# Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

arXiv: https://arxiv.org/abs/2010.12512 · HTML: https://mlsanity.com/paper/2010.12512/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 30 of 40)

- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective](https://mlsanity.com/paper/2211.08073/) (2022)
- [Towards LLM-guided Causal Explainability for Black-box Text Classifiers](https://mlsanity.com/paper/2309.13340/) (2023)
- [Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications](https://mlsanity.com/paper/2408.11878/) (2024)
- [Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis](https://mlsanity.com/paper/2106.15231/) (2021)
- [A Rationale-Centric Framework for Human-in-the-loop Machine Learning](https://mlsanity.com/paper/2203.12918/) (2022)
- [Demystifying Domain-adaptive Post-training for Financial LLMs](https://mlsanity.com/paper/2501.04961/) (2025)
- [A Survey on Natural Language Counterfactual Generation](https://mlsanity.com/paper/2407.03993/) (2024)
- [Supervised Knowledge Makes Large Language Models Better In-context Learners](https://mlsanity.com/paper/2312.15918/) (2023)
- [Text Counterfactuals via Latent Optimization and Shapley-Guided Search](https://mlsanity.com/paper/2110.11589/) (2021)
- [Empowering Language Understanding with Counterfactual Reasoning](https://mlsanity.com/paper/2106.03046/) (2021)
- [A Comprehensive Review on Financial Explainable AI](https://mlsanity.com/paper/2309.11960/) (2023)
- [Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research](https://mlsanity.com/paper/2307.02131/) (2023)
- [Interpreting Language Reward Models via Contrastive Explanations](https://mlsanity.com/paper/2411.16502/) (2024)
- [A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification](https://mlsanity.com/paper/2411.02476/) (2024)
- [Causal Inference for Banking Finance and Insurance A Survey](https://mlsanity.com/paper/2307.16427/) (2023)
- [A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers](https://mlsanity.com/paper/2411.02643/) (2024)
- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)
- [ExplainableFold: Understanding AlphaFold Prediction with Explainable AI](https://mlsanity.com/paper/2301.11765/) (2023)
- [REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization](https://mlsanity.com/paper/2310.14418/) (2023)
- [Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning](https://mlsanity.com/paper/2312.10045/) (2023)
- [RORA: Robust Free-Text Rationale Evaluation](https://mlsanity.com/paper/2402.18678/) (2024)
- [FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking](https://mlsanity.com/paper/2504.16188/) (2025)
- [Counterfactually Evaluating Explanations in Recommender Systems](https://mlsanity.com/paper/2203.01310/) (2022)
- [A General Search-based Framework for Generating Textual Counterfactual Explanations](https://mlsanity.com/paper/2211.00369/) (2022)
- [Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method](https://mlsanity.com/paper/2310.02019/) (2023)
- [NoMatterXAI: Generating "No Matter What" Alterfactual Examples for Explaining Black-Box Text Classification Models](https://mlsanity.com/paper/2408.10528/) (2024)
- [Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations](https://mlsanity.com/paper/2409.17774/) (2024)
- [Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling](https://mlsanity.com/paper/2507.06419/) (2025)

## Abstract (arXiv)

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user's trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user's trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.