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Found 7 papers, 5 papers with code
Hypothesis Testing the Circuit Hypothesis in LLMs
In this paper, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them.
Data Augmentations for Improved (Large) Language Model Generalization
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Evaluating the Moral Beliefs Encoded in LLMs
(2) We apply this method to study what moral beliefs are encoded in different LLMs, especially in ambiguous cases where the right choice is not obvious.
An Invariant Learning Characterization of Controlled Text Generation
In this paper, we show that the performance of controlled generation may be poor if the distributions of text in response to user prompts differ from the distribution the predictor was trained on.
Invariant Representation Learning for Treatment Effect Estimation
To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding.
Adapting Neural Networks for the Estimation of Treatment Effects
We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
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