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NEW! Get a glimpse of how ElectionBots, built with SafeChat, are being used in the South Carolina Elections 2024! See demo and participate in user studies.

SafeChat
Collaborators: Vishal Pallagani, Kausik Lakkaraju, Bharath Muppasani, Biplav Srivastava
SafeChat is an architecture that aims to provide a safe and secure environment for users to interact with AI chatbots in trust-sensitive domains. It uses a combination of neural (learning-based, including generative AI) and symbolic (rule-based) methods, together called a neuro-symbolic approach, to provide known information in easy-to-use consume forms that are adapted from user interactions (provenance). The chatbots generated are scalable, quick to build and is evaluated for trust issues like fairness, robustness and appropriateness of responses.

Representative Publications

  • On Safe and Usable Chatbots for Promoting Voter Participation
    AAAI AI Magazine
    Bharath Muppasani, Vishal Pallagani, Kausik Lakkaraju, Shuge Lei, Biplav Srivastava, Brett Robertson, Andrea Hickerson, Vignesh Narayanan
    [Paper]
  • LLMs for Financial Advisement: A Fairness and Efficacy Study in Personal Decision Making
    4th ACM International Conference on AI in Finance: ICAIF'23, New York, 2023
    Kausik Lakkaraju, Sara Rae Jones, Sai Krishna Revanth Vuruma, Vishal Pallagani, Bharath C Muppasani and Biplav Srivastava
    [Paper] [Slides]
  • Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik’s Cube
    ICML 2023 TEACH Conversational AI Workshop, Hawaii, 2023
    Kausik Lakkaraju, Vedant Khandelwal, Biplav Srivastava, Forest Agostinelli, Hengtao Tang, Prathamjeet Singh, Dezhi Wu, Matt Irvin, Ashish Kundu
    [Paper]

Representative Activities

  • Use of SafeChat in CSCE 580 (Fall 2023) to create chatbot interface for Water Quality Decider System.
  • Use of SafeChat in creating Garnet n Talk, a chatbot designed to answer questions about the University of South Carolina. This project was awarded Best Idea in the USC Spring 2024 Hackathon.
  • Use of SafeChat for chatbots in education, election (Mississippi).

Generic Information Retrieval Chatbot using Planning & RL
Collaborators: Vishal Pallagani, Biplav Srivastava
In this body of work, we address the challenge of enabling users to effectively search large datasets, such as product catalogs and open data, using natural language. Many users find it difficult to interact with these datasets due to unfamiliarity with query languages. Our system allows users to retrieve information through a dialog-based interface that adapts to the underlying structure of the data, making it versatile across various domains. By incorporating planning techniques and reinforcement learning (RL), the system can learn and optimize its search strategies over time. We demonstrate the system’s effectiveness using datasets such as UNSPSC and ICD-10, highlighting its capability to enhance information retrieval through the combined use of planning and RL.

Representative Publications

  • A generic dialog agent for information retrieval based on automated planning within a reinforcement learning platform
    Bridging the Gap Between AI Planning and Reinforcement Learning (PRL) Workshop at ICAPS, 2021
    [Paper] [BibTex] [Poster] [Github]
  • PRUDENT-A Generic Dialog Agent for Information Retrieval That Can Flexibly Mix Automated Planning and Reinforcement Learning
    ICAPS Demonstration Track, 2021
    [Paper] [BibTex] [Demo] [Github]

For more details, visit: https://sites.google.com/site/biplavsrivastava/research-1/dialog