1 citations · 1 across the 9 of their papers we have counts for
4 papers · 2 filters
State Space Models are Strong Text Rerankers
Zhichao Xu, Jinghua Yan, Ashim Gupta +1
Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Am…
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
Zhichao Xu, Ashim Gupta, Tao Li +2
Increasingly, model compression techniques enable large language models (LLMs) to be deployed in real-world applications. As a result of this momentum towards local deployment, com…
An Empirical Investigation of Matrix Factorization Methods for Pre-trained Transformers
Ashim Gupta, Sina Mahdipour Saravani, P. Sadayappan +1
The increasing size of transformer-based models in NLP makes the question of compressing them important. In this work, we present a comprehensive analysis of factorization based mo…
Enhancing Question Answering on Charts Through Effective Pre-training Tasks
Ashim Gupta, Vivek Gupta, Shuo Zhang +3
To completely understand a document, the use of textual information is not enough. Understanding visual cues, such as layouts and charts, is also required. While the current state-…