Registrations are open for Axis Bank Large Language Models Bankathon 2023. The last date of registration is 10 August.

About the Bankathon

Step into the future of AI with the two-phase LLM Bankathon by Axis Bank! This event presents an exhilarating opportunity to combine the power of Large Language Models (LLMs) and the dynamic world of banking. Coders, innovators, and visionaries, gear up for an epic adventure that could reshape the future of FinTech.

Hackathon Phases

This is a two-phased hackathon with the preliminary round (online) being based on the theme given in this page, and the final round being conducted with only the shortlisted participants afterwards wherein the theme will be shared on the event day in Bangalore (which will broadly be based on leveraging LLMs).

  • Online Hackathon (July 14th – August 10th, 2023): Phase 1 is a virtual round where teams all across India will compete online, harnessing the capabilities of Large language models (like ChatGPT, etc) to solve an interesting problem with brownie points for being creative and thinking outside the box.
  • Onsite Grand Finale (September 15th & 16th, 2023): The best teams from the virtual round will move forward to Phase 2, an action-packed, on-premise round taking place over a thrilling weekend. In this grand finale, you will get to pick one of the key problems in banking world to solve using GPT like Large language models , network with like-minded peers and industry experts, and ultimately compete for the title of Axis Bank’s LLM Bankathon Champion.

The LLM Bankathon by Axis bank is not just a hackathon. It’s a journey of learning and innovation, an adrenaline-fueled race against time, and a chance to redefine the boundaries of AI in banking. Are you ready to accept the challenge?

Join us to code today for a smarter bank tomorrow at Axis Bank’s LLM Bankathon. Don’t miss out on your chance to be a part of the revolution! 

Eligibility

This hackathon is open to any Student, Working Professional, or Freelancer with a passion for coding and solving current banking problems using Large language models.

Important dates

Themes

Tech stack

  • Open to all
  • Must use a Large language model for the given problem

Task 

  • Input: Accept job description and multiple CVs in various formats.
  • Job Description evaluation
    • Score the job description based on the job title, and provide recommendations for enhancements
    • Give the user the option to either continue with the original version or incorporate the suggested changes
  • CV ranking
    • Rank the CVs according to their alignment with the job requirements and shortlist candidates
    • Provide additional information on the shortlisted candidates
  • Email notifications: Send emails to the shortlisted candidates, informing them about the next steps in the hiring process
  • Screening questions
    Develop screening questions for each candidate, considering different levels of importance or significance assigned to the job description and the candidate’s CV
  • First-round interview
    • Conduct the first-round of interview
    • Record the candidate’s responses to the screening questions
    • Evaluate their performance for consideration in the next in-person round
  • Communication
    • Maintain continuous communication with the HR team
    • Providing updates on the hiring process and relevant information throughout

Submission format 

  • Source code in zip file
  • Dataset used
  • Demo video of the project
  • Important files, PDF, resources used and ppt

Resources

Evaluation metrics

  • User Interface (UI) Design and Information Extraction (10 points)
    • Quality of the UI design, including but not limited to:
      • Visual appeal
      • Ease of navigation
      • Overall user experience
    • Your prototype’s ability to extract relevant information accurately from resumes
  • Algorithm development for candidate selection and accuracy (20 points)
    • Ability of the algorithm used to provide accurate and meaningful results that align with the project’s objectives
    • Effectiveness of the algorithm used for candidate selection, ensuring it can accurately rank candidates based on their qualifications and suitability for specific job roles
  • Question generation and alignment to Candidate (20 points)
    • Prototype’s ability to generate relevant and appropriate interview questions based on candidate profiles or job requirements
    • How well the generated questions align with each candidate’s qualifications and how effectively they assess their suitability for the role.
  • Screening Interview and Evaluation (20 points)
    • Screening interview process, including its structure, effectiveness, and ability to assess candidate skills and fit
    • Captures and evaluates candidate responses during the interview process
  • Speed (10 points)
    • Speed and responsiveness of the project, ensuring it can handle a reasonable number of candidates or resumes efficiently
    • How quickly the prototype performs tasks and provides results without compromising accuracy
  • Value adds (20 points)
    • Identify additional features, functionalities, or innovative aspects that the prototype offers beyond the basic requirements
    • How these value-adds contribute to enhancing the HR process and overall project goals.

Prizes

Winning teams would be contacted post-conclusion of the onsite hackathon finale regarding the steps to claim their prizes.

Click here to register for Axis Bank Large Language Models Bankathon 2023.