GIScholarBench shows LLMs exhibit consistent overconfidence across three scholarly tasks in GIS, with different manifestations in factual retrieval, citation expansion, and idea generation.
Understanding the bias of call detail records in human mobility research
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Multilevel regression and poststratification corrects socioeconomic sampling bias in CDR mobility estimates, lowering average radius of gyration by 17 percent.
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
citing papers explorer
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GIScholarBench: Benchmarking LLM Overconfidence in GIS Research
GIScholarBench shows LLMs exhibit consistent overconfidence across three scholarly tasks in GIS, with different manifestations in factual retrieval, citation expansion, and idea generation.
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Correcting socioeconomic bias in mobile phone mobility estimates using multilevel regression and poststratification
Multilevel regression and poststratification corrects socioeconomic sampling bias in CDR mobility estimates, lowering average radius of gyration by 17 percent.
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A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.