Overview
The moral beliefs encoded in large language models (LLMs) is examined through a study involving 28 different models. The researchers created a survey with various moral scenarios and analyzed the models' responses to assess their alignment with common sense and identify any uncertainties or inconsistencies.
The findings revealed that in scenarios with clear right and wrong choices, most models selected the commonsense action. However, in ambiguous scenarios, the models expressed uncertainty, and some even displayed clear preferences. To measure the beliefs encoded in LLMs, the study developed statistical methods and evaluated the uncertainty and consistency of their choices.
Additionally, the article introduces the MoralChoice dataset, which comprises 1767 moral decision-making scenarios categorized into low and high ambiguity types. The dataset was created through a three-step process involving scenario generation, curation, and acquisition of auxiliary labels.
Furthermore, the article discusses the evaluation of different LLMs using the MoralChoice dataset. It examines the models' performance in terms of action likelihood, rule violations, and response consistency. The results demonstrate variations among the models, with some consistently favoring favorable actions, while others exhibiting uncertainty or violating certain rules. The models also show sensitivity to question formats and option orderings.
Takeaway
The article provides valuable insights into the moral beliefs encoded in LLMs, emphasizes the significance of understanding and evaluating these models, and highlights the construction and evaluation of the MoralChoice dataset, shedding light on the variations in performance among different language models.
Reference: https://arxiv.org/pdf/2307.14324