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arXiv Computation and Language By Kavindu Perera, Mohammad Abaeiani, Ekaterina Gilman, Lauri Loven, Mourad Oussalah, Tassos Kanellos, Beatrice Gobbo, Dante Adami, Nicol\`o Ferriani, Maximiliano Romero, Pierre Rossel, Marc Bonazountas, Christina Deligianni, Nikos Xyderis, Artur Bogucki, Lampros Argyriou, Prasasthy Balasubramanian

Automated Detection and Structuring of Social Tipping Point Evidence in Climate related Documents: A Modular AI Framework

Read the original on arXiv Computation and Language →

The paper introduces an open, modular AI framework that automatically detects and structures evidence of social tipping points in climate literature at the passage level. It integrates a DistilBERT boundary splitter, an iteratively augmented RoBERTa classifier, a Mistral 7B rewrite model, a LLaMA 3.2 3B rating model, and a Milvus vector store, all accessible via a Streamlit interface. Evaluation on a GPT‑4.1‑labelled benchmark and expert‑reviewed set shows the splitter outperforms competitors and the RoBERTa detector achieves high accuracy and agreement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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