our benchmarks show that ARc exceeds 99% soundness and achieves a near-zero false positive rate in identifying logical validity. Our approach produces auditable artifacts that substantiate the verification outcomes and can be used to improve the original text. ARc is the first commercial offering from a major cloud provider to integrate automated reasoning into a generative AI guardrail.
Chenyang An, Sam Bayless, Stefano Buliani, Darion Cassel, Byron Cook, Duncan Clough, Rémi Delmas, Nafi Diallo, Ferhat Erata, Nick Feng, Dimitra Giannakopoulou, Aman Goel, Aditya Gokhale, Joe Hendrix, Victor Heorhiadi, Marc Hudak, Dejan Jovanovic, Andrew M. Kent, Benjamin Kiesl-Reiter, Jeffrey J. Kuna, Nadia Labai, Joe Lilien, Divya Raghunathan, Zvonimir Rakamaric, Niloofar Razavi, Michael Tautschnig, Ali Torkamani, Nathaniel Weir, Michael W. Whalen, Jianan Yao
CAV (2)22
2023 Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Shuaichen Chang, Jun Wang 0122, Mingwen Dong, Lin Pan 0003, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang 0029, Jiarong Jiang, Joe Lilien, Steve Ash, William Yang Wang, Zhiguo Wang 0006, Vittorio Castelli, Patrick Ng, Bing Xiang
ICLR10