{
 "id": "lrsbrgrn/LetzSDG-1.0-Dataset-70k",
 "owner": "lrsbrgrn",
 "name": "LetzSDG-1.0-Dataset-70k",
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 "last_modified": "2026-04-12T17:42:37.000Z",
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 "gated": "auto",
 "tasks": [
  "text-classification"
 ],
 "citation": "@INPROCEEDINGS {11402269,\nauthor = { Bergeron, Loris and Francois, Jerome and State, Radu and Hilger, Jean },\nbooktitle = { 2025 IEEE International Conference on Big Data (BigData) },\ntitle = {{ Leveraging Large Language Models to Build Computationally Efficient Models for Sustainable Finance Investment Decision Support }},\nyear = {2025},\nvolume = {},\nISSN = {},\npages = {7123-7132},\nabstract = { Assessing companies' contributions to the United Nations Sustainable Development Goals (SDGs) is essential for sustainable investment and regulatory reporting. However, extracting reliable insights from heterogeneous textual sources remains a challenge due to limited labeled data, domain imbalance, and privacy constraints. We present LëtzSDG, a lightweight BERT-based multiclass classifier fine-tuned on a hybrid dataset constructed using Large Language Models (LLMs). Multiple LLMs are used to (i) expand domain-specific SDG keywords, (ii) perform consensus-based zero-shot labeling, and (iii) generate synthetic data to balance underrepresented classes. Unlike cloudhosted LLMs, LëtzSDG is designed for on-premises deployment within financial institutions, ensuring data-privacy compliance. Integrated into a human-in-the-loop investment workflow, its predictions are span-linked for traceability and committee review. Evaluated on public datasets (the OSDG Community Dataset and the SDG Integration Corpus), LëtzSDG outperforms SDGspecific baselines, a strong NLI-based zero-shot model, and sever",
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