SURGELLM：通过任务感知特征门控与类别均衡归一化重构多任务评测

This paper has been withdrawn by arXiv Admin

No PDF available, click to view other formats

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), \emph{task-conditioned prefix tokens} (quantized feature values and task identity prepended to every input), and \emph{Instance-Weighted Normalization} (IWN; removes class-prior bias from gate statistics). We prove an excess-risk bound linking gate benefit to \emph{surgical feature alignment}. Across four tasks, SST-2, multi-hop retrieval, LLM-prompt attribution, and authorship detection, covering 17,830 examples and eleven model variants over three seeds, the IWN variant achieves macro-F1 \textbf{0.940} ($+0.036$ over the strongest non-IWN baseline; $+0.130$ on authorship detection). A random-vocabulary control ($-0.028$ avg.\ F1) confirms gains are lexical, not parametric. Code, vocabularies, and a $99.5\%$-recovery auto-extraction recipe are released.

Comments:	arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2606.24259 [cs.CL]
	(or arXiv:2606.24259v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2606.24259 arXiv-issued DOI via DataCite

Submission history

From: arXiv Admin [view email]
[v1] Tue, 23 Jun 2026 07:47:21 UTC (161 KB)
[v2] Mon, 26 Oct 2026 15:31:47 UTC (1 KB) (withdrawn)