Authors: Nicole Cho, Nishan Srishankar, Lucas Cecchi, William Watson
Abstract: Financial intelligence generation from vast data sources has typically relied
on traditional methods of knowledge-graph construction or database engineering.
Recently, fine-tuned financial domain-specific Large Language Models (LLMs),
have emerged. While these advancements are promising, limitations such as high
inference costs, hallucinations, and the complexity of concurrently analyzing
high-dimensional financial data, emerge. This motivates our invention FISHNET
(Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning,
Expert swarming, and Task planning), an agentic architecture that accomplishes
highly complex analytical tasks for more than 98,000 regulatory filings that
vary immensely in terms of semantics, data hierarchy, or format. FISHNET shows
remarkable performance for financial insight generation (61.8% success rate
over 5.0% Routing, 45.6% RAG R-Precision). We conduct rigorous ablations to
empirically prove the success of FISHNET, each agent’s importance, and the
optimized performance of assembling all agents. Our modular architecture can be
leveraged for a myriad of use-cases, enabling scalability, flexibility, and
data integrity that are critical for financial tasks.
Source: http://arxiv.org/abs/2410.19727v1
