@inproceedings{chen-etal-2024-knowledge,
title = "Knowledge-augmented Financial Market Analysis and Report Generation",
author = "Chen, Yuemin and
Wu, Feifan and
Wang, Jingwei and
Qian, Hao and
Liu, Ziqi and
Zhang, Zhiqiang and
Zhou, Jun and
Wang, Meng",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.90",
pages = "1207--1217",
abstract = "Crafting a convincing financial market analysis report necessitates a wealth of market information and the expertise of financial analysts, posing a highly challenging task. While large language models (LLMs) have enabled the automated generation of financial market analysis text, they still face issues such as hallucinations, errors in financial knowledge, and insufficient capability to reason about complex financial problems, which limits the quality of the generation. To tackle these shortcomings, we propose a novel task and a retrieval-augmented framework grounded in a financial knowledge graph (FKG). The proposed framework is compatible with commonly used instruction-tuning methods. Experiments demonstrate that our framework, coupled with a small-scale language model fine-tuned with instructions, can significantly enhance the logical consistency and quality of the generated analysis texts, outperforming both large-scale language models and other retrieval-augmented baselines.",
}
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<abstract>Crafting a convincing financial market analysis report necessitates a wealth of market information and the expertise of financial analysts, posing a highly challenging task. While large language models (LLMs) have enabled the automated generation of financial market analysis text, they still face issues such as hallucinations, errors in financial knowledge, and insufficient capability to reason about complex financial problems, which limits the quality of the generation. To tackle these shortcomings, we propose a novel task and a retrieval-augmented framework grounded in a financial knowledge graph (FKG). The proposed framework is compatible with commonly used instruction-tuning methods. Experiments demonstrate that our framework, coupled with a small-scale language model fine-tuned with instructions, can significantly enhance the logical consistency and quality of the generated analysis texts, outperforming both large-scale language models and other retrieval-augmented baselines.</abstract>
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%0 Conference Proceedings
%T Knowledge-augmented Financial Market Analysis and Report Generation
%A Chen, Yuemin
%A Wu, Feifan
%A Wang, Jingwei
%A Qian, Hao
%A Liu, Ziqi
%A Zhang, Zhiqiang
%A Zhou, Jun
%A Wang, Meng
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F chen-etal-2024-knowledge
%X Crafting a convincing financial market analysis report necessitates a wealth of market information and the expertise of financial analysts, posing a highly challenging task. While large language models (LLMs) have enabled the automated generation of financial market analysis text, they still face issues such as hallucinations, errors in financial knowledge, and insufficient capability to reason about complex financial problems, which limits the quality of the generation. To tackle these shortcomings, we propose a novel task and a retrieval-augmented framework grounded in a financial knowledge graph (FKG). The proposed framework is compatible with commonly used instruction-tuning methods. Experiments demonstrate that our framework, coupled with a small-scale language model fine-tuned with instructions, can significantly enhance the logical consistency and quality of the generated analysis texts, outperforming both large-scale language models and other retrieval-augmented baselines.
%U https://aclanthology.org/2024.emnlp-industry.90
%P 1207-1217
Markdown (Informal)
[Knowledge-augmented Financial Market Analysis and Report Generation](https://aclanthology.org/2024.emnlp-industry.90) (Chen et al., EMNLP 2024)
ACL
- Yuemin Chen, Feifan Wu, Jingwei Wang, Hao Qian, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2024. Knowledge-augmented Financial Market Analysis and Report Generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1207–1217, Miami, Florida, US. Association for Computational Linguistics.