1 citations · 1 across the 5 of their papers we have counts for
8 papers
SCORE: Specificity, Context Utilization, Robustness, and Relevance for Reference-Free LLM Evaluation
Homaira Huda Shomee, Rochana Chaturvedi, Yangxinyu Xie +1
Large language models (LLMs) are increasingly used to support question answering and decision-making in high-stakes, domain-specific settings such as natural hazard response and in…
Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection
Yangxinyu Xie, Xuyang Chen, Zhimei Ren +1
As artificial intelligence tools become ubiquitous in education, maintaining academic integrity while accommodating pedagogically beneficial AI assistance presents unprecedented ch…
GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?
Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang +6
We present GeoGrid-Bench, a benchmark designed to evaluate the ability of foundation models to understand geo-spatial data in the grid structure. Geo-spatial datasets pose distinct…
Comparative Evaluation of Prompting and Fine-Tuning for Applying Large Language Models to Grid-Structured Geospatial Data
Akash Dhruv, Yangxinyu Xie, Jordan Branham +1
This paper presents a comparative study of large language models (LLMs) in interpreting grid-structured geospatial data. We evaluate the performance of a base model through structu…
Foundations of Top- Decoding For Language Models
Georgy Noarov, Soham Mallick, Tao Wang +5
Top- decoding is a widely used method for sampling from LLMs: at each token, only the largest next-token-probabilities are kept, and the next token is sampled after re-norma…
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation
Yangxinyu Xie, Bowen Jiang, Tanwi Mallick +10
Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressi…