GIScholarBench: Benchmarking LLM Overconfidence in GIS Research
Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence. Here, overconfidence is defined behaviorally as the tendency to produce confident, assertive, and well-formatted outputs even when the underlying knowledge is incomplete or unverifiable, rather than as a calibration gap between stated confidence and accuracy. To examine this issue, we introduce GIScholarBench, a benchmark built from 10,865 papers published in 25 core GIScience journals between 2020 and 2025. The benchmark covers three tasks with increasing cognitive complexity: metadata retrieval, literat...
| Authors | Zongrng Li, Mingzheng Yang, Lei Zou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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