2 citations · 3 across the 11 of their papers we have counts for
12 papers
Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks
Vivek Dahiya, Sunny Nehra, Vipul Dholariya +2
We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and…
Chitrakshara: A Large Multilingual Multimodal Dataset for Indian languages
Shaharukh Khan, Ali Faraz, Abhinav Ravi +6
Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image underst…
BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages
Guduru Manoj, Neel Prabhanjan Rachamalla, Ashish Kulkarni +8
In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particula…
IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs
Ali Faraz, Akash, Shaharukh Khan +6
Vision-language models (VLMs) have demonstrated impressive generalization across multimodal tasks, yet most evaluation benchmarks remain Western-centric, leaving open questions abo…
Seeing Straight: Document Orientation Detection for Efficient OCR
Suranjan Goswami, Abhinav Ravi, Raja Kolla +5
Despite significant advances in document understanding, determining the correct orientation of scanned or photographed documents remains a critical pre-processing step in the real…
MUTANT: A Recipe for Multilingual Tokenizer Design
Souvik Rana, Arul Menezes, Ashish Kulkarni +2
Tokenizers play a crucial role in determining the performance, training efficiency, and the inference cost of Large Language Models (LLMs). Designing effective tokenizers for multi…
Pragyaan: Designing and Curating High-Quality Cultural Post-Training Datasets for Indian Languages
Neel Prabhanjan Rachamalla, Aravind Konakalla, Gautam Rajeev +3
The effectiveness of Large Language Models (LLMs) depends heavily on the availability of high-quality post-training data, particularly instruction-tuning and preference-based examp…
VoiceAgentBench: Are Voice Assistants ready for agentic tasks?
Dhruv Jain, Harshit Shukla, Gautam Rajeev +3
Large scale Speech Language Models have enabled voice assistants capable of understanding natural spoken queries and performing complex tasks. However, existing speech benchmarks l…
Chitranuvad: Adapting Multi-Lingual LLMs for Multimodal Translation
Shaharukh Khan, Ayush Tarun, Ali Faraz +7
In this work, we provide the system description of our submission as part of the English to Lowres Multimodal Translation Task at the Workshop on Asian Translation (WAT2024). We in…
Krutrim LLM: Multilingual Foundational Model for over a Billion People
Aditya Kallappa, Palash Kamble, Abhinav Ravi +10
India is a diverse society with unique challenges in developing AI systems, including linguistic diversity, oral traditions, data accessibility, and scalability. Existing foundatio…
Chitrarth: Bridging Vision and Language for a Billion People
Shaharukh Khan, Ayush Tarun, Abhinav Ravi +7
Recent multimodal foundation models are primarily trained on English or high resource European language data, which hinders their applicability to other medium and low-resource lan…
Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing
Rahul Kumar, Shubham Kakde, Divyansh Rajput +6
We present a novel approach to data preparation for developing multilingual Indic large language model. Our meticulous data acquisition spans open-source and proprietary sources, i…