Machine learning practitioners often face significant challenges in formally
integrating their prior knowledge and beliefs into predictive models, limiting
the potential for nuanced and context-aware analyses. Moreover, the expertise
needed to integrate this prior knowledge into probabilistic modeling typically
limits the application of these models to specialists. Our goal is to build a
regression model that can process numerical data and make probabilistic
predictions at arbitrary locations, guided by natural language text which
describes a user's prior knowledge. Large Language Models (LLMs) provide a
useful starting point for designing such a tool since they 1) provide an
interface where users can incorporate expert insights in natural language and
2) provide an opportunity for leveraging latent problem-relevant knowledge
encoded in LLMs that users may not have themselves. We start by exploring
strategies for eliciting explicit, coherent numerical predictive distributions
from LLMs. We examine these joint predictive distributions, which we call LLM
Processes, over arbitrarily-many quantities in settings such as forecasting,
multi-dimensional regression, black-box optimization, and image modeling. We
investigate the practical details of prompting to elicit coherent predictive
distributions, and demonstrate their effectiveness at regression. Finally, we
demonstrate the ability to usefully incorporate text into numerical
predictions, improving predictive performance and giving quantitative structure
that reflects qualitative descriptions. This lets us begin to explore the rich,
grounded hypothesis space that LLMs implicitly encode.

构建能够处理数字数据并根据自然语言文本进行概率预测的回归模型，以整合先前的知识和信念，提高预测性能并给出定量结构。

LLM 过程：基于自然语言的数值预测分布

LLM Processes: Numerical Predictive Distributions Conditioned on Natural  Language

To answer a question, language models often need to integrate prior knowledge
learned during pretraining and new information presented in context. We
hypothesize that models perform this integration in a predictable way across
different questions and contexts: models will rely more on prior knowledge for
questions about entities (e.g., persons, places, etc.) that they are more
familiar with due to higher exposure in the training corpus, and be more easily
persuaded by some contexts than others. To formalize this problem, we propose
two mutual information-based metrics to measure a model's dependency on a
context and on its prior about an entity: first, the persuasion score of a
given context represents how much a model depends on the context in its
decision, and second, the susceptibility score of a given entity represents how
much the model can be swayed away from its original answer distribution about
an entity. Following well-established measurement modeling methods, we
empirically test for the validity and reliability of these metrics. Finally, we
explore and find a relationship between the scores and the model's expected
familiarity with an entity, and provide two use cases to illustrate their
benefits.

语言模型的先验知识与上下文信息的整合方式对不同问题和上下文情境具有可预见的特征，模型在涉及熟悉实体的问题中更依赖于先验知识，易受特定情境影响。我们提出两个基于互信息的度量，分别衡量模型对上下文的依赖和对实体先验的敏感性。通过经验测试，验证了这些度量的有效性和可靠性，并发现其与模型对实体的熟悉程度存在关联，提供两个使用案例来说明其优势。

语境与先验知识在语言模型中的比较

Context versus Prior Knowledge in Language Models

In the field of eXplainable Artificial Intelligence (XAI), post-hoc
interpretability methods aim at explaining to a user the predictions of a
trained decision model. Integrating prior knowledge into such interpretability
methods aims at improving the explanation understandability and allowing for
personalised explanations adapted to each user. In this paper, we propose to
define a cost function that explicitly integrates prior knowledge into the
interpretability objectives: we present a general framework for the
optimization problem of post-hoc interpretability methods, and show that user
knowledge can thus be integrated to any method by adding a compatibility term
in the cost function. We instantiate the proposed formalization in the case of
counterfactual explanations and propose a new interpretability method called
Knowledge Integration in Counterfactual Explanation (KICE) to optimize it. The
paper performs an experimental study on several benchmark data sets to
characterize the counterfactual instances generated by KICE, as compared to
reference methods.

本文提出了一种新的解释性方法框架，它在拟合先验知识与解释可解释性目标之间增加了兼容性项，并在反事实解释上实例化了提出的形式化方法，称为知识整合反事实解释（KICE）。