In the dynamic landscape of generative NLP, traditional text processing
pipelines limit research flexibility and reproducibility, as they are tailored
to specific dataset, task, and model combinations. The escalating complexity,
involving system prompts, model-specific formats, instructions, and more, calls
for a shift to a structured, modular, and customizable solution. Addressing
this need, we present Unitxt, an innovative library for customizable textual
data preparation and evaluation tailored to generative language models. Unitxt
natively integrates with common libraries like HuggingFace and LM-eval-harness
and deconstructs processing flows into modular components, enabling easy
customization and sharing between practitioners. These components encompass
model-specific formats, task prompts, and many other comprehensive dataset
processing definitions. The Unitxt-Catalog centralizes these components,
fostering collaboration and exploration in modern textual data workflows.
Beyond being a tool, Unitxt is a community-driven platform, empowering users to
build, share, and advance their pipelines collaboratively. Join the Unitxt
community at this https URL

Unitxt 是一个创新的库，用于可定制的文本数据准备和评估，适用于生成式语言模型。它通过模块化组件将处理流程解构，实现易定制和共享，促进现代文本数据工作流中的合作和探索。

Unitxt: 用于生成式人工智能的灵活、可共享和可复用的数据准备与评估

Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation  for Generative AI

In this position paper, we propose a new approach to generating a type of
knowledge base (KB) from text, based on question generation and entity linking.
We argue that the proposed type of KB has many of the key advantages of a
traditional symbolic KB: in particular, it consists of small modular
components, which can be combined compositionally to answer complex queries,
including relational queries and queries involving "multi-hop" inferences.
However, unlike a traditional KB, this information store is well-aligned with
common user information needs.

本文提出了一种新的基于问题生成和实体链接的知识库生成方法，该知识库由小的模块化组件组成，支持关系查询和多跳推理，与用户信息需求相匹配。

QA 是新的 KR：将问题 - 答案对作为知识库

QA Is the New KR: Question-Answer Pairs as Knowledge Bases

In this paper, we address referring expression comprehension: localizing an
image region described by a natural language expression. While most recent work
treats expressions as a single unit, we propose to decompose them into three
modular components related to subject appearance, location, and relationship to
other objects. This allows us to flexibly adapt to expressions containing
different types of information in an end-to-end framework. In our model, which
we call the Modular Attention Network (MAttNet), two types of attention are
utilized: language-based attention that learns the module weights as well as
the word/phrase attention that each module should focus on; and visual
attention that allows the subject and relationship modules to focus on relevant
image components. Module weights combine scores from all three modules
dynamically to output an overall score. Experiments show that MAttNet
outperforms previous state-of-art methods by a large margin on both
bounding-box-level and pixel-level comprehension tasks. Demo and code are
provided.

本文提出了一种通过使用模块化组件和多种注意力机制，实现对自然语言描述的图像区域定位的方法，该方法在特征抽象、指向性和篮球场景等任务中都优于以往最先进的模型。