Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
Usable xai: 10 strategies towards exploiting explainability in the llm era
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
XGRAG uses graph perturbations to quantify component contributions in GraphRAG and achieves 14.81% better explanation quality than text-based baselines on QA datasets, with correlations to graph centrality.
AnTenA uses task-agnostic and task-specific LLM prompts to explain co-clustered patterns from tensor decomposition and evaluates them on forward and backward inference tasks.
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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Analysis and Explainability of LLMs Via Evolutionary Methods
Evolutionary trees from LLM weights recover ground-truth training topologies and identify key datasets and layers through phenotypic analysis.
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XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation
XGRAG uses graph perturbations to quantify component contributions in GraphRAG and achieves 14.81% better explanation quality than text-based baselines on QA datasets, with correlations to graph centrality.
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AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models
AnTenA uses task-agnostic and task-specific LLM prompts to explain co-clustered patterns from tensor decomposition and evaluates them on forward and backward inference tasks.
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Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.