Tag: Robustness
All the articles with the tag "Robustness".
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Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
Published: at 11:30 AM
89.11 🤔This paper provides a theoretical analysis using Contextual Stochastic Block Models to demonstrate that graph attention mechanisms are beneficial for node classification only when structure noise exceeds feature noise, proposes a multi-layer GAT to achieve perfect classification at lower SNR thresholds, and validates these findings through synthetic and real-world experiments.
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Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
Published: at 11:09 AM 89.11 🤔This paper introduces the Objective Fairness Index (OFI), a legally grounded metric for evaluating bias in machine learning by comparing marginal benefits across groups, demonstrating its ability to detect algorithmic bias in applications like COMPAS and Folktable's Adult Employment dataset where traditional Disparate Impact fails.
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The Mosaic Memory of Large Language Models
Published: at 11:08 AM 89.04 🤔This paper introduces the concept of 'mosaic memory' in Large Language Models, demonstrating through experiments on canaries and real-world datasets like SlimPajama that LLMs memorize training data via fuzzy duplicates with partial overlaps, predominantly syntactically, challenging existing deduplication practices and raising concerns for privacy, model utility, and benchmark fairness.
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Memorization-Compression Cycles Improve Generalization
Published: at 11:18 AM 88.89 🤔本文通过提出信息瓶颈语言建模（IBLM）目标和Gated Phase Transition (GAPT)算法，理论和实验上证明了通过动态切换记忆和压缩阶段来降低表征熵，可以显著提升大型语言模型的泛化能力和冲突记忆分辨能力。
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Large Language Models are Miscalibrated In-Context Learners
Published: at 11:24 AM 88.84 🤔本文通过对大型语言模型在低资源场景下的校准问题进行深入分析，揭示上下文学习（ICL）未一致改善校准效果，并提出自集成方法显著提升校准性能（平均降低ECE 43%），同时维持或略提升任务性能。