# Does Using Counterfactual Help LLMs Explain Textual Importance in Classification?

arXiv: https://arxiv.org/abs/2510.04031 · HTML: https://mlsanity.com/paper/2510.04031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models](https://mlsanity.com/paper/2402.04614/) (2024)
- [Can LLMs Explain Themselves Counterfactually?](https://mlsanity.com/paper/2502.18156/) (2025)
- [Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning](https://mlsanity.com/paper/2412.08587/) (2024)
- [Improved LLM Agents for Financial Document Question Answering](https://mlsanity.com/paper/2506.08726/) (2025)
- [Large Language Models for Healthcare Text Classification: A Systematic Review](https://mlsanity.com/paper/2503.01159/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. More recently, they have been shown to be very effective in textual classification tasks, motivating the need to explain the LLMs' decisions. Motivated by practical constrains where LLMs are black-boxed and LLM calls are expensive, we study how incorporating counterfactuals into LLM reasoning can affect the LLM's ability to identify the top words that have contributed to its classification decision. To this end, we introduce a framework called the decision changing rate that helps us quantify the importance of the top words in classification. Our experimental results show that using counterfactuals can be helpful.