Given the success of ChatGPT, LaMDA and other large language models (LLMs),
there has been an increase in development and usage of LLMs within the
technology sector and other sectors. While the level in which LLMs has not
reached a level where it has surpassed human intelligence, there will be a time
when it will. Such LLMs can be referred to as advanced LLMs. Currently, there
are limited usage of ethical artificial intelligence (AI) principles and
guidelines addressing advanced LLMs due to the fact that we have not reached
that point yet. However, this is a problem as once we do reach that point, we
will not be adequately prepared to deal with the aftermath of it in an ethical
and optimal way, which will lead to undesired and unexpected consequences. This
paper addresses this issue by discussing what ethical AI principles and
guidelines can be used to address highly advanced LLMs.

通过讨论伦理人工智能原则和指南的使用，本文旨在解决高级大型语言模型（LLMs）的伦理问题。

高级大语言模型的治理和利用的道德人工智能原则和指南

Ethical Artificial Intelligence Principles and Guidelines for the  Governance and Utilization of Highly Advanced Large Language Models

Developmental psychologists have spent decades devising experiments to test
the intelligence and knowledge of infants and children, tracing the origin of
crucial concepts and capacities. Moreover, experimental techniques in
developmental psychology have been carefully designed to discriminate the
cognitive capacities that underlie particular behaviors. We propose that using
classical experiments from child development is a particularly effective way to
probe the computational abilities of AI models, in general, and LLMs in
particular. First, the methodological techniques of developmental psychology,
such as the use of novel stimuli to control for past experience or control
conditions to determine whether children are using simple associations, can be
equally helpful for assessing the capacities of LLMs. In parallel, testing LLMs
in this way can tell us whether the information that is encoded in text is
sufficient to enable particular responses, or whether those responses depend on
other kinds of information, such as information from exploration of the
physical world. In this work we adapt classical developmental experiments to
evaluate the capabilities of LaMDA, a large language model from Google. We
propose a novel LLM Response Score (LRS) metric which can be used to evaluate
other language models, such as GPT. We find that LaMDA generates appropriate
responses that are similar to those of children in experiments involving social
understanding, perhaps providing evidence that knowledge of these domains is
discovered through language. On the other hand, LaMDA's responses in early
object and action understanding, theory of mind, and especially causal
reasoning tasks are very different from those of young children, perhaps
showing that these domains require more real-world, self-initiated exploration
and cannot simply be learned from patterns in language input.

利用儿童发展心理学经典实验评估大型语言模型（LLMs）的能力，提出一种评估 LLMs 能力的 LRS 度量，将 Google 的 LaMDA 模型应用于实验，发现 LaMDA 在社交认知任务中回答的适当反应与儿童相似，但在早期物体行为认知、心理理论以及因果推理方面的回答则与儿童有很大不同，表明这些领域需要更多的现实世界自发探索，不能简单地通过语言输入模式学习。

机器与儿童的比较：使用发展心理学实验评估 LaMDA 响应的优缺点

Comparing Machines and Children: Using Developmental Psychology  Experiments to Assess the Strengths and Weaknesses of LaMDA Responses

This work is intended as a voice in the discussion over the recent claims
that LaMDA, a pretrained language model based on the Transformer model
architecture, is sentient. This claim, if confirmed, would have serious
ramifications in the Natural Language Processing (NLP) community due to
wide-spread use of similar models. However, here we take the position that such
a language model cannot be sentient, or conscious, and that LaMDA in particular
exhibits no advances over other similar models that would qualify it. We
justify this by analysing the Transformer architecture through Integrated
Information Theory. We see the claims of consciousness as part of a wider
tendency to use anthropomorphic language in NLP reporting. Regardless of the
veracity of the claims, we consider this an opportune moment to take stock of
progress in language modelling and consider the ethical implications of the
task. In order to make this work helpful for readers outside the NLP community,
we also present the necessary background in language modelling.

对于最近有人声称基于 Transformer 模型体系结构的预训练语言模型 LaMDA 具有感性的争论中，我们认为这种语言模型不能具有感性或意识，LaMDA 没有表现出其他类似模型的进展，我们通过集成信息理论分析变压器结构来证明这一点，我们认为意识的主张是 NLP 报道中使用人类描述语言的趋势的一部分，无论这些主张的真实性如何，都需要考虑语言建模的进展和相关的伦理问题。

去人格化自然语言处理：语言模型能否有意识？

Deanthropomorphising NLP: Can a Language Model Be Conscious?

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family
of Transformer-based neural language models specialized for dialog, which have
up to 137B parameters and are pre-trained on 1.56T words of public dialog data
and web text. While model scaling alone can improve quality, it shows less
improvements on safety and factual grounding. We demonstrate that fine-tuning
with annotated data and enabling the model to consult external knowledge
sources can lead to significant improvements towards the two key challenges of
safety and factual grounding. The first challenge, safety, involves ensuring
that the model's responses are consistent with a set of human values, such as
preventing harmful suggestions and unfair bias. We quantify safety using a
metric based on an illustrative set of human values, and we find that filtering
candidate responses using a LaMDA classifier fine-tuned with a small amount of
crowdworker-annotated data offers a promising approach to improving model
safety. The second challenge, factual grounding, involves enabling the model to
consult external knowledge sources, such as an information retrieval system, a
language translator, and a calculator. We quantify factuality using a
groundedness metric, and we find that our approach enables the model to
generate responses grounded in known sources, rather than responses that merely
sound plausible. Finally, we explore the use of LaMDA in the domains of
education and content recommendations, and analyze their helpfulness and role
consistency.

LaMDA 是一种专门用于对话的基于 Transformer 的神经语言模型，其具有高达 137B 的参数，并使用公共对话数据和 Web 文本预训练。它具有先进的优化技术，能够解决安全性和事实基础等关键挑战，以及能够在教育和内容推荐领域发挥积极作用。