Tootfinder

Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Arduin Findeis, Floris Weers, Guoli Yin, Ke Ye, Ruoming Pang, Tom Gunter
https://arxiv.org/abs/2507.17015

Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the "better" response. This approach can provide feedback for domains where other hard-coded metrics are difficult to obtain (e.g., chat response quality), thereby helping model evaluation or training. However, for some domains high-quality pairwise comparisons can be tricky to ob…

A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts
Angela John, Selvyn Allotey, Till Koebe, Alexandra Tyukavina, Ingmar Weber
https://arxiv.org/abs/2508.11349

A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts
Afforestation and reforestation are popular strategies for mitigating climate change by enhancing carbon sequestration. However, the effectiveness of these efforts is often self-reported by project developers, or certified through processes with limited external validation. This leads to concerns about data reliability and project integrity. In response to increasing scrutiny of voluntary carbon markets, this study presents a dataset on global afforestation and reforestation efforts compiled fr…