arXiv AI
ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers
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Explainable embeddings with Distance Explainer
arXiv:2505. 15516v3 Announce Type: replace-cross Abstract: While eXplainable AI (XAI) has advanced significantly, few methods address interpretability in embedded vector spaces where dimensions represent complex abstractions.
arXiv AI
Aug 3
A Human-Centered Validation of the Explainability-Performance Coefficient
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
arXiv AI
Jul 24
KeySI: An Interaction Framework for Tuning Text Embeddings Based on Human Feedback
arXiv:2607. 20556v1 Announce Type: new Abstract: In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis.
arXiv AI
Jun 3
Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
arXiv:2606. 02814v1 Announce Type: cross Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs.
arXiv Machine Learning
Jun 25
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.
arXiv Computation and Language
Sep 7
PROMPT2BOX:Improving LLM Weakness Discovery and Specificity Estimation by Uncovering Entailment Structure among Prompts
The paper introduces Prompt2Box, a method that embeds prompts into a box embedding space to capture both semantic similarity and specificity relations, addressing the limitation of traditional vector embeddings that conflate topical similarity with specificity. Using a trained encoder on existing and synthesized datasets, Prompt2Box achieves significant improvements, reducing specificity prediction error by 45% over a prompt-length baseline and identifying 13.5% more LLM weaknesses in hierarchical clustering compared to vector baselines. The authors also present a novel dimension‑reduction technique for visualizing and comparing box embeddings, and provide the code on GitHub.