Understanding and attributing mental states, known as Theory of Mind (ToM),
emerges as a fundamental capability for human social reasoning. While Large
Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms
underlying these capabilities remain elusive. In this study, we discover that
it is possible to linearly decode the belief status from the perspectives of
various agents through neural activations of language models, indicating the
existence of internal representations of self and others' beliefs. By
manipulating these representations, we observe dramatic changes in the models'
ToM performance, underscoring their pivotal role in the social reasoning
process. Additionally, our findings extend to diverse social reasoning tasks
that involve different causal inference patterns, suggesting the potential
generalizability of these representations.

通过神经激活语言模型，线性解码不同代理人的信念状态，发现其内部包含了自我和他人信念的表征，这些表征对社会推理过程具有关键作用，同时在不同因果推理模式的多种社会推理任务中表现出潜在的泛化能力。

语言模型表达自我和他人的信念

Language Models Represent Beliefs of Self and Others

How can a machine learn to recognize visual attributes emerging out of online
community without a definitive supervised dataset? This paper proposes an
automatic approach to discover and analyze visual attributes from a noisy
collection of image-text data on the Web. Our approach is based on the
relationship between attributes and neural activations in the deep network. We
characterize the visual property of the attribute word as a divergence within
weakly-annotated set of images. We show that the neural activations are useful
for discovering and learning a classifier that well agrees with human
perception from the noisy real-world Web data. The empirical study suggests the
layered structure of the deep neural networks also gives us insights into the
perceptual depth of the given word. Finally, we demonstrate that we can utilize
highly-activating neurons for finding semantically relevant regions.

该研究论文提出了一种无需具体的监督数据集，从网络上嘈杂的图像文本数据中发现和分析视觉属性的自动化方法，其基于属性和深度神经网络中的神经激活之间的关系，该方法对于发现和学习分类器，以及利用高度激活的神经元找到语义相关地区是有用的。

基于神经激活的自动属性发现

Automatic Attribute Discovery with Neural Activations

This paper designs a high-performance deep convolutional network (DeepID2+)
for face recognition. It is learned with the identification-verification
supervisory signal. By increasing the dimension of hidden representations and
adding supervision to early convolutional layers, DeepID2+ achieves new
state-of-the-art on LFW and YouTube Faces benchmarks. Through empirical
studies, we have discovered three properties of its deep neural activations
critical for the high performance: sparsity, selectiveness and robustness. (1)
It is observed that neural activations are moderately sparse. Moderate sparsity
maximizes the discriminative power of the deep net as well as the distance
between images. It is surprising that DeepID2+ still can achieve high
recognition accuracy even after the neural responses are binarized. (2) Its
neurons in higher layers are highly selective to identities and
identity-related attributes. We can identify different subsets of neurons which
are either constantly excited or inhibited when different identities or
attributes are present. Although DeepID2+ is not taught to distinguish
attributes during training, it has implicitly learned such high-level concepts.
(3) It is much more robust to occlusions, although occlusion patterns are not
included in the training set.

本文设计了一个高性能的深度卷积网络（DeepID2+）用于人脸识别，经实验证明它在 LFW 和 YouTube Faces 的基准测试中达到了最新的最优效果，并通过了控制神经激活的稀疏性、选择性和鲁棒性研究证明了其高性能的三个关键属性。