Analysis of Material Facts on Financial Assets: a Generative AI Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ANRZEDJBD" target="_blank" >RIV/00216208:11320/25:NRZEDJBD - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195182338&partnerID=40&md5=4209b9905edb2fe4ce430918ab0a33f0" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195182338&partnerID=40&md5=4209b9905edb2fe4ce430918ab0a33f0</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of Material Facts on Financial Assets: a Generative AI Approach

Popis výsledku v původním jazyce

Material facts (MF) are crucial and obligatory disclosures that can significantly influence asset values. Following their release, financial analysts embark on the meticulous and highly specialized task of crafting analyses to shed light on their impact on company assets, a challenge elevated by the daily amount of MFs released. Generative AI, with its demonstrated power of crafting coherent text, emerges as a promising solution to this task. However, while these analyses must incorporate the MF, they must also transcend it, enhancing it with vital background information, valuable and grounded recommendations, prospects, potential risks, and their underlying reasoning. In this paper, we approach this task as an instance of controllable text generation, aiming to ensure adherence to the MF and other pivotal attributes as control elements. We first explore language models’ capacity to manage this task by embedding those elements into prompts and engaging popular chatbots. A bilingual proof of concept underscores both the potential and the challenges of applying generative AI techniques to this task. © 2024 ELRA Language Resource Association.

Název v anglickém jazyce

Analysis of Material Facts on Financial Assets: a Generative AI Approach

Popis výsledku anglicky

Material facts (MF) are crucial and obligatory disclosures that can significantly influence asset values. Following their release, financial analysts embark on the meticulous and highly specialized task of crafting analyses to shed light on their impact on company assets, a challenge elevated by the daily amount of MFs released. Generative AI, with its demonstrated power of crafting coherent text, emerges as a promising solution to this task. However, while these analyses must incorporate the MF, they must also transcend it, enhancing it with vital background information, valuable and grounded recommendations, prospects, potential risks, and their underlying reasoning. In this paper, we approach this task as an instance of controllable text generation, aiming to ensure adherence to the MF and other pivotal attributes as control elements. We first explore language models’ capacity to manage this task by embedding those elements into prompts and engaging popular chatbots. A bilingual proof of concept underscores both the potential and the challenges of applying generative AI techniques to this task. © 2024 ELRA Language Resource Association.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Jt. Workshop Financ. Technol. Nat. Lang. Process., Knowl. Discov. from Unstructured Data Financ. Serv. Econ. Nat. Lang. Process., FinNLP-KDF-ECONLP LREC-COLING - Workshop Proc.

ISBN

978-249381419-7

ISSN

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e-ISSN

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Počet stran výsledku

16

Strana od-do

103-118

Název nakladatele

European Language Resources Association (ELRA)

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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