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

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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
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arxiv
Read original ↗All from arxiv →
Tags
04
#language-models#benchmark#interpretability#steering

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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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