125 coordinated Wikipedia animal-welfare edits dominate attribution and counterfactual influence for animal-welfare queries on Llama models, with no spillover to general queries about the same entities.
arXiv preprint arXiv:2202.00622 , year=
5 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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representative citing papers
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
idSCD uses semantic correlation descriptors to perform dataset membership inference by comparing learned semantic structures, outperforming baselines in NLI, emotion, and medical text experiments.
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
Watermark-based dataset inference achieves membership detection performance comparable to loss-based methods when subset exposure is high, under alternate assumptions.
citing papers explorer
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Small edits, large models: How Wikipedia advocacy shapes LLM values
125 coordinated Wikipedia animal-welfare edits dominate attribution and counterfactual influence for animal-welfare queries on Llama models, with no spillover to general queries about the same entities.
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Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
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idSCD: Identifying Training Datasets through Semantic Correlation Descriptors
idSCD uses semantic correlation descriptors to perform dataset membership inference by comparing learned semantic structures, outperforming baselines in NLI, emotion, and medical text experiments.
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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
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Watermarking for Proprietary Dataset Protection
Watermark-based dataset inference achieves membership detection performance comparable to loss-based methods when subset exposure is high, under alternate assumptions.