Monetizing Currency Pair Sentiments through LLM Explainability
<|reference_start|>Monetizing Currency Pair Sentiments through LLM Explainability: Large language models (LLMs) play a vital role in almost every domain in today's organizations. In the context of this work, we highlight the use of LLMs for sentiment analysis (SA) and explainability. Specifically, we contribute a novel technique to leverage LLMs as a post-hoc model-independent tool for the explainability of SA. We applied our technique in the financial domain for currency-pair price predictions using open news feed data merged with market prices. Our application shows that the developed technique is not only a viable alternative to using conventional eXplainable AI but can also be fed back to enrich the input to the machine learning (ML) model to better predict future currency-pair values. We envision our results could be generalized to employing explainability as a conventional enrichment for ML input for better ML predictions in general.<|reference_end|>
@article{limonad2024monetizing,
    title={Monetizing Currency Pair Sentiments through LLM Explainability},
    author={Lior Limonad and Fabiana Fournier and Juan Manuel Vera D'iaz and Inna
  Skarbovsky and Shlomit Gur and Raquel Lazcano},
    journal={AIFin workshop at ECAI 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19922},
    primaryClass={cs.AI}
}