The use of automatic short answer grading (ASAG) models may help alleviate
the time burden of grading while encouraging educators to frequently
incorporate open-ended items in their curriculum. However, current
state-of-the-art ASAG models are large neural networks (NN) often described as
"black box", providing no explanation for which characteristics of an input are
important for the produced output. This inexplicable nature can be frustrating
to teachers and students when trying to interpret, or learn from an
automatically-generated grade. To create a powerful yet intelligible ASAG
model, we experiment with a type of model called a Neural Additive Model that
combines the performance of a NN with the explainability of an additive model.
We use a Knowledge Integration (KI) framework from the learning sciences to
guide feature engineering to create inputs that reflect whether a student
includes certain ideas in their response. We hypothesize that indicating the
inclusion (or exclusion) of predefined ideas as features will be sufficient for
the NAM to have good predictive power and interpretability, as this may guide a
human scorer using a KI rubric. We compare the performance of the NAM with
another explainable model, logistic regression, using the same features, and to
a non-explainable neural model, DeBERTa, that does not require feature
engineering.

使用自动短答案评分（ASAG）模型可以减轻评分的时间负担，同时鼓励教育者经常在课程中引入开放性问题。然而，目前最先进的 ASAG 模型是大型神经网络（NN），常被描述为 “黑匣子”，对于生成的输出哪些特征是重要的没有解释。为了创建一个强大且可解释的 ASAG 模型，我们尝试使用一种称为神经可添加模型（NAM）的模型类型，将 NN 的性能与可解释性模型的性能结合起来。我们使用学习科学中的知识整合（KI）框架指导特征工程，创建反映学生回答中是否包含某些思想的输入。我们假设指示预定义思想的包含（或排除）作为特征将足以使 NAM 具有良好的预测能力和可解释性，因为这可以指导使用 KI 评分标准的人工评分人员。我们使用相同的特征比较 NAM 和另一个可解释模型逻辑回归的性能，以及不需要特征工程的不可解释的神经模型 DeBERTa。

可解释的神经叠加模型自动评分

Explainable Automatic Grading with Neural Additive Models

Many diagnostic errors occur because clinicians cannot easily access relevant
information in patient Electronic Health Records (EHRs). In this work we
propose a method to use LLMs to identify pieces of evidence in patient EHR data
that indicate increased or decreased risk of specific diagnoses; our ultimate
aim is to increase access to evidence and reduce diagnostic errors. In
particular, we propose a Neural Additive Model to make predictions backed by
evidence with individualized risk estimates at time-points where clinicians are
still uncertain, aiming to specifically mitigate delays in diagnosis and errors
stemming from an incomplete differential. To train such a model, it is
necessary to infer temporally fine-grained retrospective labels of eventual
"true" diagnoses. We do so with LLMs, to ensure that the input text is from
before a confident diagnosis can be made. We use an LLM to retrieve an initial
pool of evidence, but then refine this set of evidence according to
correlations learned by the model. We conduct an in-depth evaluation of the
usefulness of our approach by simulating how it might be used by a clinician to
decide between a pre-defined list of differential diagnoses.

使用 LLMs 方法识别患者电子健康记录中表明特定诊断风险增加或减少的证据，以提高证据获取并减少诊断错误。通过神经附加模型在临床医生不确定时点进行预测，具有个体化的风险估计，旨在减少诊断延误和因不完整鉴别引起的错误。使用 LLMs 推断细粒度的过去真实诊断标签，确保输入文本在自信诊断之前。通过模型学习的相关性，从初始证据集中筛选出更精确的证据，通过模拟临床医生在预定义鉴别诊断列表中选择来详细评估我们方法的实用性。

朝着可解释的风险预测减少诊断误差

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction

Deep neural networks (DNNs) have found successful applications in many
fields, but their black-box nature hinders interpretability. This is addressed
by the neural additive model (NAM), in which the network is divided into
additive sub-networks, thus making apparent the interaction between input
features and predictions. In this paper, we approach the additive structure
from a Bayesian perspective and develop a practical Laplace approximation. This
enhances interpretability in three primary ways: a) It provides credible
intervals for the recovered feature interactions by estimating function-space
uncertainty of the sub-networks; b) it yields a tractable estimate of the
marginal likelihood, which can be used to perform an implicit selection of
features through an empirical Bayes procedure; and c) it can be used to rank
feature pairs as candidates for second-order interactions in fine-tuned
interaction models. We show empirically that our proposed Laplace-approximated
NAM (LA-NAM) improves performance and interpretability on tabular regression
and classification datasets and challenging real-world medical tasks.

本文从贝叶斯角度探讨神经相加模型，并开发了实用的拉普拉斯近似方法。研究结果表明，使用该方法得到的神经相加模型可以提高表格回归和分类数据集以及现实世界医学任务的性能和解释性。