Concept Fields model text corpora as local Gaussian drift fields in embedding space to score sentence transitions for hallucination detection and novelty via standardized deviation.
InProceedings of the Workshop on NEW TEXT Wikis and blogs and other dynamic text sources
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Current VLMs depend on tightly aligned curated data and cannot exploit the weakly-aligned egocentric video signals that dominate naturalistic infant input.
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
FLiP recovers more than 75% lexical content from pretrained sentence embeddings across languages and modalities, outperforming non-factorized baselines and exposing intrinsic biases.
The authors built and publicly released sentence-aligned simplification corpora for five languages by processing crowd-sourced data from comparable documents.
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.
citing papers explorer
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Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement
Concept Fields model text corpora as local Gaussian drift fields in embedding space to score sentence transitions for hallucination detection and novelty via standardized deviation.
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EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Current VLMs depend on tightly aligned curated data and cannot exploit the weakly-aligned egocentric video signals that dominate naturalistic infant input.
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From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
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FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings
FLiP recovers more than 75% lexical content from pretrained sentence embeddings across languages and modalities, outperforming non-factorized baselines and exposing intrinsic biases.
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Align and Shine: Building High-Quality Sentence-Aligned Corpora for Multilingual Text Simplification
The authors built and publicly released sentence-aligned simplification corpora for five languages by processing crowd-sourced data from comparable documents.
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The Faiss library
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.