SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization
arXiv:2606.24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge. We introduce \textbf{\surgellm}, a unified transformer framework that addresses each with a dedicated lightweight module: a \emph{surgical feature gate} (learned per-dimension sigmoid over curated lexical indicators and \texttt{[CLS]}; provably degenerates to identity when features are uninformative), \emp
The ongoing pressure to improve the efficiency and robustness of large language models for diverse applications drives continuous research into multi-task evaluation and architectural optimizations.
This research contributes to more efficient and adaptable AI models, directly impacting the development costs and performance ceiling for a wide range of NLP applications.
The proposed SURGELLM framework offers a novel approach to tackle key challenges in multi-task learning for NLP, potentially leading to more generalized and stable AI systems.
- · AI model developers
- · NLP application providers
- · Enterprises deploying AI
- · AI researchers
- · Inefficient multi-task learning architectures
- · Companies relying on less robust models
Improved performance and reliability of AI models across various natural language processing tasks.
Reduced computational resources needed for fine-tuning and deployment of specialized NLP models.
Acceleration of the development and adoption of AI agentic systems due to more versatile and stable underlying models.
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Read at arXiv cs.AI