arXiv AI July 13, 2026 By Pan Li

All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models

arXiv:2607. 09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

arXiv:2606. 08497v1 Announce Type: new Abstract: As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability.

By Minyoung Hwang, Seokhyun Lee, Changhee Lee

llms benchmarks safety

arXiv AI

6d ago

Explainability in Practice: A Survey of Explainable NLP Across Various Domains

arXiv:2502. 00837v3 Announce Type: replace-cross Abstract: Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and BERT increasingly inform decisions.

By Hadi Mohammadi, Robert A. Bagheri, Anastasia Giachanou, Daniel L. Oberski

llms nlp safety

arXiv AI

Jul 17

Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.

By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos

rag benchmarks

arXiv AI

Jun 11

Forecasting Future Behavior as a Learning Task

arXiv:2606. 11445v1 Announce Type: new Abstract: Trust in an AI system is often anchored by explanations of how it works, which one then uses to forecast its behavior on new inputs.

By Mosh Levy, Yoav Goldberg, Asa Cooper Stickland

llms fine-tuning

arXiv AI

Jun 2

From Features to Actions: Explainability in Traditional and Agentic AI Systems

arXiv:2602. 06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure.

By Sindhuja Chaduvula, Jessee Ho, Kina Kim, Aravind Narayanan, Ahmed Y. Radwan, Mahshid Alinoori, Muskan Garg, Dhanesh Ramachandram, Shaina Raza

llms agents

arXiv Machine Learning

Jul 27

CEL: Comprehensive Counterfactual Explanations Library and Benchmark

arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.

By Oleksii Furman, {\L}ukasz Lenkiewicz, Marcel Musia{\l}ek, Maciej Zi\k{e}ba

efficiency benchmarks

Related stories

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

Explainability in Practice: A Survey of Explainable NLP Across Various Domains

Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

Forecasting Future Behavior as a Learning Task

From Features to Actions: Explainability in Traditional and Agentic AI Systems

CEL: Comprehensive Counterfactual Explanations Library and Benchmark