# ICX360: In-Context eXplainability 360 Toolkit

arXiv: https://arxiv.org/abs/2511.10879 · HTML: https://mlsanity.com/paper/2511.10879/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales](https://mlsanity.com/paper/2606.01148/) (2026)
- [LumiXAI: A Modular Full-Stack Framework for Feature Attribution](https://mlsanity.com/paper/2608.24524/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting transcripts to answering doctors' questions. As was the case with earlier predictive models, it is crucial that we develop tools for explaining the output of LLMs, be it a summary, list, response to a question, etc. With these needs in mind, we introduce In-Context Explainability 360 (ICX360), an open-source Python toolkit for explaining LLMs with a focus on the user-provided context (or prompts in general) that are fed to the LLMs. ICX360 contains implementations for three recent tools that explain LLMs using both black-box and white-box methods (via perturbations and gradients respectively). The toolkit, available at https://github.com/IBM/ICX360, contains quick-start guidance materials as well as detailed tutorials covering use cases such as retrieval augmented generation, natural language generation, and jailbreaking.