Simulating sampling algorithms with people has proven a useful method for
efficiently probing and understanding their mental representations. We propose
that the same methods can be used to study the representations of Large
Language Models (LLMs). While one can always directly prompt either humans or
LLMs to disclose their mental representations introspectively, we show that
increased efficiency can be achieved by using LLMs as elements of a sampling
algorithm. We explore the extent to which we recover human-like representations
when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo
(MCMC). We found a significant increase in efficiency and performance using
adaptive sampling algorithms based on MCMC. We also highlight the potential of
our method to yield a more general method of conducting Bayesian inference
\textit{with} LLMs.

使用大型语言模型（LLMs）作为抽样算法的元素，可以有效地研究 LLMs 的心理表征。我们通过使用 Direct Sampling 和 Markov chain Monte Carlo（MCMC）基于自适应抽样算法，实现了高效和高性能的心理表征恢复，同时展示了通过 LLMs 进行贝叶斯推断的潜力。

使用马尔可夫链蒙特卡洛从大型语言模型中恢复心智表征

Recovering Mental Representations from Large Language Models with Markov  Chain Monte Carlo

Similarity judgments provide a well-established method for accessing mental
representations, with applications in psychology, neuroscience and machine
learning. However, collecting similarity judgments can be prohibitively
expensive for naturalistic datasets as the number of comparisons grows
quadratically in the number of stimuli. One way to tackle this problem is to
construct approximation procedures that rely on more accessible proxies for
predicting similarity. Here we leverage recent advances in language models and
online recruitment, proposing an efficient domain-general procedure for
predicting human similarity judgments based on text descriptions. Intuitively,
similar stimuli are likely to evoke similar descriptions, allowing us to use
description similarity to predict pairwise similarity judgments. Crucially, the
number of descriptions required grows only linearly with the number of stimuli,
drastically reducing the amount of data required. We test this procedure on six
datasets of naturalistic images and show that our models outperform previous
approaches based on visual information.

利用语言模型和在线招募，提出了一种基于文本描述的高效通用程序，以预测相似度判断，其数量只随刺激物数量线性增长，可极大减少数据需求，且在六个自然图像数据集上的结果优于基于视觉信息的先前方法。

利用大型语言模型预测人类相似性评价

Predicting Human Similarity Judgments Using Large Language Models

When human cognition is modeled in Philosophy and Cognitive Science, there is
a pervasive idea that humans employ mental representations in order to navigate
the world and make predictions about outcomes of future actions. By
understanding how these representational structures work, we not only
understand more about human cognition but also gain a better understanding for
how humans rationalise and explain decisions. This has an influencing effect on
explainable AI, where the goal is to provide explanations of computer
decision-making for a human audience. We show that the Contextual Importance
and Utility method for XAI share an overlap with the current new wave of
action-oriented predictive representational structures, in ways that makes CIU
a reliable tool for creating explanations that humans can relate to and trust.

通过理解人类认知中心的特点，提出了使用 “上下文重要性和效用（Contextual Importance and Utility method）” 等方法去实现可解释的 AI 决策。