2 citations · 2 across the 1 of their papers we have counts for
5 papers
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal +2
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages…
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts
Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh +7
Effective human-robot collaboration requires the ability to learn personalized concepts from a limited number of demonstrations, while exhibiting inductive generalization, hierarch…
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…