# LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework Source: [LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:09:56.744Z Authors: Zongyou Yu, Qiang Qu, Qian Zhang, Nan Zhang, Xiaoming Chen Published: 2025-02-20T05:18:36.000Z ## Abstract Recent advancements in event-based recognition have demonstrated significant promise, yet most existing approaches rely on extensive training, limiting their adaptability for efficient processing of event-driven visual content. Meanwhile, large language models (LLMs) have exhibited remarkable zero-shot capabilities across diverse domains, but their application to event-based visual recognition remains largely unexplored. To bridge this gap, we propose \\textbf{LLM-EvGen}, an event representation generator that produces LLM-compatible event representations \\textbf{LLM-EvRep}, thereby enhancing the performance of LLMs on event recognition tasks. The generator is trained using a self-supervised framework, aligning the generated representations with semantic consistency and structural fidelity. Comprehensive experiments were conducted on three datasets: N-ImageNet, N-Caltech101, and N-MNIST. The results demonstrate that our method, \\textbf{LLM-EvRep}, outperforms the event-to-video method, E2VID, by 15.93\\%, 0.82\\%, and 50.21\\%, respectively, in recognition tasks when evaluated using GPT-4o. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()