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LLMs Generate Narrative Explanations for AI Decisions

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces "XAIstories," a method that uses Large Language Models to create narrative explanations for AI decisions, aiming to make complex AI outputs more understandable to general audiences and data scientists alike. The approach, which generates stories based on SHAP values and counterfactual explanations, has shown promising results, with over 90% of surveyed general users finding the narratives convincing. Data scientists also see value in XAIstories for communicating AI insights, and in image classification tasks, CFstories were found to be significantly faster and equally or more convincing than user-crafted narratives. AI

IMPACT Enhances AI explainability for both general users and data scientists, potentially improving decision-making.

RANK_REASON The cluster contains a research paper detailing a novel method for AI explainability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs Generate Narrative Explanations for AI Decisions

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The cluster contains a research paper detailing a novel method for AI explainability. [lever_c_demoted from research: ic=1 ai=1.0]
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139 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Martens, James Hinns, Camille Dams, Mark Vergouwen, Theodoros Evgeniou ·

    Tell Me a Story! Narrative-Driven XAI with Large Language Models

    arXiv:2309.17057v3 Announce Type: replace Abstract: In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for Explainable AI (XAI). Existing XAI approaches, such as the widely used SHAP val…

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