AI assistance in decision-making has become popular, yet people's
inappropriate reliance on AI often leads to unsatisfactory human-AI
collaboration performance. In this paper, through three pre-registered,
randomized human subject experiments, we explore whether and how the provision
of {second opinions} may affect decision-makers' behavior and performance in
AI-assisted decision-making. We find that if both the AI model's decision
recommendation and a second opinion are always presented together,
decision-makers reduce their over-reliance on AI while increase their
under-reliance on AI, regardless whether the second opinion is generated by a
peer or another AI model. However, if decision-makers have the control to
decide when to solicit a peer's second opinion, we find that their active
solicitations of second opinions have the potential to mitigate over-reliance
on AI without inducing increased under-reliance in some cases. We conclude by
discussing the implications of our findings for promoting effective human-AI
collaborations in decision-making.

通过三个预先注册的、随机的人类主体实验，我们探讨了 {第二意见} 的提供是否会影响决策者在人工智能辅助决策中的行为和表现，并且发现如果人工智能模型的决策建议和第二意见总是一起呈现，决策者会减少对人工智能的过度依赖，同时增加对人工智能的不足依赖，而无论第二意见是由同行还是另一个人工智能模型生成。然而，如果决策者有决定何时请教同行意见的控制权，则我们发现他们积极征求第二意见的行为在某些情况下有可能减轻对人工智能的过度依赖，而不引起不足依赖的增加。通过讨论我们的发现对促进有效的人工智能辅助决策中的人机协作的影响，我们得出结论。

第二意见对人工智能辅助决策的影响

Does More Advice Help? The Effects of Second Opinions in AI-Assisted  Decision Making

Large Language Models (LLMs) are increasingly used for accessing information
on the web. Their truthfulness and factuality are thus of great interest. To
help users make the right decisions about the information they're getting, LLMs
should not only provide but also help users fact-check information. In this
paper, we conduct experiments with 80 crowdworkers in total to compare language
models with search engines (information retrieval systems) at facilitating
fact-checking by human users. We prompt LLMs to validate a given claim and
provide corresponding explanations. Users reading LLM explanations are
significantly more efficient than using search engines with similar accuracy.
However, they tend to over-rely the LLMs when the explanation is wrong. To
reduce over-reliance on LLMs, we ask LLMs to provide contrastive information -
explain both why the claim is true and false, and then we present both sides of
the explanation to users. This contrastive explanation mitigates users'
over-reliance on LLMs, but cannot significantly outperform search engines.
However, showing both search engine results and LLM explanations offers no
complementary benefits as compared to search engines alone. Taken together,
natural language explanations by LLMs may not be a reliable replacement for
reading the retrieved passages yet, especially in high-stakes settings where
over-relying on wrong AI explanations could lead to critical consequences.

通过与搜索引擎进行对比实验证明，大型语言模型虽然能提高事实核查的效率，但在解释错误的情况下容易让用户过分依赖，因此在高风险环境中不宜将其作为阅读检索内容的可靠替代品。

大型语言模型协助人类验证真实性 -- 除非其错得令人信服

Large Language Models Help Humans Verify Truthfulness -- Except When  They Are Convincingly Wrong

AI systems have shown impressive performance at answering questions by
retrieving relevant context. However, with the increasingly large models, it is
impossible and often undesirable to constrain models' knowledge or reasoning to
only the retrieved context. This leads to a mismatch between the information
that these models access to derive the answer and the information available to
the user consuming the AI predictions to assess the AI predicted answer. In
this work, we study how users interact with AI systems in absence of sufficient
information to assess AI predictions. Further, we ask the question of whether
adding the requisite background alleviates the concerns around over-reliance in
AI predictions. Our study reveals that users rely on AI predictions even in the
absence of sufficient information needed to assess its correctness. Providing
the relevant background, however, helps users catch AI errors better, reducing
over-reliance on incorrect AI predictions. On the flip side, background
information also increases users' confidence in their correct as well as
incorrect judgments. Contrary to common expectation, aiding a user's perusal of
the context and the background through highlights is not helpful in alleviating
the issue of over-confidence stemming from availability of more information.
Our work aims to highlight the gap between how NLP developers perceive
informational need in human-AI interaction and the actual human interaction
with the information available to them.

研究了用户如何与 AI 系统进行交互以及增加必要的背景是否有助于减轻过度依赖于 AI 预测的问题，并发现提供相关背景信息有助于用户更好地捕捉错误，并减少对不正确 AI 预测的过度依赖。

背景信息对用户依赖人工智能系统的影响

What Else Do I Need to Know? The Effect of Background Information on  Users' Reliance on AI Systems

Knowledge-dependent tasks typically use two sources of knowledge: parametric,
learned at training time, and contextual, given as a passage at inference time.
To understand how models use these sources together, we formalize the problem
of knowledge conflicts, where the contextual information contradicts the
learned information. Analyzing the behaviour of popular models, we measure
their over-reliance on memorized information (the cause of hallucinations), and
uncover important factors that exacerbate this behaviour. Lastly, we propose a
simple method to mitigate over-reliance on parametric knowledge, which
minimizes hallucination, and improves out-of-distribution generalization by
4%-7%. Our findings demonstrate the importance for practitioners to evaluate
model tendency to hallucinate rather than read, and show that our mitigation
strategy encourages generalization to evolving information (i.e.,
time-dependent queries). To encourage these practices, we have released our
framework for generating knowledge conflicts.

本文研究了知识依赖任务中遇到的知识冲突问题，找出造成模型幻觉的原因，提出了一种简单的减轻过度依赖学习参数的方法，可以减少幻觉现象并提高模型的泛化性能。