Search Results for author:
Found 11 papers, 9 papers with code
GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?
We present GeoGrid-Bench, a benchmark designed to evaluate the ability of foundation models to understand geo-spatial data in the grid structure.
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation
In this work we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events.
Information Retrieval for Climate Impact
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts.
Debiasing Watermarks for Large Language Models via Maximal Coupling
Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communication.
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression
We propose a computationally efficient algorithm for gradient-based linear dimension reduction and high-dimensional regression.
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners
This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias.
Towards Rationality in Language and Multimodal Agents: A Survey
This work discusses how to build more rational language and multimodal agents and what criteria define rationality in intelligent systems.
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation
Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events.
A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios
While accurate forecasting of regular traffic conditions is crucial, a reliable AI system must also accurately forecast congestion scenarios to maintain safe and efficient transportation.
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
Improving random walk rankings with feature selection and imputation
The Science4cast Competition consists of predicting new links in a semantic network, with each node representing a concept and each edge representing a link proposed by a paper relating two concepts.
