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Truth-O-Meter: Fact-checking content generated by a LLM

June 26 @ 7:00 pm - 8:30 pm

ZOOM LINK AND LOCATION ADDRESS

AGENDA
7:00 SFbayACM intro, upcoming events, introduce the speaker
7:10 presentation starts (~90 min with Q&A)

ABSTRACT
A text obtained by a Large Language Model (LLM) such as GPT4 usually has issues in terms of incorrectness and hallucinations. We build a fact-checking system ‘Truth-O-Meter’ which identifies wrong facts, comparing the generation results with the web and other sources of information, and suggests corrections. Text mining and web mining techniques are leveraged to identify correct corresponding sentences; also, the syntactic and semantic generalization procedure adopted to the content improvement task. To handle inconsistent sources while fact-checking, we rely on an argumentation analysis in the form of defeasible logic programming. We compare our fact checking engine with competitive approach based on reinforcement learning on top of LLM or token-based hallucination detection. Our approach is an instance of what we call “Shaped-charge learning architecture” which is intended to combine an efficient LLM with explainable inductive learning. It is observed that LLM content can be substantially improved for factual correctness and meaningfulness.
[https://github.com/bgalitsky/Truth-O-Meter-Making-ChatGPT-Truthful](https://github.com/bgalitsky/Truth-O-Meter-Making-ChatGPT-Truthful)

SPEAKER BIO
Boris Galitsky contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. Boris’ information extraction and sentiment analysis techniques assisted a number of 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 acquisition of Elastica by Semantec. [https://github.com/bgalitsky/relevance-based-on-parse-trees](https://github.com/bgalitsky/relevance-based-on-parse-trees)
As an architect of the Intelligent Bots project at Oracle, Boris developed a discourse analysis technique user 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. Boris is Apache committer to OpenNLP where he created OpenNLP.Similarity component which is a basis for a semantically-enriched search engine and chatbot development.
Galitsky’s exploration and formalization of human seasoning culminated in the book “Computational Autism” broadly used by parents of children with autistic reasoning and rehabilitation personnel. Boris focus on medical domain led to another research monograph, “AI for Health Applications and Management”.
[https://www.amazon.com/Books-Boris-Galitsky/s?rh=n%3A283155%2Cp_27%3ABoris+Galitsky](https://www.amazon.com/Books-Boris-Galitsky/s?rh=n%3A283155%2Cp_27%3ABoris+Galitsky)
An Author of 150+ publications, 50+ patents and 6 books, Boris’s focus now is on improving content generation quality.
https://www.linkedin.com/in/boris-galitsky-342109204/

Event topics
Content Management Text Analytics Automated Reasoning Chatbots Data Analytics

Details

Date:
June 26
Time:
7:00 pm - 8:30 pm
Website:
https://www.meetup.com/sf-bay-acm/events/294097673

Venue

Online event