SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.
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Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
Young adults engage with low-quality news content on social media despite stating preferences for high-quality, accurate, and diverse information, and they produce higher-quality feeds when curating for a hypothetical persona.
Visibility in generative AI search must be assessed as a distribution over repeated measurements rather than single queries because outputs vary across runs, prompts, and time.
citing papers explorer
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Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.
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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
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Understanding the Gap Between Stated and Revealed Preferences in News Curation: A Study of Young Adult Social Media Users
Young adults engage with low-quality news content on social media despite stating preferences for high-quality, accurate, and diverse information, and they produce higher-quality feeds when curating for a hypothetical persona.
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Don't Measure Once: Measuring Visibility in AI Search (GEO)
Visibility in generative AI search must be assessed as a distribution over repeated measurements rather than single queries because outputs vary across runs, prompts, and time.