# Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives

arXiv: https://arxiv.org/abs/2403.14341 · HTML: https://mlsanity.com/paper/2403.14341/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning](https://mlsanity.com/paper/2309.13064/) (2023)
- [Exploiting Document Knowledge for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1806.04346/) (2018)
- [FinEntity: Entity-level Sentiment Classification for Financial Texts](https://mlsanity.com/paper/2310.12406/) (2023)
- [GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks](https://mlsanity.com/paper/2305.16663/) (2023)
- [Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications](https://mlsanity.com/paper/2310.12620/) (2023)
- [CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports](https://mlsanity.com/paper/2310.16095/) (2023)

## Built on by (most cited first, 13 of 13)

- [FinMTEB: Finance Massive Text Embedding Benchmark](https://mlsanity.com/paper/2502.10990/) (2025)
- [Do We Need Domain-Specific Embedding Models? An Empirical Investigation](https://mlsanity.com/paper/2409.18511/) (2024)
- [EconNLI: Evaluating Large Language Models on Economics Reasoning](https://mlsanity.com/paper/2407.01212/) (2024)
- [TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts](https://mlsanity.com/paper/2502.07131/) (2025)
- [Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities](https://mlsanity.com/paper/2504.07274/) (2025)
- [Same Company, Same Signal: The Role of Identity in Earnings Call Transcripts](https://mlsanity.com/paper/2412.18029/) (2024)
- [Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval](https://mlsanity.com/paper/2506.00363/) (2025)
- [Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models](https://mlsanity.com/paper/2509.05691/) (2025)
- [FinCap: Topic-Aligned Captions for Short-Form Financial YouTube Videos](https://mlsanity.com/paper/2509.25745/) (2025)
- [NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance](https://mlsanity.com/paper/2507.09601/) (2025)
- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives](https://mlsanity.com/paper/2601.03794/) (2026)
- [Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting](https://mlsanity.com/paper/2610.01128/) (2026)

## Abstract (arXiv)

In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives. These narratives originate from the financial statements of the same company but correspond to different periods, such as year-over-year comparisons. Measuring the subtle semantic differences between these paired narratives enables market stakeholders to gauge changes over time in the company's financial and operational situations, which is critical for financial decision-making. We find that existing pretrained embedding models and LLM embeddings fall short in discerning these subtle financial narrative shifts. To address this gap, we propose an LLM-augmented pipeline specifically designed for the Financial-STS task. Evaluation on a human-annotated dataset demonstrates that our proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings.