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News/Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines
arxiv
PublishedJune 1, 2026 at 4:00 AM
▲bullish

Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines

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

arXiv:2605.31183v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) have been seen as a promising avenue for exploring the internals of Large Language Models (LLMs) and for steering model output generation. When AxBench - a model steering benchmark - was introduced in Wu et al. (2025), SAEs

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Discussion
Mentioned models
03
  • 01
    Sparse Autoencoders
  • 02
    Large Language Models
  • 03
    LoRA
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#language-models#benchmark#interpretability#steering

No replies yet. Be first.

Mentioned models
03
  • 01
    Sparse Autoencoders
  • 02
    Large Language Models
  • 03
    LoRA
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#language-models#benchmark#interpretability#steering

Related coverage

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arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning16harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning16harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models16harxivTemporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents16h
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