Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly
impacted various fields by enabling high-quality synthetic data generation and
reducing dependence on expensive human-generated datasets. Despite this,
challenges remain in the areas of generalization, controllability, diversity,
and truthfulness within the existing generative frameworks. To address these
challenges, this paper presents UniGen, a comprehensive LLM-powered framework
designed to produce diverse, accurate, and highly controllable datasets. UniGen
is adaptable, supporting all types of text datasets and enhancing the
generative process through innovative mechanisms. To augment data diversity,
UniGen incorporates an attribute-guided generation module and a group checking
feature. For accuracy, it employs a code-based mathematical assessment for
label verification alongside a retrieval-augmented generation technique for
factual validation. The framework also allows for user-specified constraints,
enabling customization of the data generation process to suit particular
requirements. Extensive experiments demonstrate the superior quality of data
generated by UniGen, and each module within UniGen plays a critical role in
this enhancement. Additionally, UniGen is applied in two practical scenarios:
benchmarking LLMs and data augmentation. The results indicate that UniGen
effectively supports dynamic and evolving benchmarking, and that data
augmentation improves LLM capabilities in various domains, including
agent-oriented abilities and reasoning skills.

UniGen 是一个综合的 LLM 框架，利用创新机制解决现有生成框架中的泛化、可控性、多样性和准确性等挑战，通过引入属性导向的生成模块和检查功能，以及基于代码的数学评估和检索增强生成技术，产生多样、准确和高度可控的数据，并支持用户指定的约束条件，满足特定需求。相比其他生成方法，UniGen 生成的数据质量更高，适用于动态演变的基准测试和数据增强，提升了 LLM 在代理能力和推理技能等领域的能力。

UniGen：使用大型语言模型进行文本数据集生成的统一框架

UniGen: A Unified Framework for Textual Dataset Generation Using Large  Language Models

This paper introduces UniGen, a novel approach to generating new traffic
scenarios for evaluating and improving autonomous driving software through
simulation. Our approach models all driving scenario elements in a unified
model: the position of new agents, their initial state, and their future motion
trajectories. By predicting the distributions of all these variables from a
shared global scenario embedding, we ensure that the final generated scenario
is fully conditioned on all available context in the existing scene. Our
unified modeling approach, combined with autoregressive agent injection,
conditions the placement and motion trajectory of every new agent on all
existing agents and their trajectories, leading to realistic scenarios with low
collision rates. Our experimental results show that UniGen outperforms prior
state of the art on the Waymo Open Motion Dataset.

UniGen 是一种用于通过模拟生成新交通场景以评估和改进自动驾驶软件的新方法，通过统一建模来模拟所有驾驶场景元素，包括新代理的位置、初始状态和未来运动轨迹，以实现对现有场景中所有上下文的完全条件。该方法结合自回归代理注入，使每个新代理的位置和运动轨迹都依赖于所有已有代理及其轨迹，从而产生具有低碰撞率的逼真场景。在 Waymo Open Motion 数据集上的实验证明 UniGen 优于现有前沿技术。

UniGen：用于生成自动驾驶场景的初始代理状态和轨迹的统一建模

UniGen: Unified Modeling of Initial Agent States and Trajectories for  Generating Autonomous Driving Scenarios

Generative information retrieval, encompassing two major tasks of Generative
Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained
significant attention in the area of information retrieval and natural language
processing. Existing methods for GDR and GAR rely on separate retrieval and
reader modules, which hinder simultaneous optimization. To overcome this, we
present \textbf{UniGen}, a \textbf{Uni}fied \textbf{Gen}erative framework for
retrieval and question answering that integrates both tasks into a single
generative model leveraging the capabilities of large language models. UniGen
employs a shared encoder and two distinct decoders for generative retrieval and
question answering. To facilitate the learning of both tasks, we introduce
connectors, generated by large language models, to bridge the gaps between
query inputs and generation targets, as well as between document identifiers
and answers. Furthermore, we propose an iterative enhancement strategy that
leverages generated answers and retrieved documents to iteratively improve both
tasks. Through extensive experiments on the MS MARCO and NQ datasets, we
demonstrate the effectiveness of UniGen, showcasing its superior performance in
both the retrieval and the question answering tasks.

UniGen 是一种集成了生成式检索和问答功能的统一生成模型，通过使用大型语言模型，UniGen 采用一个共享编码器和两个不同的解码器，引入连接器来弥补查询输入和生成目标、文档标识符和答案之间的差距，并提出了一种迭代增强策略来通过生成的答案和检索的文档迭代地改进两个任务，在 MS MARCO 和 NQ 数据集上的广泛实验证明了 UniGen 在检索和问答任务中具有优越的性能。