While state-of-the-art Large Language Models (LLMs) have shown impressive
performance on many tasks, there has been extensive research on undesirable
model behavior such as hallucinations and bias. In this work, we investigate
how the quality of LLM responses changes in terms of information accuracy,
truthfulness, and refusals depending on three user traits: English proficiency,
education level, and country of origin. We present extensive experimentation on
three state-of-the-art LLMs and two different datasets targeting truthfulness
and factuality. Our findings suggest that undesirable behaviors in
state-of-the-art LLMs occur disproportionately more for users with lower
English proficiency, of lower education status, and originating from outside
the US, rendering these models unreliable sources of information towards their
most vulnerable users.

通过对三种领先的大型语言模型 (LLM) 和两个不同的针对真实性和事实性的数据集进行深入实验，我们研究了 LLM 响应质量在信息准确性、真实性和拒绝方面如何随用户的英语水平、教育水平和国籍的不同而变化。我们的研究结果表明，领先的 LLM 模型在不可取的行为方面存在不均衡，对英语水平较低、教育水平较低和来自美国以外的用户具有更多的不可取行为，从而使这些模型对于最弱势的用户成为不可靠的信息来源。

LLM 有针对性的低效率问题主要影响弱势用户

LLM Targeted Underperformance Disproportionately Impacts Vulnerable  Users

When assisting human users in reinforcement learning (RL), we can represent
users as RL agents and study key parameters, called \emph{user traits}, to
inform intervention design. We study the relationship between user behaviors
(policy classes) and user traits. Given an environment, we introduce an
intuitive tool for studying the breakdown of "user types": broad sets of traits
that result in the same behavior. We show that seemingly different real-world
environments admit the same set of user types and formalize this observation as
an equivalence relation defined on environments. By transferring intervention
design between environments within the same equivalence class, we can help
rapidly personalize interventions.

针对辅助人类用户在强化学习中的应用，研究了被称为 "用户特征" 的关键参数，以指导干预设计，同时研究了用户行为 (策略类) 与用户特征之间的关系，通过构建一种易于理解的工具来研究 "用户类型" 的分解，证明了看似不同的现实环境存在相同的用户类型，并将其形式化为在环境上定义的等价关系，通过在同一等价类中的环境之间转移干预设计，可以帮助快速个性化干预。

发现用户类型：通过针对性任务行为映射用户特征在强化学习中

Discovering User Types: Mapping User Traits by Task-Specific Behaviors  in Reinforcement Learning

Existing argumentation datasets have succeeded in allowing researchers to
develop computational methods for analyzing the content, structure and
linguistic features of argumentative text. They have been much less successful
in fostering studies of the effect of "user" traits -- characteristics and
beliefs of the participants -- on the debate/argument outcome as this type of
user information is generally not available. This paper presents a dataset of
78, 376 debates generated over a 10-year period along with surprisingly
comprehensive participant profiles. We also complete an example study using the
dataset to analyze the effect of selected user traits on the debate outcome in
comparison to the linguistic features typically employed in studies of this
kind.

本文介绍了一个包含 78,376 次辩论和全面参与者个人资料的数据集，利用该数据集，与通常用于类似研究的语言特征相比，分析了选定用户特征对辩论结果的影响。