Framework for Reduction of Ambiguity in Text Data from Generative AI

Submission Number: 191
Submission ID: 4361
Submission UUID: 075cb4fc-4a23-4388-a6d4-668a2d2f65e5
Submission URI: /form/project

Created: Mon, 02/12/2024 - 13:20
Completed: Mon, 02/12/2024 - 13:34
Changed: Wed, 09/04/2024 - 15:34

Remote IP address: 128.6.36.2
Submitted by: Udi Zelzion
Language: English

Is draft: No
Webform: Project
Framework for Reduction of Ambiguity in Text Data from Generative AI
CAREERS
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ai (271), natural-language-processing (274), python (69)
In Progress

Project Leader

Jim Samuel
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Project Personnel

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Kushal Gunavantkumar Patel
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Project Information

Generative AI has invaded our places of work and learning with the promise of increasing productivity.
However, many generative AIs are built on (Large Language Model) LLMs which act as next-wordpredictors
based on probabilistic modeling. This leads to numerous challenges, especially ambiguity.
This proposal addresses the research question: How can we reduce ambiguity in AI generated text?
The current proposal seeks to 1) identify ways to algorithmically identify and flag ambiguity, and 2)
explore identifying levels of ambiguity and 3) explore ways in which ambiguity could be reduced or
managed.
Once ambiguity is identified, we intend to use a LLM application to generate improved alternatives. This
project will help improve the quality of human interactions with AI applications such as chatbots.

Project Information Subsection

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Practical applications
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CR-Rutgers
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Yes
Already behind3Start date is flexible
6
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  • Milestone Title: Launch
    Milestone Description: Give a launch presentation during the monthly meeting get HPC access and explore and validate multiple LLMs on ready to use datasets
    Completion Date Goal: 2024-04-19
    Actual Completion Date: 2024-05-31
  • Milestone Title: Identify ambiguity
    Milestone Description: Narrow down on most promising approaches to identify ambiguity and run tests
    Completion Date Goal: 2024-06-01
    Actual Completion Date: 2024-07-15
  • Milestone Title: Finetuning
    Milestone Description: Apply Finetuning, and other customizations to the LLMs to generate suitable text
    Completion Date Goal: 2024-07-16
    Actual Completion Date: 2024-08-31
  • Milestone Title: Finalize Documentation
    Milestone Description: Produce a workflow that Write a white paper, prepare presentation and package any other deliverable/s.
    Completion Date Goal: 2024-09-01
    Actual Completion Date: 2024-10-01
  • Milestone Title: Wrap presentation
    Milestone Description: Give a wrap presentation at the monthly meeting and have an exit interview.
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The student will gain familiarity with Rutgers' HPC system, Amarel, and understand how to run NLP analysis using Amarel.
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Jupyter notebooks with examples on how to run NLP analysis.
Access to the Amarel cluster, Rutgers' HPC system.
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Final Report

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