sem-sparse-debiasing-vlm

name	sem-sparse-debiasing-vlm
title	SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.19028
keywords	["Vision-Language Models","Debiasing","Sparse Autoencoders","Post-Hoc","Fairness"]
description	Achieve post-hoc debiasing of frozen vision-language models by operating in sparse autoencoder latent space. Identify and modulate bias-relevant neurons with neuron-level precision while preserving task-relevant features, requiring no task-specific fine-tuning.

Component ID

Sparse Embedding Modulation (SEM) for Post-Hoc VLM Debiasing

Motivation

Vision-language models like CLIP exhibit bias in retrieval and zero-shot classification. Traditional debiasing requires full model fine-tuning. Bias and task-relevant information are heavily entangled in dense embedding space, making precise intervention difficult. A method for post-hoc modification of frozen models would enable deployment-time fairness corrections.

The Modification

SEM decomposes CLIP text embeddings into disentangled features using a Sparse Autoencoder (SAE), enabling neuron-level bias intervention on frozen pretrained models:

1. Sparse Autoencoder Decomposition - Use a pre-trained SAE trained on general text data to decompose dense text embeddings into interpretable sparse dimensions, where each dimension corresponds to identifiable semantic or bias-relevant features.

2. Dual-Score Neuron Evaluation - Compute two scores per neuron:

   • Content relevance: How much a neuron activates on task-relevant content (via LLM-generated paraphrases)
   • Bias sensitivity: How much a neuron responds to different bias classes (structured comparisons)

3. Modulation Coefficients - Combine scores into per-neuron multipliers that amplify content-relevant neurons and attenuate bias-relevant ones.

4. Debiased Reconstruction - Apply modulation coefficients, then reconstruct embeddings through the SAE decoder.

How It Works

Scoring stage: For each neuron, gather activations across diverse prompts:

• Paraphrase-based content estimation (does the neuron track semantic content?)
• Bias-class comparison (does the neuron vary with demographic attributes?)

Modulation stage: Combine scores via weighted combination that prioritizes content-relevant neurons, suppresses bias-sensitive ones, and reconstructs via SAE decoder.

Three implementation variants available:

• SEMi: LLM-generated paraphrases for content estimation
• SEMb: Explicit bias prompts for structured identification
• SEMbi: Combined approach (both sources)

Ablation Results

The paper demonstrates:

• Substantial fairness gains in both zero-shot classification and retrieval tasks
• Improvements over linear projection baselines, especially in worst-group accuracy
• Consistent performance across multiple bias dimensions and model variants
• Post-hoc applicability to frozen models without retraining
• Effectiveness with minimal task-specific data (just bias class definitions)

Conditions

• Requires pre-trained SAE (authors use SAE trained on general text)
• Works on frozen models—no fine-tuning needed
• Applicable to any vision-language embedding space (text or image)
• Requires definition of bias classes/attributes for the target domain
• More effective when content and bias features are partially separable in SAE space

Drop-In Checklist

• Load pre-trained SAE for the embedding model
• Implement content relevance scoring (paraphrases or task-specific examples)
• Implement bias sensitivity scoring (bias-class prompt comparisons)
• Design modulation coefficient combination (prioritize content, suppress bias)
• Add modulation application and SAE reconstruction step
• Test on zero-shot classification and retrieval tasks
• Validate fairness improvements (worst-group accuracy, demographic parity)
• Benchmark against fine-tuning-based debiasing methods