The model editing problem concerns how language models should learn new facts
about the world over time. While empirical research on model editing has drawn
widespread attention, the conceptual foundations of model editing remain shaky
-- perhaps unsurprisingly, since model editing is essentially belief revision,
a storied problem in philosophy that has eluded succinct solutions for decades.
Model editing nonetheless demands a solution, since we need to be able to
control the knowledge within language models. With this goal in mind, this
paper critiques the standard formulation of the model editing problem and
proposes a formal testbed for model editing research. We first describe 12 open
problems with model editing, based on challenges with (1) defining the problem,
(2) developing benchmarks, and (3) assuming LLMs have editable beliefs in the
first place. Many of these challenges are extremely difficult to address, e.g.
determining far-reaching consequences of edits, labeling probabilistic
entailments between facts, and updating beliefs of agent simulators. Next, we
introduce a semi-synthetic dataset for model editing based on Wikidata, where
we can evaluate edits against labels given by an idealized Bayesian agent. This
enables us to say exactly how belief revision in language models falls short of
a desirable epistemic standard. We encourage further research exploring
settings where such a gold standard can be compared against. Our code is
publicly available at: this https URL

该研究采用半合成数据集的方法，对模型编辑问题进行实证研究，旨在探索语言模型的知识控制方式，进一步提出一个形式化测试平台，批判现有模型编辑问题的标准公式，并证明其离期望的认识标准还有一定差距。

模型编辑的基本问题： 如何在大型语言模型中进行合理的信念修正工作？

Fundamental Problems With Model Editing: How Should Rational Belief  Revision Work in LLMs?

The objective of this work is to segment human body parts from egocentric
video using semantic segmentation networks. Our contribution is two-fold: i) we
create a semi-synthetic dataset composed of more than 15, 000 realistic images
and associated pixel-wise labels of egocentric human body parts, such as arms
or legs including different demographic factors; ii) building upon the
ThunderNet architecture, we implement a deep learning semantic segmentation
algorithm that is able to perform beyond real-time requirements (16 ms for 720
x 720 images). It is believed that this method will enhance sense of presence
of Virtual Environments and will constitute a more realistic solution to the
standard virtual avatars.

本文提出一种使用语义分割网络从自我中心视频中分割人体部位的方法。方法包括使用半合成数据集和基于 ThunderNet 架构的深度学习语义分割算法，用于构建比标准虚拟化身更逼真的虚拟环境，实现对人体部位的高效分割。

混合现实中的自我中心人体分割

Egocentric Human Segmentation for Mixed Reality

We present a Neural Program Search, an algorithm to generate programs from
natural language description and a small number of input/output examples. The
algorithm combines methods from Deep Learning and Program Synthesis fields by
designing rich domain-specific language (DSL) and defining efficient search
algorithm guided by a Seq2Tree model on it. To evaluate the quality of the
approach we also present a semi-synthetic dataset of descriptions with test
examples and corresponding programs. We show that our algorithm significantly
outperforms a sequence-to-sequence model with attention baseline.

本文提出了一种用自然语言描述和少量输入 / 输出样例来生成程序的算法，称之为神经程序搜索。该算法将深度学习和程序合成领域的方法结合起来，通过设计丰富的领域特定语言和根据 Seq2Tree 模型进行指导的高效搜索算法。通过一个半合成的描述和测试案例数据集对该方法的质量进行了评估，结果表明我们的算法明显优于基线的带注意力的序列到序列模型。