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This paper constructs a multimodal dataset of 1000 academic papers with text, images, and audio to study keyword extraction, showing that fusing multiple modalities improves performance.
This paper introduces a text-based causal inference methodology using an enhanced CausalBERT to disentangle the effects of individual aspects (e.g., school administration, academic performance) on overall online review ratings, validated on 600K+ U.S. K-12 school reviews. Key improvements include temperature scaling, hyperparameter optimization, and interpretability methods to reduce confounding bias.
The paper presents Prometheus, a framework that uses large language models to extract local causal claims from text and organizes them into navigable causal atlases, enabling deep causal research across diverse domains.