Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
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Generative AI enables scalable, context-aware spear phishing by extracting profiles from public social media, producing emails that outperform real-world phishing samples in personalization and lower recipient suspicion.
A gamified system with multiple LLM agents of varied personalities gathers interaction data to produce more effective and interpretable Big Five personality assessments than single-context methods.
AI data firms view human expertise as an extractable, low-cost resource to feed AI systems while treating institutional expertise as something needing liberation or reform to fit this model.
Uni-HOI learns the joint distribution of text, human motion, and object motion using LLMs and VQ-VAEs in a two-stage training process for multiple HOI tasks.
Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.
citing papers explorer
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Semantic Recall for Vector Search
Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
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Context-Aware Spear Phishing: Generative AI-Enabled Attacks Against Individuals via Public Social Media Data
Generative AI enables scalable, context-aware spear phishing by extracting profiles from public social media, producing emails that outperform real-world phishing samples in personalization and lower recipient suspicion.
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Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities
A gamified system with multiple LLM agents of varied personalities gathers interaction data to produce more effective and interpretable Big Five personality assessments than single-context methods.
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Cheap Expertise: Mapping and Challenging Industry Perspectives in the Expert Data Gig Economy
AI data firms view human expertise as an extractable, low-cost resource to feed AI systems while treating institutional expertise as something needing liberation or reform to fit this model.
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Uni-HOI:A Unified framework for Learning the Joint distribution of Text and Human-Object Interaction
Uni-HOI learns the joint distribution of text, human motion, and object motion using LLMs and VQ-VAEs in a two-stage training process for multiple HOI tasks.
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An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.