Latent Scaling corrects crosscoder sparsity artifacts and isolates chat latents

measured in 1 paper

Minder et al. show the standard crosscoder's L1-on-decoder-norm loss can misattribute a latent's direction as chat-unique when it genuinely exists in the base model too [minder-etal-2025-crosscoders-chat-tuning-latent-scaling] Latent Scaling, a post-hoc diagnostic, re-measures each latent's true presence in each model rather than trusting the trained decoder norm [minder-etal-2025-crosscoders-chat-tuning-latent-scaling] Replacing the L1 crosscoder with a BatchTopK one substantially mitigates the artifact on Gemma-2-2B base/chat [minder-etal-2025-crosscoders-chat-tuning-latent-scaling] The corrected crosscoder isolates genuinely chat-specific, causally-steerable latents (e.g. "false information," multiple distinct refusal-trigger latents) rather than one monolithic refusal feature [minder-etal-2025-crosscoders-chat-tuning-latent-scaling]

Structure

Linear Direction

Context

Latent Scaling diagnostic correcting crosscoder L1-sparsity misattribution, BatchTopK crosscoder training mitigating the artifact, genuinely chat-specific latents validated by both Latent Scaling and causal steering, multiple distinct refusal-trigger latents rather than one monolithic refusal feature

Confirmed in models

Gemma-2-2B · Gemma, Gemma-2-2B-it · Gemma

Method

Sparse crosscoders, Causal interventions (steering)

Papers

Overcoming Sparsity Artifacts in Crosscoders to Interpret Chat-Tuning — Minder, Julian, Dumas, Clément, Juang, Caden, Chugtai, Bilal, Nanda, Neel2025 · arXiv:2504.02922