5 papers
Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection
Pritam Kadasi, Anuj Tiwari, Mayank Singh
Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary…
When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models
Sailesh Panda, Pritam Kadasi, Abhishek Upperwal +1
Large language models (LLMs) often achieve strong performance on reasoning benchmarks, but final-answer accuracy alone does not show whether they faithfully execute the procedure s…
Task--Specificity Score: Measuring How Much Instructions Really Matter for Supervision
Pritam Kadasi, Abhishek Upperwal, Mayank Singh
Instruction tuning is now the default way to train and adapt large language models, but many instruction--input--output pairs are only weakly specified: for a given input, the same…
ADAPT: Learning Task Mixtures for Budget-Constrained Instruction Tuning
Pritam Kadasi, Abhishek Upperwal, Mayank SIngh
We propose ADAPT, a meta-learning algorithm that \emph{learns} task sampling proportions under an explicit token budget for multi-task instruction tuning. Instead of fixing task we…
Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation
Pritam Kadasi, Sriman Reddy Kondam, Srivathsa Vamsi Chaturvedula +7
With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on m…