9 citations · 19 across the 10 of their papers we have counts for
6 papers · 1 filter
GiVA: Gradient-Informed Bases for Vector-Based Adaptation
Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky +4
As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent…
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models
Neeraj Gangwar, Suma P Bhat, Nickvash Kani
While large models pre-trained on high-quality data exhibit excellent performance on mathematical reasoning (e.g., GSM8k, MultiArith), it remains challenging to specialize smaller…
STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing
Jiaru Zou, Qing Wang, Pratyush Thakur +1
Advances in large language models (LLMs) have spurred research into enhancing their reasoning capabilities, particularly in math-rich STEM (Science, Technology, Engineering, and Ma…
Mathematical Derivation Graphs: A Relation Extraction Task in STEM Manuscripts
Vishesh Prasad, Brian Kim, Nickvash Kani
Recent advances in natural language processing (NLP), particularly with the emergence of large language models (LLMs), have significantly enhanced the field of textual analysis. Ho…
Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems
Neeraj Gangwar, Nickvash Kani
Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical soft…
Semantic Representations of Mathematical Expressions in a Continuous Vector Space
Neeraj Gangwar, Nickvash Kani
Mathematical notation makes up a large portion of STEM literature, yet finding semantic representations for formulae remains a challenging problem. Because mathematical notation is…