The recent surge in Large Language Model (LLM) related applications has led
to a concurrent escalation in expectations for LLMs to accommodate a myriad of
personas and encompass a broad spectrum of perspectives. An important first
step towards addressing this demand is to align language models with specific
personas, be it groups of users or individuals. Towards this goal, we first
present a new conceptualization of a persona. Moving beyond the traditional
reliance on demographics like age, gender, or political party affiliation, we
introduce a data-driven persona definition methodology built on
collaborative-filtering. In this methodology, users are embedded into a
continuous vector space based on their opinions and clustered into cohorts that
manifest coherent views across specific inquiries. This methodology allows for
a more nuanced understanding of different latent social groups present in the
overall population (as opposed to simply using demographic groups) and enhances
the applicability of model steerability. Finally, we present an efficient
method to steer LLMs towards a particular persona. We learn a soft-prompting
model to map the continuous representation of users into sequences of virtual
tokens which, when prepended to the LLM input, enables the LLM to produce
responses aligned with a given user. Our results show that our steerability
algorithm is superior in performance compared to a collection of baselines.

通过数据驱动的协同过滤方法，将用户嵌入到连续矢量空间中并聚类为具有一致观点的群体，以便实现更细致、更准确地理解不同社会群体，并增强模型的可操控性。最后，我们提出了一种有效的方法来将大型语言模型导向特定的个人，并通过学习软提示模型将用户的连续表示映射为虚拟令牌序列，从而使模型能够根据给定用户生成对应的回应。我们的结果表明，相比于一系列基准方法，我们的操控性算法具有更好的性能。

关于大型语言模型的操纵能力与基于数据的人物角色

On the steerability of large language models toward data-driven personas

We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative-filtering methods to make unfair predictions for users from
minority groups. We identify the insufficiency of existing fairness metrics and
propose four new metrics that address different forms of unfairness. These
fairness metrics can be optimized by adding fairness terms to the learning
objective. Experiments on synthetic and real data show that our new metrics can
better measure fairness than the baseline, and that the fairness objectives
effectively help reduce unfairness.

研究了协同过滤推荐系统中存在的歧视问题，提出四个新的公平度量标准来优化目标函数以减少不公平现象。通过在合成数据和真实数据上进行实验，证明我们的新度量标准比基准更能衡量公平性，并且公平目标的确帮助减少不公平现象。