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Found 4 papers, 1 papers with code
Causal Inference for Human-Language Model Collaboration
A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality.
Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects.
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
Advances in language modeling have paved the way for novel human-AI co-writing experiences.
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace.
