GeoGrid-Bench evaluates 11 foundation models on 3,200 expert-curated questions about gridded climate data across 16 variables, finding vision-language models strongest and code generation weakest.
Information Retrieval for Climate Impact
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?
GeoGrid-Bench evaluates 11 foundation models on 3,200 expert-curated questions about gridded climate data across 16 variables, finding vision-language models strongest and code generation weakest.