Source: unstop Remote / Online
The Liar's Chatroom
Organized by International Institute of Information Technology (IIIT), Bangalore•university•India
DEADLINEOct 5, 2026Registration Closed
EVENT DATESOct 5 - Nov 12Timeline window
PRIZE POOL🏆 Certificates & SwagVerified bounties
The Liar's Chatroom
About the Opportunity
Uncover the Deceiver in a 48-Hour AI & NLP Kaggle Challenge!About the EventStep into The Liar's Chatroom, a fast-paced 48-hour online Kaggle competition where NLP meets digital forensics!Participants will build an AI model to analyze fictional group-chat conversations, examine verified evidence, and identify which character is lying. Each conversation features four characters (A, B, C, and D), with exactly one character making a statement that contradicts the facts. Using a custom synthetic dataset, your mission is to train a model that spots contradictions with high accuracy.️ Event Format & DatasetDataset: Participants receive a custom synthetic dataset containing labeled training data and unlabeled test data.️ Approach: You may use any Machine Learning or NLP approach—including custom architectures, pretrained models, LLMs, fine-tuning, or rule-based heuristics.Platform: Hosted externally on Kaggle.Rules & RegulationsTeam Size: Compete individually or in teams of up to 4 members.Submission Limit: Maximum of 5 submissions per team per day on Kaggle.Submission Format: Predictions must be uploaded in the prescribed CSV format before the deadline.Fair Play: Manual labeling of the test dataset and sharing predictions/code between competing teams are strictly prohibited.Code & Methodology Submission: Top-performing teams on the private leaderboard must submit a brief explanation/repository of their methodology and may be asked to present their approach to qualify for prizes.Evaluation CriteriaPrimary Metric: Evaluation is based strictly on Classification Accuracy on the dataset.Final Ranking: Official winners are determined by the Private Kaggle Leaderboard following post-competition code verification.
Relevant Tech & Domains
AI / Machine Learning
Score Composition (0–100)
Hiring Potential (25%)30%
Organizer Prestige (20%)85%
Accessibility & Format (15%)100%
Data Quality (10%)85%