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How to Build Personalization into LLM Recommendations
October 23 @ 7:00 pm - 8:30 pm
855 Maude Ave,
Mountain View, CA 94043
This is a hybrid meeting, you can join remotely and submit questions via Zoom QnA. The zoom link will be provided (TBD).
6:30 Door opens, Food
7:00 SFBayACM upcoming events, introduce the speaker
7:20 presentation starts (~90 min with Q&A)
We enable Large Language Models (LLM) with personalization capability. This is not specific to the LLM (Open AI’s ChatGPT, Athropic’s Claude, Meta’s Llama 2, Googles,…)
Today, LLMs are not good at personalization and providing recommendations. They may advise physicians and financial advisors to “ask professionals” in their respective fields for help, even having user information available. When answering questions for software professionals, the LLM may need to deliver in-depth answers with code or algorithms, whereas for professionals in other fields would need definitions and main concepts.
The intent of this project is to make LLMs provide answers tailored to the needs of a specific user, taking into account available information about that individual. To do that, we need to generalize available documents about a person. Based on the needs of the application and with the permission of the individual being served, information used could include: their LinkedIn profile, visited web pages, investment history extracted from tax documents, and health forms (while maintaining the privacy of this person). We rely on meta-learning techniques to design an LLM prompt to produce a personalization prompt to obtain suitable relevant information. Such a “meta-prompt” is produced by a generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization, on one hand, and will maintain user privacy on the other hand.
A personalization profile is built from the link provided by the user.
Then, given a user question, this system will use the LLM to generate a set of queries. The URLs from search results are stored internally in a self.urls. A check is performed for any new URLs that haven’t been processed yet (not in self.url_database). Only these new URLs are loaded, transformed, and added to the vector store. The vector store is queried for relevant documents based on the questions generated by the LLM. Only unique documents are returned as the final result.
This project build is in [https://github.com/langchain-ai/web-explorer](https://github.com/langchain-ai/web-explorer)
Boris is working at the stealth mode startup, Cybernator.
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”.
An Author of 150+ publications, 50+ patents and 6 books, Boris’s focus now is on improving content generation quality.