title: Data-Centric Financial Large Language Models
summary: Large language models (LLMs) show promise for natural language tasks but
  struggle when applied directly to complex domains like finance. LLMs have
  difficulty reasoning about and integrating all relevant information. We propose
  a data-centric approach to enable LLMs to better handle financial tasks. Our
  key insight is that rather than overloading the LLM with everything at once, it
  is more effective to preprocess and pre-understand the data. We create a
  financial LLM (FLLM) using multitask prompt-based finetuning to achieve data
  pre-processing and pre-understanding. However, labeled data is scarce for each
  task. To overcome manual annotation costs, we employ abductive augmentation
  reasoning (AAR) to automatically generate training data by modifying the pseudo
  labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR
  substantially outperforms baseline financial LLMs designed for raw text,
  achieving state-of-the-art on financial analysis and interpretation tasks. We
  also open source a new benchmark for financial analysis and interpretation. Our
  methodology provides a promising path to unlock LLMs' potential for complex
  real-world domains.
paper_authors._id: 653f14516217d355c6b43207
paper_authors.name: Zhixuan Chu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-31T08:23:28.831Z
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paper_authors.avatarUrl: /avatars/f4814a675e9b7e796934f3ed774bca8c.svg
paper_authors.fullname: Zhixuan Chu
paper_authors.type: user
paper_authors.user: Zhixuanchu
paper_authors._id: 653f14516217d355c6b43208
paper_authors.name: Huaiyu Guo
paper_authors._id: 653f14516217d355c6b43209
paper_authors.name: Xinyuan Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-30T13:56:28.681Z
paper_authors._id: 643ca51326f177a3e41aca9b
paper_authors.avatarUrl: /avatars/86ec4255874fe77bceead720cecb6cd3.svg
paper_authors.fullname: zhouxinyuan
paper_authors.type: user
paper_authors.user: braverr
paper_authors._id: 653f14516217d355c6b4320a
paper_authors.name: Yijia Wang
paper_authors._id: 653f14516217d355c6b4320b
paper_authors.name: Fei Yu
paper_authors._id: 653f14516217d355c6b4320c
paper_authors.name: Hong Chen
paper_authors._id: 653f14516217d355c6b4320d
paper_authors.name: Wanqing Xu
paper_authors._id: 653f14516217d355c6b4320e
paper_authors.name: Xin Lu
paper_authors._id: 653f14516217d355c6b4320f
paper_authors.name: Qing Cui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-30T13:54:52.382Z
paper_authors._id: 641a8e9605290a13504167ab
paper_authors.avatarUrl: /avatars/4404fa9f32955e3543a6afc8a923683e.svg
paper_authors.fullname: Qing Cui
paper_authors.type: user
paper_authors.user: haccanri
paper_authors._id: 653f14516217d355c6b43210
paper_authors.name: Longfei Li
paper_authors._id: 653f14516217d355c6b43211
paper_authors.name: Jun Zhou
paper_authors._id: 653f14516217d355c6b43212
paper_authors.name: Sheng Li
paper_title: Data-Centric Financial Large Language Models
paper_summary: Large language models (LLMs) show promise for natural language tasks but
  struggle when applied directly to complex domains like finance. LLMs have
  difficulty reasoning about and integrating all relevant information. We propose
  a data-centric approach to enable LLMs to better handle financial tasks. Our
  key insight is that rather than overloading the LLM with everything at once, it
  is more effective to preprocess and pre-understand the data. We create a
  financial LLM (FLLM) using multitask prompt-based finetuning to achieve data
  pre-processing and pre-understanding. However, labeled data is scarce for each
  task. To overcome manual annotation costs, we employ abductive augmentation
  reasoning (AAR) to automatically generate training data by modifying the pseudo
  labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR
  substantially outperforms baseline financial LLMs designed for raw text,
  achieving state-of-the-art on financial analysis and interpretation tasks. We
  also open source a new benchmark for financial analysis and interpretation. Our
  methodology provides a promising path to unlock LLMs' potential for complex
  real-world domains.
paper_ai_keywords: multitask prompt-based finetuning abductive augmentation reasoning (AAR) FLLM (financial LLM) financial analysis financial interpretation