Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning
performance for large language models (LLMs). While some studies focus on
improving CoT accuracy through methods like retrieval enhancement, yet a
rigorous explanation for why CoT achieves such success remains unclear. In this
paper, we analyze CoT methods under two different settings by asking the
following questions: (1) For zero-shot CoT, why does prompting the model with
"let's think step by step" significantly impact its outputs? (2) For few-shot
CoT, why does providing examples before questioning the model could
substantially improve its reasoning ability? To answer these questions, we
conduct a top-down explainable analysis from the Hopfieldian view and propose a
Read-and-Control approach for controlling the accuracy of CoT. Through
extensive experiments on seven datasets for three different tasks, we
demonstrate that our framework can decipher the inner workings of CoT, provide
reasoning error localization, and control to come up with the correct reasoning
path.

通过分析在不同设置下的 CoT 方法，从 Hopfieldian 视角提出了 Read-and-Control 方法，通过广泛实验验证了该框架对于理解 CoT、识别推理错误和控制正确推理路径的能力。

基于视图的 Hopfield 解释的思维链推理

A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning

Novel and high-performance medical image classification pipelines are heavily
utilizing ensemble learning strategies. The idea of ensemble learning is to
assemble diverse models or multiple predictions and, thus, boost prediction
performance. However, it is still an open question to what extent as well as
which ensemble learning strategies are beneficial in deep learning based
medical image classification pipelines. In this work, we proposed a
reproducible medical image classification pipeline for analyzing the
performance impact of the following ensemble learning techniques: Augmenting,
Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing
and image augmentation methods as well as 9 deep convolution neural network
architectures. It was applied on four popular medical imaging datasets with
varying complexity. Furthermore, 12 pooling functions for combining multiple
predictions were analyzed, ranging from simple statistical functions like
unweighted averaging up to more complex learning-based functions like support
vector machines. Our results revealed that Stacking achieved the largest
performance gain of up to 13% F1-score increase. Augmenting showed consistent
improvement capabilities by up to 4% and is also applicable to single model
based pipelines. Cross-validation based Bagging demonstrated significant
performance gain close to Stacking, which resulted in an F1-score increase up
to +11%. Furthermore, we demonstrated that simple statistical pooling functions
are equal or often even better than more complex pooling functions. We
concluded that the integration of ensemble learning techniques is a powerful
method for any medical image classification pipeline to improve robustness and
boost performance.

该研究提出了一种可复现的医学图像分类管道，分析了集成学习技术在深度学习医学图像分类管道中的优化效果，发现 Stacking 技术效果最好，能增加 13％的 F1 得分，此外，Augmenting 和 Bagging 技术也能显著提高性能。此外也展示了简单的统计汇总函数与较复杂的机器学习技术汇总函数相比并不逊色。综述结果表明，集成学习技术的使用能够为医学图像分类管道提高性能和抗干扰能力。

使用深度卷积神经网络优化的集成学习医学图像分类分析

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

Current state-of-the-art neural dialogue models learn from human
conversations following the data-driven paradigm. As such, a reliable training
corpus is the crux of building a robust and well-behaved dialogue model.
However, due to the open-ended nature of human conversations, the quality of
user-generated training data varies greatly, and effective training samples are
typically insufficient while noisy samples frequently appear. This impedes the
learning of those data-driven neural dialogue models. Therefore, effective
dialogue learning requires not only more reliable learning samples, but also
fewer noisy samples. In this paper, we propose a data manipulation framework to
proactively reshape the data distribution towards reliable samples by
augmenting and highlighting effective learning samples as well as reducing the
effect of inefficient samples simultaneously. In particular, the data
manipulation model selectively augments the training samples and assigns an
importance weight to each instance to reform the training data. Note that, the
proposed data manipulation framework is fully data-driven and learnable. It not
only manipulates training samples to optimize the dialogue generation model,
but also learns to increase its manipulation skills through gradient descent
with validation samples. Extensive experiments show that our framework can
improve the dialogue generation performance with respect to various automatic
evaluation metrics and human judgments.

本文提出了一种数据操纵框架来通过增强和突显有效的学习样本以及同时减少无效样本的影响来主动重塑数据分布，以优化对话生成模型的训练样本。通过选择性地增加训练样本并为每个实例分配重要性权重来转换训练数据。结果表明，该框架能够改善对话生成性能，符合各种自动评估指标和人类判断。