A multitude of industries depend on accurate and reasonable tabular data
augmentation for their business processes. Contemporary methodologies in
generating tabular data revolve around utilizing Generative Adversarial
Networks (GAN) or fine-tuning Large Language Models (LLM). However, GAN-based
approaches are documented to produce samples with common-sense errors
attributed to the absence of external knowledge. On the other hand, LLM-based
methods exhibit a limited capacity to capture the disparities between
synthesized and actual data distribution due to the absence of feedback from a
discriminator during training. Furthermore, the decoding of LLM-based
generation introduces gradient breakpoints, impeding the backpropagation of
loss from a discriminator, thereby complicating the integration of these two
approaches. To solve this challenge, we propose using proximal policy
optimization (PPO) to apply GANs, guiding LLMs to enhance the probability
distribution of tabular features. This approach enables the utilization of LLMs
as generators for GANs in synthesizing tabular data. Our experiments
demonstrate that PPO leads to an approximately 4\% improvement in the accuracy
of models trained on synthetically generated data over state-of-the-art across
three real-world datasets.

利用近似策略优化（PPO）引导大语言模型（LLM）来增强表格特征的概率分布，从而使 LLM 成为用于合成表格数据的生成器，在三个真实数据集上通过实验证明，该方法相对于最先进技术提高了约 4％的合成模型准确性。

P-TA：使用近端策略优化增强大型语言模型下的表格数据增强技术

P-TA: Using Proximal Policy Optimization to Enhance Tabular Data  Augmentation via Large Language Models

General purpose Large Language Models (LLM) such as the Generative Pretrained
Transformer (GPT) and Large Language Model Meta AI (LLaMA) have attracted much
attention in recent years. There is strong evidence that these models can
perform remarkably well in various natural language processing tasks. However,
how to leverage them to approach domain-specific use cases and drive value
remains an open question. In this work, we focus on a specific use case,
pharmaceutical manufacturing investigations, and propose that leveraging
historical records of manufacturing incidents and deviations in an organization
can be beneficial for addressing and closing new cases, or de-risking new
manufacturing campaigns. Using a small but diverse dataset of real
manufacturing deviations selected from different product lines, we evaluate and
quantify the power of three general purpose LLMs (GPT-3.5, GPT-4, and Claude-2)
in performing tasks related to the above goal. In particular, (1) the ability
of LLMs in automating the process of extracting specific information such as
root cause of a case from unstructured data, as well as (2) the possibility of
identifying similar or related deviations by performing semantic search on the
database of historical records are examined. While our results point to the
high accuracy of GPT-4 and Claude-2 in the information extraction task, we
discuss cases of complex interplay between the apparent reasoning and
hallucination behavior of LLMs as a risk factor. Furthermore, we show that
semantic search on vector embedding of deviation descriptions can be used to
identify similar records, such as those with a similar type of defect, with a
high level of accuracy. We discuss further improvements to enhance the accuracy
of similar record identification.

利用历史记录，将大型语言模型（LLM）应用于制药生产调查，提取特定信息并进行语义搜索。

基础大型语言模型能辅助进行制药生产调查吗？

Can Foundational Large Language Models Assist with Conducting  Pharmaceuticals Manufacturing Investigations?

We present MatAtlas, a method for consistent text-guided 3D model texturing.
Following recent progress we leverage a large scale text-to-image generation
model (e.g., Stable Diffusion) as a prior to texture a 3D model. We carefully
design an RGB texturing pipeline that leverages a grid pattern diffusion,
driven by depth and edges. By proposing a multi-step texture refinement
process, we significantly improve the quality and 3D consistency of the
texturing output. To further address the problem of baked-in lighting, we move
beyond RGB colors and pursue assigning parametric materials to the assets.
Given the high-quality initial RGB texture, we propose a novel material
retrieval method capitalized on Large Language Models (LLM), enabling
editabiliy and relightability. We evaluate our method on a wide variety of
geometries and show that our method significantly outperform prior arts. We
also analyze the role of each component through a detailed ablation study.

我们提出了 MatAtlas，一种用于一致的文本引导的 3D 模型纹理方法。通过利用大规模的文本生成模型（例如稳定的扩散器）作为纹理 3D 模型的先验知识。我们设计了一种 RGB 纹理流程，利用了基于深度和边缘的网格模式扩散。通过提出多步骤的纹理细化过程，我们显著提高了纹理输出的质量和 3D 一致性。为了进一步解决烘焙光照的问题，我们超越了 RGB 颜色，将参数化材质分配给资源。鉴于高质量的初始 RGB 纹理，我们提出了一种利用大语言模型（LLM）的新颖材质检索方法，实现了可编辑性和重光能力。我们在各种几何形状上评估了我们的方法，并展示了我们的方法明显优于先前的方法。通过详细的消融研究，我们还分析了每个组件的作用。

MatAtlas: 文本驱动的一致性几何纹理和材质分配

MatAtlas: Text-driven Consistent Geometry Texturing and Material  Assignment

Accurately recommending candidate news articles to users is a basic challenge
faced by personalized news recommendation systems. Traditional methods are
usually difficult to grasp the complex semantic information in news texts,
resulting in unsatisfactory recommendation results. Besides, these traditional
methods are more friendly to active users with rich historical behaviors.
However, they can not effectively solve the "long tail problem" of inactive
users. To address these issues, this research presents a novel general
framework that combines Large Language Models (LLM) and Knowledge Graphs (KG)
into semantic representations of traditional methods. In order to improve
semantic understanding in complex news texts, we use LLMs' powerful text
understanding ability to generate news representations containing rich semantic
information. In addition, our method combines the information about news
entities and mines high-order structural information through multiple hops in
KG, thus alleviating the challenge of long tail distribution. Experimental
results demonstrate that compared with various traditional models, the
framework significantly improves the recommendation effect. The successful
integration of LLM and KG in our framework has established a feasible path for
achieving more accurate personalized recommendations in the news field. Our
code is available at this https URL

通过将大型语言模型（LLM）和知识图谱（KG）结合为传统方法的语义表示形式，该研究提出了一个新的通用框架，以准确地推荐候选新闻文章给用户，从而改善了新闻推荐系统中的个性化推荐效果。