5 papers · 1 filter
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…
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…
MAFIA: Multi-Adapter Fused Inclusive LanguAge Models
Prachi Jain, Ashutosh Sathe, Varun Gumma +2
Pretrained Language Models (PLMs) are widely used in NLP for various tasks. Recent studies have identified various biases that such models exhibit and have proposed methods to corr…
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…
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma +8
There has been a surge in LLM evaluation research to understand LLM capabilities and limitations. However, much of this research has been confined to English, leaving LLM building…