176 citations · 360 across the 13 of their papers we have counts for
15 papers
Scaling Laws for Multimodal Data Mixtures
Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu +5
Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the m…
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
Nivya Talokar, Ayush K Tarun, Murari Mandal +2
LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios…
Revisiting Multilingual Data Mixtures in Language Model Pretraining
Negar Foroutan, Paul Teiletche, Ayush Kumar Tarun +1
The impact of different multilingual data mixtures in pretraining large language models (LLMs) has been a topic of ongoing debate, often raising concerns about potential trade-offs…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling Opt-Outs
Dongyang Fan, Vinko Sabolčec, Matin Ansaripour +4
The increasing adoption of web crawling opt-outs by copyright holders of online content raises critical questions about the impact of data compliance on large language model (LLM)…
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…
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…
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova +56
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal val…
A Unified Framework for Continual Learning and Unlearning
Romit Chatterjee, Vikram Chundawat, Ayush Tarun +2
Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while…
EcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +3
Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley va…
Deep Regression Unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +1
With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few…
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +2
Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality…
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +1
Machine unlearning has become an important area of research due to an increasing need for machine learning (ML) applications to comply with the emerging data privacy regulations. I…
Zero-Shot Machine Unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +1
Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion o…
Fast Yet Effective Machine Unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +1
Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-bas…