99 citations · 183 across the 27 of their papers we have counts for
3 papers · 1 filter
Aligning Language Models Towards Counterfactual Invariance
Basar Demir, Emily Shen, Ksheeraj Sai Vepuri +5
Small variations in prompt characteristics can meaningfully alter the semantics, tone, and contextual assumptions of large language model outputs. Such characteristics can involve…
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
Kristian Lum, Jacy Reese Anthis, Kevin Robinson +2
Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between the user attributes stated or implied by a prompt and the LLM's short text…
Transforming and Combining Rewards for Aligning Large Language Models
Zihao Wang, Chirag Nagpal, Jonathan Berant +4
A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language mod…