·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing2h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents2h◆Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects2h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks2h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models2h◆Creative Integration: A Decidable Criterion of Creativity2h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi2h◆Joint Optimization for Greedy Longest-match Tokenization2h◆Formally Verified Synthesizable Floating-Point Data Types in ARCH HDL2h◆Kimi K3: Open Frontier Intelligence2h◆The Few-shot Dilemma: Over-prompting Large Language Models2h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism2h◆Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions2h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram2h◆StageGuard: Physiologically Constrained Sleep Staging2h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders2h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment2h◆Photonic reservoir computing with complex networks2h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation2h◆Predicting Channel Closures in the Lightning Network with Machine Learning2h◆Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing2h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents2h◆Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects2h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks2h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models2h◆Creative Integration: A Decidable Criterion of Creativity2h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi2h◆Joint Optimization for Greedy Longest-match Tokenization2h◆Formally Verified Synthesizable Floating-Point Data Types in ARCH HDL2h◆Kimi K3: Open Frontier Intelligence2h◆The Few-shot Dilemma: Over-prompting Large Language Models2h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism2h◆Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions2h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram2h◆StageGuard: Physiologically Constrained Sleep Staging2h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders2h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment2h◆Photonic reservoir computing with complex networks2h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation2h◆Predicting Channel Closures in the Lightning Network with Machine Learning2h◆
News/Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
arxiv
PublishedApril 21, 2026 at 4:00 AM
—neutral

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.16042v2 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing surveys in explainable AI largely focus on post-hoc explanation methods t

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#explainability#nlp#research#transparency

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#explainability#nlp#research#transparency

Related coverage

More from ARXIV
arxivAgentic Permissions Policy Algebra for Taint Confinement in LLM Agents2harxivSparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects2harxivBeyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks2harxivThe One-Word Census: Answer-Choice Conformity Across 44 Language Models2h
The Bubble Brief
WEEKLY

Read explainability insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews