[論文レビュー] A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges

Yuqi Nie, Yaxuan Kong+5 more|arXiv (Cornell University)|Jun 15, 2024

Stock Market Forecasting Methods被引用数 29

ひとこと要約

大規模言語モデルが金融に適用される方法の包括的な調査で、モデルタイプ、適用分野、データセット、ベンチマーク、主要な課題と機会を網羅する。

ABSTRACT

Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector.

研究の動機と目的

• 研究者と実務家にとって、金融LLMの応用についての全体的な視点とその実践的影響を提供する。
• アーキテクチャ、事前学習、微調整、カスタマイズ戦略を含む金融ドメインLLMのカタログ化。
• 金融LLM研究のためのデータセット、ベンチマーク、コードリソースの要約。
• 金融におけるデータ問題、ベンチマーキング、倫理、解釈性などの特有の課題を特定し、今後の方向性を提案する。

提案手法

• 金融ドメインLLMとその微調整戦略を分類・分析する。
• 適用領域をレビューする: 言語タスク、感情分析、時系列分析、金融推論、エージェントベースのモデリング。
• 金融LLMsのデータセット、ベンチマーク、および利用可能なコードを要約する。
• データ品質、倫理、セーフティを含む金融固有の課題と機会について論じる。

実験結果

リサーチクエスチョン

• RQ1 What are the dominant LLM architectures and fine-tuning approaches used in finance?
• RQ2 How are LLMs applied across linguistic tasks, sentiment, time series, reasoning, and agent-based modeling in finance?
• RQ3 What datasets, benchmarks, and code resources support financial LLM research?
• RQ4 What are the key challenges and open opportunities for deploying LLMs in the financial sector?

主な発見

• Financial-domain LLMs include specialized variants derived from GPT, BERT/RoBERTa, T5, ELECTRA, BLOOM and Llama families, with domain-specific adaptations.
• Applications span linguistic tasks, sentiment analysis, financial time series, financial reasoning, and agent-based modeling, with methodologies like textual analysis, forecasting, planning, and simulations.
• There is a growing collection of datasets, benchmarks, and open-code resources tailored to finance to support research and development.
• Key challenges include data quality, backtesting biases, interpretability, legal and ethical considerations, scalability, and privacy concerns.