Identifying Financial Risk Information Using RAG with a Contrastive Insight
This addresses the issue of generating context-specific insights for financial risk analysis, though it appears incremental as it builds on existing RAG methods.
The paper tackled the problem of generic outputs in specialized reasoning tasks, particularly in finance, by proposing a peer-aware comparative inference layer on top of RAG, resulting in outperformance over baseline RAG in metrics like ROUGE and BERTScore.
In specialized domains, humans often compare new problems against similar examples, highlight nuances, and draw conclusions instead of analyzing information in isolation. When applying reasoning in specialized contexts with LLMs on top of a RAG, the pipeline can capture contextually relevant information, but it is not designed to retrieve comparable cases or related problems. While RAG is effective at extracting factual information, its outputs in specialized reasoning tasks often remain generic, reflecting broad facts rather than context-specific insights. In finance, it results in generic risks that are true for the majority of companies. To address this limitation, we propose a peer-aware comparative inference layer on top of RAG. Our contrastive approach outperforms baseline RAG in text generation metrics such as ROUGE and BERTScore in comparison with human-generated equity research and risk.