DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging
Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representation bias: systematic drift between the merged model's hidden states and those of each individual source model. Prior work (Yang et al., 2024a) study and mitigate this bias for encoder-based vision models using a lightweight correction module trained with L1 loss. However, such bias is not studied for decoder models due to their autoregressive nature. We analyze the problem of representation bias in decoder models, a
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- FuzzySimilar title/name (fuzzy) · 84%unit8co/darts →
“Fuzzy title match (0.92): “DARTS: Decoder-Aware Representation Tuning via Surgery for M” ≈ “unit8co/darts””
- FuzzyOverlapping authors or contributors · 62%deepfakes/faceswap →
“Shared author/contributor keys: sharma”
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: sharma”
- LinkedLinked via arxiv author · 85%Aaryan Ajay Sharma →
“DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging”
- LinkedLinked via arxiv author · 85%Sai Nishanth Padala →
“DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging”
- LinkedLinked via arxiv author · 85%Seganrasan Subramanian →
“DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging”