# Identifying Financial Risk Information Using RAG with a Contrastive Insight

arXiv: https://arxiv.org/abs/2510.03521 · HTML: https://mlsanity.com/paper/2510.03521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions](https://mlsanity.com/paper/2508.11152/) (2025)
- [Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models](https://mlsanity.com/paper/2411.01368/) (2024)
- [Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations](https://mlsanity.com/paper/2410.22874/) (2024)
- [RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning](https://mlsanity.com/paper/2506.11555/) (2025)
- [A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling](https://mlsanity.com/paper/2109.09103/) (2021)
- [Modeling and Detecting Company Risks from News: A Case Study in Bloomberg News](https://mlsanity.com/paper/2508.10927/) (2025)

## Abstract (arXiv)

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.