Monetizing Currency Pair Sentiments through LLM Explainability
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917746109841408 |
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| author | Limonad, Lior Fournier, Fabiana Díaz, Juan Manuel Vera Skarbovsky, Inna Gur, Shlomit Lazcano, Raquel |
| author_facet | Limonad, Lior Fournier, Fabiana Díaz, Juan Manuel Vera Skarbovsky, Inna Gur, Shlomit Lazcano, Raquel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19922 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monetizing Currency Pair Sentiments through LLM Explainability Limonad, Lior Fournier, Fabiana Díaz, Juan Manuel Vera Skarbovsky, Inna Gur, Shlomit Lazcano, Raquel Artificial Intelligence 68T50 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. |
| title | Monetizing Currency Pair Sentiments through LLM Explainability |
| topic | Artificial Intelligence 68T50 |
| url | https://arxiv.org/abs/2407.19922 |