Language models (LMs) are being scaled and becoming powerful. Improving their
efficiency is one of the core research topics in neural information processing
systems. Tay et al. (2022) provided a comprehensive overview of efficient
Transformers that have become an indispensable staple in the field of NLP.
However, in the section of "On Evaluation", they left an open question "which
fundamental efficient Transformer one should consider," answered by "still a
mystery" because "many research papers select their own benchmarks."
Unfortunately, there was not quantitative analysis about the performances of
Transformers on any benchmarks. Moreover, state space models (SSMs) have
demonstrated their abilities of modeling long-range sequences with
non-attention mechanisms, which were not discussed in the prior review. This
article makes a meta analysis on the results from a set of papers on efficient
Transformers as well as those on SSMs. It provides a quantitative review on LM
efficiency research and gives suggestions for future research.

本文对一系列高效 Transformer 和状态空间模型进行了元分析，系统地回顾了语言模型效率研究的现状，并对未来研究提出了建议。

语言模型效率研究的定量综述

A Quantitative Review on Language Model Efficiency Research

Turning principles into practice is one of the most pressing challenges of
artificial intelligence (AI) governance. In this article, we reflect on a novel
governance initiative by one of the world's largest AI conferences. In 2020,
the Conference on Neural Information Processing Systems (NeurIPS) introduced a
requirement for submitting authors to include a statement on the broader
societal impacts of their research. Drawing insights from similar governance
initiatives, including institutional review boards (IRBs) and impact
requirements for funding applications, we investigate the risks, challenges and
potential benefits of such an initiative. Among the challenges, we list a lack
of recognised best practice and procedural transparency, researcher opportunity
costs, institutional and social pressures, cognitive biases, and the inherently
difficult nature of the task. The potential benefits, on the other hand,
include improved anticipation and identification of impacts, better
communication with policy and governance experts, and a general strengthening
of the norms around responsible research. To maximise the chance of success, we
recommend measures to increase transparency, improve guidance, create
incentives to engage earnestly with the process, and facilitate public
deliberation on the requirement's merits and future. Perhaps the most important
contribution from this analysis are the insights we can gain regarding
effective community-based governance and the role and responsibility of the AI
research community more broadly.

本文回顾了世界上最大的人工智能会议之一通过要求提交作者包含其研究对更广泛社会影响的声明的新型治理举措，探讨了此类举措的风险，挑战和潜在好处，包括改善对影响的预测和识别，更好地与政策和治理专家沟通以及加强负责任研究的规范的潜在好处；同时提出了增加透明度，改进指导，创造积极参与这一过程的激励措施，并促进公众就要求的优点和未来进行公开讨论等建议。

通过更广泛的影响要求将伦理道德制度化于人工智能中

Institutionalising Ethics in AI through Broader Impact Requirements

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate
measurable progress towards robust machine vision models and more generally
applicable adversarial attacks. This document is an updated version of our
competition proposal that was accepted in the competition track of 32nd
Conference on Neural Information Processing Systems (NIPS 2018).

NIPS 2018 Adversarial Vision Challenge aims to promote robust machine vision models and adversarial attacks, and this document presents the updated competition proposal accepted at the Neural Information Processing Systems conference.

对抗视觉挑战

Adversarial Vision Challenge

Neural Information Processing Systems (NIPS) is a top-tier annual conference
in machine learning. The 2016 edition of the conference comprised more than
2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents
a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and
over 100% in terms of attendees as compared to the previous year. The massive
scale as well as rapid growth of the conference calls for a thorough quality
assessment of the peer-review process and novel means of improvement. In this
paper, we analyze several aspects of the data collected during the review
process, including an experiment investigating the efficacy of collecting
ordinal rankings from reviewers. Our goal is to check the soundness of the
review process, and provide insights that may be useful in the design of the
review process of subsequent conferences.

本文分析了 Neural Information Processing Systems 会议上关于机器学习的论文审查数据，并测试了从审稿人收集序数排名的效用，旨在检查审查流程的严谨性并提供对后续会议审查流程设计有用的见解。