Project Info
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 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
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!
ArguMentor
Hello! We are ArguMentor, and our application productively challenges your arguments rather than agreeing with them.
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.
We see our application functioning primarily in the education space - whether being used to prepare for thesis defenses, research panels, or class discussions, our application provides users with questions that challenge ideas and provoke more critical thought.
Educational Use Cases
Our application provides users with agents that can portray different scenarios, enabling users to find a customized scenario for their situation.
- A researcher preparing for a research panel might use our application to simulate a Q&A session to prepare for questions that might be asked
- A PhD student might use our application to get ready to defend their thesis
- Even in everyday scenarios, our application can be useful for simulating discussions involving multiple viewpoints to providing counterarguments for an essay.
If you're looking for something even more custom, you have the ability to even create custom agents yourself!
How to Use
First, choose an agent that you want to simulate a situation with. After, input information about your situation - i.e. if the situation is a research fair, a class discussion, etc. Then, sit back and let our platform work its magic!
Resources Used
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. Our front-end was modeled off of a Convex template and our backend is also powered by Convex.
Analysis
View
Metric
- 20
- 16
- 15
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- HTMLIn code
- JavaScriptIn code
- OpenAIIn code
- ReactIn code
- TypeScriptIn code
6 of 6 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
47 KB
Source files
21
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
Bri-Guy/arguMentor
37 files · 1.5 MB · @ f687219
Structure
Interface
3 files · 8%Screens, components and styles rendered to the user.
Application logic
16 files · 43%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript76%
- Markdown10%
- JavaScript8%
- CSS5%
- HTML1%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
package.json
npm · 16- convex
- openai
- react
- react-dom
- +12 more
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
Export this project's context (description, README, evidence, key source files) to chat with an AI agent elsewhere.