ArguMentor

Hello! We are ArguMentor, and our application productively challenges your arguments rather than agreeing with them.

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Inspiration

We believe that most LLMs are too agreeable, which is nice but not incredibly conducive to all use cases. That's where ArguMentor steps in - our application actively debates with you over your ideas to highlight flaws and weaknesses in your argument.

What it does

Our application provides users with agents that can portray different scenarios, enabling users to find a customized scenario for their situation.

How we built it

We fine-tuned our LLM to reflect more critical thinking with an Anthropic dataset using Together.ai. Additionally, we created a second model that was fine-tuned using Monster API. We also iterated on prompts to develop useful and generalizable AI agents for educational purposes, including simulated audience members for a panel discussion. Our front-end was modeled off of a Convex template and our backend is also powered by Convex.

Challenges we ran into

This was our first hackathon, and we were relatively new to full-stack development, so we're proud of the progress and product we created. Some challenges we faced included: model fine-tuning and dataset formatting, multi-agent LLM tuning and prompt engineering, and integrating the front end with LLM API calls.

Accomplishments that we're proud of

We are proud by the emergent multi-agent conversations our system is capable of. Particularly, different agents are capable of responding to one another as if they were scholars debating a research topic. Additionally, we are proud of using LLM's in an unconventional light, guiding them to be less agreeable and more honest in helping users strengthen their logic and prepare for challenging situations.

What we learned

We learned a great deal: front-end development, fine-tuning and prompt-engineering for LLM's, multi-agent LLM interactions, and full-stack development.

What's next for ArguMentor

We will continue iterating upon our models for better robustness and generalizability. We have many exciting ideas in education and sustainability applications related to this project!

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