arXiv (All)
open access
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization#630135
- SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization#628273
- Cross-Layer Discrete Concept Discovery for Interpreting Language Models#1142543
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#818956
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#808964
Record · ID 999806
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive