35 citations · 69 across the 8 of their papers we have counts for
4 papers · 1 filter
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu +2
The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern language modeling. With the understanding that task adaptation in LMs is often a…
Laying Anchors: Semantically Priming Numerals in Language Modeling
Mandar Sharma, Rutuja Murlidhar Taware, Pravesh Koirala +2
Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks. However, the inability of these models to properly…
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency
Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and s…
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic
Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks. Tho…