While Large Language Models (LLMs) have shown exceptional performance in
various tasks, their (arguably) most prominent drawback is generating
inaccurate or false information with a confident tone. In this paper, we
hypothesize that the LLM's internal state can be used to reveal the
truthfulness of a statement. Therefore, we introduce a simple yet effective
method to detect the truthfulness of LLM-generated statements, which utilizes
the LLM's hidden layer activations to determine the veracity of statements. To
train and evaluate our method, we compose a dataset of true and false
statements in six different topics. A classifier is trained to detect which
statement is true or false based on an LLM's activation values. Specifically,
the classifier receives as input the activation values from the LLM for each of
the statements in the dataset. Our experiments demonstrate that our method for
detecting statement veracity significantly outperforms even few-shot prompting
methods, highlighting its potential to enhance the reliability of LLM-generated
content and its practical applicability in real-world scenarios.

介绍了一种基于大型语言模型的 “激活函数值” 的真实性检测方法，进一步提高了文本可靠性和实际应用价值。

LLM 内在状态知道自己在撒谎

The Internal State of an LLM Knows When its Lying

A key to deciphering the inner workings of neural networks is understanding
what a model has learned. Promising methods for discovering learned features
are based on analyzing activation values, whereby current techniques focus on
analyzing high activation values to reveal interesting features on a neuron
level. However, analyzing high activation values limits layer-level concept
discovery. We present a method that instead takes into account the entire
activation distribution. By extracting similar activation profiles within the
high-dimensional activation space of a neural network layer, we find groups of
inputs that are treated similarly. These input groups represent neural
activation patterns (NAPs) and can be used to visualize and interpret learned
layer concepts. We release a framework with which NAPs can be extracted from
pre-trained models and provide a visual introspection tool that can be used to
analyze NAPs. We tested our method with a variety of networks and show how it
complements existing methods for analyzing neural network activation values.

通过提取神经网络层中高维激活空间内相似的激活特点，我们找到了一些被对待相似的输入群体，这些输入群体代表神经激活模式 (NAPs)，并可用于可视化和解释学习层概念，从而实现了神经网络的分析方法的互补。

神经激活模式（NAPs）：学习概念的视觉可解释性

Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts

Quantization is one of the most effective methods to compress neural
networks, which has achieved great success on convolutional neural networks
(CNNs). Recently, vision transformers have demonstrated great potential in
computer vision. However, previous post-training quantization methods performed
not well on vision transformer, resulting in more than 1% accuracy drop even in
8-bit quantization. Therefore, we analyze the problems of quantization on
vision transformers. We observe the distributions of activation values after
softmax and GELU functions are quite different from the Gaussian distribution.
We also observe that common quantization metrics, such as MSE and cosine
distance, are inaccurate to determine the optimal scaling factor. In this
paper, we propose the twin uniform quantization method to reduce the
quantization error on these activation values. And we propose to use a Hessian
guided metric to evaluate different scaling factors, which improves the
accuracy of calibration at a small cost. To enable the fast quantization of
vision transformers, we develop an efficient framework, PTQ4ViT. Experiments
show the quantized vision transformers achieve near-lossless prediction
accuracy (less than 0.5% drop at 8-bit quantization) on the ImageNet
classification task.

本文提出了双均匀量化方法和用 Hessian 指导的指标方法来优化视觉转换器上量化的准确度，提出了一个高效的框架 PTQ4ViT，实验证明量化视觉转换器在 ImageNet 分类任务上能够实现接近无损的预测准确度（8 位量化的准确度降低小于 0.5%）。