204 citations · 243 across the 16 of their papers we have counts for
17 papers
You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come From
Calvin Zhou, Vincent McCloskey, Krishna Srinivasan
Questions scraped from the web are used across academia and industry as a proxy for what people want to know. Across QA training data, retrieval benchmarks, and content strategy, q…
LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model
Tao Sun, Oliver Liu, JinJin Li +1
Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring res…
Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan +3
The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously imprac…
Entity Image and Mixed-Modal Image Retrieval Datasets
Cristian-Ioan Blaga, Paul Suganthan, Sahil Dua +6
Despite advances in multimodal learning, challenging benchmarks for mixed-modal image retrieval that combines visual and textual information are lacking. This paper introduces a no…
Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
Krishna Prasad Varadarajan Srinivasan, Prasanth Gumpena, Madhusudhana Yattapu +1
In the domain of large language models (LLMs), arXiv:2305.16938 showed that few-shot full-model fine-tuning -- namely Vanilla Fine Tuning (FT) and Pattern-Based Fine Tuning (PBFT)…
Automatic Multi-Path Web Story Creation from a Structural Article
Daniel Nkemelu, Peggy Chi, Daniel Castro Chin +2
Web articles such as Wikipedia serve as one of the major sources of knowledge dissemination and online learning. However, their in-depth information--often in a dense text format--…