Data-Centric Financial Large Language Models
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.
653f14516217d355c6b43207
Zhixuan Chu
claimed_verified
2023-10-31T08:23:28.831Z
65405529001b872f8fcb0a73
/avatars/f4814a675e9b7e796934f3ed774bca8c.svg
Zhixuan Chu
user
Zhixuanchu
653f14516217d355c6b43208
Huaiyu Guo
653f14516217d355c6b43209
Xinyuan Zhou
admin_assigned
2023-10-30T13:56:28.681Z
643ca51326f177a3e41aca9b
/avatars/86ec4255874fe77bceead720cecb6cd3.svg
zhouxinyuan
user
braverr
653f14516217d355c6b4320a
Yijia Wang
653f14516217d355c6b4320b
Fei Yu
653f14516217d355c6b4320c
Hong Chen
653f14516217d355c6b4320d
Wanqing Xu
653f14516217d355c6b4320e
Xin Lu
653f14516217d355c6b4320f
Qing Cui
admin_assigned
2023-10-30T13:54:52.382Z
641a8e9605290a13504167ab
/avatars/4404fa9f32955e3543a6afc8a923683e.svg
Qing Cui
user
haccanri
653f14516217d355c6b43210
Longfei Li
653f14516217d355c6b43211
Jun Zhou
653f14516217d355c6b43212
Sheng Li
Data-Centric Financial Large Language Models
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.
A data-centric approach using multitask prompt-based fine-tuning and abductive augmentation reasoning enables large language models to excel in financial analysis tasks with limited labeled data.
multitask prompt-based fine-tuning abductive augmentation reasoning financial LLM FLLM pseudo(labels) state-of-the-art financial analysis interpretation tasks