S-GBT introduces a Hessian-bounding tensor and associated regularization for LSTM and CNN models that yields tighter certified robustness bounds against word substitutions, improving robust accuracy by up to 23.4%.
5555/3692070.3694025
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A learned residual accounting method with retrieved-token subtraction improves over pure Top-K selection at 1% exact-support budgets on long-context benchmarks for frozen Llama models.
CS researchers show pragmatic skepticism toward LLM leaderboards, using them despite distrust while preferring peer networks, arena leaderboards, and cost transparency as key missing feature.
citing papers explorer
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S-GBT: Smooth Growth Bound Tensor for Certified Robustness Against Word Substitution Attacks in NLP
S-GBT introduces a Hessian-bounding tensor and associated regularization for LSTM and CNN models that yields tighter certified robustness bounds against word substitutions, improving robust accuracy by up to 23.4%.
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The Trust Paradox: How CS Researchers Engage LLM Leaderboards
CS researchers show pragmatic skepticism toward LLM leaderboards, using them despite distrust while preferring peer networks, arena leaderboards, and cost transparency as key missing feature.