In this work, we present a method to control a text-to-image generative model
to produce training data specifically "useful" for supervised learning. Unlike
previous works that employ an open-loop approach and pre-define prompts to
generate new data using either a language model or human expertise, we develop
an automated closed-loop system which involves two feedback mechanisms. The
first mechanism uses feedback from a given supervised model and finds
adversarial prompts that result in image generations that maximize the model
loss. While these adversarial prompts result in diverse data informed by the
model, they are not informed of the target distribution, which can be
inefficient. Therefore, we introduce the second feedback mechanism that guides
the generation process towards a certain target distribution. We call the
method combining these two mechanisms Guided Adversarial Prompts. We perform
our evaluations on different tasks, datasets and architectures, with different
types of distribution shifts (spuriously correlated data, unseen domains) and
demonstrate the efficiency of the proposed feedback mechanisms compared to
open-loop approaches.

这项研究提出了一种利用反馈机制来控制文本到图像生成模型，以生成对监督学习特别有用的训练数据的方法，并且通过引入指导目标分布的反馈机制，演示了该方法在不同任务、数据集和架构上相对于开环方法的有效性。

使用扩散模型进行受控训练数据生成

Controlled Training Data Generation with Diffusion Models

This study investigates GPT-4's assessment of its performance in healthcare
applications. A simple prompting technique was used to prompt the LLM with
questions taken from the United States Medical Licensing Examination (USMLE)
questionnaire and it was tasked to evaluate its confidence score before posing
the question and after asking the question. The questionnaire was categorized
into two groups-questions with feedback (WF) and questions with no feedback(NF)
post-question. The model was asked to provide absolute and relative confidence
scores before and after each question. The experimental findings were analyzed
using statistical tools to study the variability of confidence in WF and NF
groups. Additionally, a sequential analysis was conducted to observe the
performance variation for the WF and NF groups. Results indicate that feedback
influences relative confidence but doesn't consistently increase or decrease
it. Understanding the performance of LLM is paramount in exploring its utility
in sensitive areas like healthcare. This study contributes to the ongoing
discourse on the reliability of AI, particularly of LLMs like GPT-4, within
healthcare, offering insights into how feedback mechanisms might be optimized
to enhance AI-assisted medical education and decision support.

本研究探讨了 GPT-4 在医疗应用中的性能评估，使用简单的提示技术从美国医学执照考试问卷中提取问题作为提示，任务是在提问前和提问后评估它的置信度得分。分为有反馈和无反馈两组问题进行分类，实验结果表明反馈会影响相对置信度但并不一致地增加或减少。这项研究对于 AI 可靠性的讨论具有重要意义，特别是在医疗领域中的 LLM 类模型（如 GPT-4），为优化反馈机制以提升 AI 辅助医学教育和决策支持提供了有价值的见解。

GPT-4 在基于 USMLE 的病例研究中的评估

GPT-4's assessment of its performance in a USMLE-based case study

We consider schemes for obtaining truthful reports on a common but hidden
signal from large groups of rational, self-interested agents. One example are
online feedback mechanisms, where users provide observations about the quality
of a product or service so that other users can have an accurate idea of what
quality they can expect. However, (i) providing such feedback is costly, and
(ii) there are many motivations for providing incorrect feedback.
Both problems can be addressed by reward schemes which (i) cover the cost of
obtaining and reporting feedback, and (ii) maximize the expected reward of a
rational agent who reports truthfully. We address the design of such
incentive-compatible rewards for feedback generated in environments with pure
adverse selection. Here, the correlation between the true knowledge of an agent
and her beliefs regarding the likelihoods of reports of other agents can be
exploited to make honest reporting a Nash equilibrium.
In this paper we extend existing methods for designing incentive-compatible
rewards by also considering collusion. We analyze different scenarios, where,
for example, some or all of the agents collude. For each scenario we
investigate whether a collusion-resistant, incentive-compatible reward scheme
exists, and use automated mechanism design to specify an algorithm for deriving
an efficient reward mechanism.

本论文考虑如何设计一种激励机制，以在固有的逆向选择环境下，利用纯粹的协作或对抗以及报告代价获得真实从众意见汇报。