Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
Interpretable user satisfaction estimation for conversational systems with large language models
2 Pith papers cite this work, alongside 13 external citations. Polarity classification is still indexing.
2
Pith papers citing it
13
external citations · OpenAlex
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Transformers equipped with continuous latent context tokens can implement foundational online decision-making algorithms such as weighted majority and Q-learning, and a trained small model outperforms larger LLMs on synthetic online prediction tasks.
citing papers explorer
-
Mitigating Label Bias with Interpretable Rubric Embeddings
Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
-
Continuous Latent Contexts Enable Efficient Online Learning in Transformers
Transformers equipped with continuous latent context tokens can implement foundational online decision-making algorithms such as weighted majority and Q-learning, and a trained small model outperforms larger LLMs on synthetic online prediction tasks.