Labelled Deductive Systems for Logical Validation of LLM Output

TALK LOGISTICS:
Monday, April 20, 2026
6:30 registration, food, networking.
7:00 SFbayACM upcoming events, introduce the speaker
7:10 to 8:15 or 8:30 presentation
The Zoom and YouTube links will be provided here about 2-3 days before the event
SFbayACM will support a local audience at VRP in Mountain View

TALK DESCRIPTION:
Large language models (LLMs) often generate fluent but in-correct or unsupported statements, commonly referred to as hallucinations. We propose a hallucination detection frame-work ValidLLP4LLM based on a Labeled Logic Program (LLP) architecture that integrates multiple reasoning paradigms, including logic programming, argumentation, probabilistic inference, and abductive explanation. By enriching symbolic rules with semantic, epistemic, and contextual labels and applying discourse-aware weighting, the system prioritizes nucleus claims over peripheral statements during verification. Experiments on three benchmark datasets and a challenging clinical narrative dataset show that LLP consistently outperforms classical symbolic validators, achieving the highest detection accuracy when combined with discourse modeling. A human evaluation further demonstrates that logic-assisted explanations improve both hallucination detection ac-curacy and user trust. The results suggest that labeled symbolic reasoning with discourse awareness provides a robust and interpretable approach to LLM verification in safety-critical domains.

SPEAKER BIO:
Prof. Boris Galitsky has contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. His information extraction and sentiment
analysis techniques assisted several acquisitions, such as Xoopit by Yahoo, Uptake by Groupon, Loglogic by Tibco, and Zvents by eBay. His security-related technologies of document analysis contributed to the acquisition of Elastica by Semantec.

As an architect of the Intelligent Bots project at Oracle, he developed a discourse analysis technique used for dialogue management and published in the book Developing Enterprise Chatbots. He also published a two-volume monograph, “AI for CRM”, based on his experience developing Oracle Digital Assistant. He is an Apache committer to OpenNLP, where he created OpenNLP. Similarity component, which is a basis for a semantically enriched search engine and chatbot development.

His exploration and formalization of human reasoning culminated in the book AQ1 Computational Autism broadly used by parents of children with autism spectrum disorder and rehabilitation personnel. His
focus on the medical domain led to another research monograph, “Artificial Intelligence for Healthcare Applications and Management”. He is a now a lead researcher at Moscow Institute for Physics and Technology, Russia
https://www.linkedin.com/in/boris-galitsky-342109204/

#ACM #SFbayACM #AI #GenAI #DataScience #LLM
#Hallucinations #LogicProgramming #NLP #Discourse #Benchmark