4 papers
Exploring Pretraining via Active Forgetting for Improving Cross Lingual Transfer for Decoder Language Models
Divyanshu Aggarwal, Ashutosh Sathe, Sunayana Sitaram
Large Language Models (LLMs) demonstrate exceptional capabilities in a multitude of NLP tasks. However, the efficacy of such models to languages other than English is often limited…
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models
Divyanshu Aggarwal, Ashutosh Sathe, Ishaan Watts +1
Parameter Efficient Finetuning (PEFT) has emerged as a viable solution for improving the performance of Large Language Models (LLMs) without requiring massive resources and compute…
Improving Self Consistency in LLMs through Probabilistic Tokenization
Ashutosh Sathe, Divyanshu Aggarwal, Sunayana Sitaram
Prior research has demonstrated noticeable performance gains through the use of probabilistic tokenizations, an approach that involves employing multiple tokenizations of the same…
A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models
Ashutosh Sathe, Prachi Jain, Sunayana Sitaram
Vision-language models (VLMs) have gained widespread adoption in both industry and academia. In this study, we propose a unified framework for systematically evaluating gender, rac…