# Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain

arXiv: https://arxiv.org/abs/2501.06277 · HTML: https://mlsanity.com/paper/2501.06277/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [BAGEL: Benchmarking Animal Knowledge Expertise in Language Models](https://mlsanity.com/paper/2604.16241/) (2026)
- [MMClima: A Framework for Multimodal Climate Science Data and Evaluation](https://mlsanity.com/paper/2606.10194/) (2026)
- [WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement](https://mlsanity.com/paper/2607.17745/) (2026)
- [WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management](https://mlsanity.com/paper/2608.07529/) (2026)

## Abstract (arXiv)

Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified evaluation framework. To address this, we present the Environmental Large Language model Evaluation (ELLE) question answer (QA) dataset, the first benchmark designed to assess large language models and their applications in ecological and environmental sciences. The ELLE dataset includes 1,130 question answer pairs across 16 environmental topics, categorized by domain, difficulty, and type. This comprehensive dataset standardizes performance assessments in these fields, enabling consistent and objective comparisons of generative AI performance. By providing a dedicated evaluation tool, ELLE dataset promotes the development and application of generative AI technologies for sustainable environmental outcomes. The dataset and code are available at https://elle.ceeai.net/ and https://github.com/CEEAI/elle.