BRIDGE reduces bias against high-scoring ELL students in automated scoring by generating synthetic samples via inter-group content pasting and quality discrimination, achieving fairness gains comparable to additional real data.
arXiv preprint arXiv:2405.13001 (2024)
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BLUE aligns LLM-generated textual user profiles with embedding-based recommendation objectives via reinforcement learning and next-item text supervision, yielding better zero-shot performance and cross-domain transfer than baselines.
citing papers explorer
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BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
BRIDGE reduces bias against high-scoring ELL students in automated scoring by generating synthetic samples via inter-group content pasting and quality discrimination, achieving fairness gains comparable to additional real data.
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Bridging Textual Profiles and Latent User Embeddings for Personalization
BLUE aligns LLM-generated textual user profiles with embedding-based recommendation objectives via reinforcement learning and next-item text supervision, yielding better zero-shot performance and cross-domain transfer than baselines.