Rocktim Jyoti Das

I am a Research Associate II at MBZUAI in Abu Dhabi working on reasoning and planning under the supervison of Prof. Ivan Laptev and Prof. Preslav Nakov. I am interested in developing agents that reason, plan and interact with the environment to solve complex tasks using the world knowledge acquired using internet scale data.

I completed my undergraduate studies at the Indian Institute of Technology, Delhi, which set the foundation for my academic journey. Subsequently, I had the incredible opportunity to spend a year at the DAIR Lab, IIT Delhi as a Project Scientist under the supervision of Prof. Mausam. As a researcher at DAIR lab, I focused on Task-Oriented Dialog Systems and Conversation based Medical Diagnosis.

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News

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Selected Research

* denotes joint first authors

FLIP-diag MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation
Harsh Singh*, Rocktim Jyoti Das*, Mingfei Han, Preslav Nakov, Ivan Laptev,
submitted to ICRA, 2025
FLIP-diag Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems
Vishal Vivek Saley, Rocktim Jyoti Das, Dinesh Raghu, Mausam,
accepted in EMNLP main, 2024
FLIP-diag MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations
Vishal Saley, Goonjan Saha, Rocktim Jyoti Das, Dinesh Raghu, Mausam,
accepted in EMNLP main, 2024
FLIP-diag EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov,
accepted in ACL main, 2024
FLIP-diag Exploring Distributional Shifts in Large Language Models for Code Analysis
Shushan Arakelyan, Rocktim Jyoti Das, Yi Mao, Xiang Ren,
accepted in EMNLP main, 2023
FLIP-diag DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies
Vishal Saley, Rocktim Jyoti Das, Dinesh Raghu, Mausam,
accepted in ACL Findings, 2023

Open-Sourced Project

Nanda is the leading open-sourced Hindi bilingual model for generation and reasoning, surpassing current models on safety benchmarks. Although our model is optimized for Devanagari Hindi, it also seamlessly supports Romanized and code-mixed Hindi, making it highly versatile for real-world applications. Check out the paper for more details about training, evaluation!

📄 Paper link

Give our model a try!

Download LLaMA-3-Nanda-10B weights from Hugging Face and see its capabilities in action:

💾 Model weights



Source code from Jon Barron's website.