Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences (2020)

Abstract: Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding. Concretely, we present a new task and corpus for learning alignments between machine and human preferences. Our newly introduced problem is concerned with predicting the preferable options from two sentences describing scenarios that may involve social and cultural situations. Our problem is framed as a natural language inference task with crowd-sourced preference votes by human players, obtained from a gamified voting platform. We benchmark several state-of-the-art neural models, along with BERT and friends on this task. Our experimental results show that current state-of-the-art NLP models still leave much room for improvement.

## introduction the ability to understanding social nuances and human preferences is central to natural language understanding. this also enables better alignment of machine learning models with human values, eventually leading to better human-compatible ai applications @xcite @xcite @xcite . there exist a plethora of work on studying optimal decision-making under a variety of situations @xcite @xcite . on the other hand, cognitive models of human decision-making are usually based on small datasets @xcite . furthermore, these studies tend to only consider individuals in isolation. in contrast, we investigate the influence of cultural and social nuances for choice prediction at scale. in other words, we study the social preference as a whole, not those of an individual in isolation, which is arguably more challenging and largely unexplored. in this work, we propose a new benchmark dataset with a large number of 200k data points, machine alignment with cultural values and social preferences (macs), for learning ai alignment with humans. our dataset is based on a popular gamified voting platform, namely the game of 'would you rather?'. in this game, participants are given two choices and vote for the more preferable option. examples from our dataset can be found at in many ways, our benchmark dataset is reminiscent of the natural language inference problem @xcite , social commonsense reasoning @xcite or other natural language understanding problems @xcite . to this end, our problem is framed in a way that enables convenient benchmarking of existing state-of-the-art nlu models such as bert @xcite or roberta @xcite . that said, unlike many nlu datasets that rely on few annotators, the key differentiator lies in the fact that our dataset aggregates across hundreds or thousands and beyond for each data point. options are also crowd-sourced and gamified which may encourage less monotonic samples, ie., encouraging players to come up with questionss that are difficult for other players. additionally, our dataset comprises of country-level statistics, which enable us to perform cultural-level prediction of preferences. our contributions all in all, the prime contribution of this work is as follows: ## learning alignment with human preferences this section describes the proposed dataset and problem formulation. ## experiments this section outlines our experimental setup and results. ## conclusion we propose macs (machine alignment with cultural and social preferences), a new benchmark dataset for learning machine alignment with human cultural and social preferences. macs encompasses and requires social and cultural reasoning to solve and an overall holistic understanding of humanity. it is designed to be challenging where state-of-the-art nlp models still struggle at ≈ 60%.