Deductive reasoning plays a pivotal role in the formulation of sound and
cohesive arguments. It allows individuals to draw conclusions that logically
follow, given the truth value of the information provided. Recent progress in
the domain of large language models (LLMs) has showcased their capability in
executing deductive reasoning tasks. Nonetheless, a significant portion of
research primarily assesses the accuracy of LLMs in solving such tasks, often
overlooking a deeper analysis of their reasoning behavior. In this study, we
draw upon principles from cognitive psychology to examine inferential
strategies employed by LLMs, through a detailed evaluation of their responses
to propositional logic problems. Our findings indicate that LLMs display
reasoning patterns akin to those observed in humans, including strategies like
$\textit{supposition following}$ or $\textit{chain construction}$. Moreover,
our research demonstrates that the architecture and scale of the model
significantly affect its preferred method of reasoning, with more advanced
models tending to adopt strategies more frequently than less sophisticated
ones. Importantly, we assert that a model's accuracy, that is the correctness
of its final conclusion, does not necessarily reflect the validity of its
reasoning process. This distinction underscores the necessity for more nuanced
evaluation procedures in the field.

该研究通过对大型语言模型在命题逻辑问题上的响应进行细致评估，利用认知心理学原理探讨了模型使用的推理策略。结果发现，大型语言模型展示出类似于人类的推理模式，包括 “解释跟踪” 和 “链式构建” 等策略。此外，该研究表明模型的架构和规模显著影响其首选的推理方法，较先进的模型更倾向于频繁使用这些策略。模型的准确性并不必然反映其推理过程的有效性，这一区别强调了该领域需要更为精细的评估程序。

人类和大型语言模型在演绎推理中的推理策略比较

Comparing Inferential Strategies of Humans and Large Language Models in  Deductive Reasoning

We describe Venture, an interactive virtual machine for probabilistic
programming that aims to be sufficiently expressive, extensible, and efficient
for general-purpose use. Like Church, probabilistic models and inference
problems in Venture are specified via a Turing-complete, higher-order
probabilistic language descended from Lisp. Unlike Church, Venture also
provides a compositional language for custom inference strategies built out of
scalable exact and approximate techniques. We also describe four key aspects of
Venture's implementation that build on ideas from probabilistic graphical
models. First, we describe the stochastic procedure interface (SPI) that
specifies and encapsulates primitive random variables. The SPI supports custom
control flow, higher-order probabilistic procedures, partially exchangeable
sequences and ``likelihood-free'' stochastic simulators. It also supports
external models that do inference over latent variables hidden from Venture.
Second, we describe probabilistic execution traces (PETs), which represent
execution histories of Venture programs. PETs capture conditional dependencies,
existential dependencies and exchangeable coupling. Third, we describe
partitions of execution histories called scaffolds that factor global inference
problems into coherent sub-problems. Finally, we describe a family of
stochastic regeneration algorithms for efficiently modifying PET fragments
contained within scaffolds. Stochastic regeneration linear runtime scaling in
cases where many previous approaches scaled quadratically. We show how to use
stochastic regeneration and the SPI to implement general-purpose inference
strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals
based on particle Markov chain Monte Carlo and mean-field variational inference
techniques.

Venture 是一种交互式虚拟机，支持概率编程，并提供定制推理策略的组成语言，可实现泊松图模型等概率图形模型。它还通过随机生成算法优化了代码的执行历史，可以用于生成式推理策略，如元卡洛马尔可夫链蒙特卡罗和平均场变分推理技术。

Venture: 一个具有可编程推理的高阶概率编程平台

Venture: a higher-order probabilistic programming platform with  programmable inference

Exponential family extensions of principal component analysis (EPCA) have
received a considerable amount of attention in recent years, demonstrating the
growing need for basic modeling tools that do not assume the squared loss or
Gaussian distribution. We extend the EPCA model toolbox by presenting the first
exponential family multi-view learning methods of the partial least squares and
canonical correlation analysis, based on a unified representation of EPCA as
matrix factorization of the natural parameters of exponential family. The
models are based on a new family of priors that are generally usable for all
such factorizations. We also introduce new inference strategies, and
demonstrate how the methods outperform earlier ones when the Gaussianity
assumption does not hold.

该文章介绍了将指数族扩展应用于多视角学习方法中，它基于 EPCA 的矩阵分解，以指数族的自然参数表示，同时提出了一种新的可用于所有因子分解的先验分布族，并演示了当高斯分布假设不成立时它的优越性。