Current Large Language Models (LLMs) are unparalleled in their ability to
generate grammatically correct, fluent text. LLMs are appearing rapidly, and
debates on LLM capacities have taken off, but reflection is lagging behind.
Thus, in this position paper, we first zoom in on the debate and critically
assess three points recurring in critiques of LLM capacities: i) that LLMs only
parrot statistical patterns in the training data; ii) that LLMs master formal
but not functional language competence; and iii) that language learning in LLMs
cannot inform human language learning. Drawing on empirical and theoretical
arguments, we show that these points need more nuance. Second, we outline a
pragmatic perspective on the issue of `real' understanding and intentionality
in LLMs. Understanding and intentionality pertain to unobservable mental states
we \textit{attribute} to other humans because they have \textit{pragmatic
value}: they allow us to abstract away from complex underlying mechanics and
predict behaviour effectively. We reflect on the circumstances under which it
would make sense for humans to similarly attribute mental states to LLMs,
thereby outlining a pragmatic philosophical context for LLMs as an increasingly
prominent technology in society.

当前大型语言模型（LLMs）在生成符合语法、流畅的文本方面无与伦比。这篇论文针对 LLMs 的能力进行了辩论，并通过批判性评估三个经常在批评中出现的观点来展示 LLMs 仍需更多细化。其次，文章从实证和理论的角度提出了对 LLMs 中 “真正” 的理解和意向性的实用观点，并讨论了在何种情况下将心理状态归因于 LLMs 对于这一日益重要的技术在社会中具有实用的哲学背景。

大型语言模型：当前辩论的细腻需求和实用角度下的理解

Large Language Models: The Need for Nuance in Current Debates and a  Pragmatic Perspective on Understanding

As large language models improve, there is increasing interest in techniques
that leverage these models' capabilities to refine their own outputs. In this
work, we introduce Shepherd, a language model specifically tuned to critique
responses and suggest refinements, extending beyond the capabilities of an
untuned model to identify diverse errors and provide suggestions to remedy
them. At the core of our approach is a high quality feedback dataset, which we
curate from community feedback and human annotations. Even though Shepherd is
small (7B parameters), its critiques are either equivalent or preferred to
those from established models including ChatGPT. Using GPT-4 for evaluation,
Shepherd reaches an average win-rate of 53-87% compared to competitive
alternatives. In human evaluation, Shepherd strictly outperforms other models
and on average closely ties with ChatGPT.

Shepherd 是一个专门针对评论回复和建议改进进行微调的语言模型，通过高质量的反馈数据集从社区反馈和人类注释中提供多样化的错误识别和建议修正，相比于 ChatGPT 等现有模型，Shepherd 具有等效或更优质的评论，通过 GPT-4 的评估，Shepherd 的胜率平均为 53-87%，在人类评估中，Shepherd 的表现明显优于其他模型，平均与 ChatGPT 持平。

Shepherd: 语言模型生成的评论家

Shepherd: A Critic for Language Model Generation

This survey article assesses and compares existing critiques of current
fairness-enhancing technical interventions into machine learning (ML) that draw
from a range of non-computing disciplines, including philosophy, feminist
studies, critical race and ethnic studies, legal studies, anthropology, and
science and technology studies. It bridges epistemic divides in order to offer
an interdisciplinary understanding of the possibilities and limits of hegemonic
computational approaches to ML fairness for producing just outcomes for
society's most marginalized. The article is organized according to nine major
themes of critique wherein these different fields intersect: 1) how "fairness"
in AI fairness research gets defined; 2) how problems for AI systems to address
get formulated; 3) the impacts of abstraction on how AI tools function and its
propensity to lead to technological solutionism; 4) how racial classification
operates within AI fairness research; 5) the use of AI fairness measures to
avoid regulation and engage in ethics washing; 6) an absence of participatory
design and democratic deliberation in AI fairness considerations; 7) data
collection practices that entrench "bias," are non-consensual, and lack
transparency; 8) the predatory inclusion of marginalized groups into AI
systems; and 9) a lack of engagement with AI's long-term social and ethical
outcomes. Drawing from these critiques, the article concludes by imagining
future ML fairness research directions that actively disrupt entrenched power
dynamics and structural injustices in society.

本文评估并比较了来自哲学、女性主义研究、批判性种族和民族研究、法律研究、人类学和科学技术研究等非计算学科的现有批评，旨在提供 ML 公正技术干预的跨学科理解，以此产生社会上最边缘化群体的公正结果，最后讨论了基于这些批评的未来 ML 公正研究方向。