One hallmark of human language is its combinatoriality -- reusing a
relatively small inventory of building blocks to create a far larger inventory
of increasingly complex structures. In this paper, we explore the idea that
combinatoriality in language reflects a human inductive bias toward
representational efficiency in symbol systems. We develop a computational
framework for discovering structure in a writing system. Built on top of
state-of-the-art library learning and program synthesis techniques, our
computational framework discovers known linguistic structures in the Chinese
writing system and reveals how the system evolves towards simplification under
pressures for representational efficiency. We demonstrate how a library
learning approach, utilizing learned abstractions and compression, may help
reveal the fundamental computational principles that underlie the creation of
combinatorial structures in human cognition, and offer broader insights into
the evolution of efficient communication systems.

人类语言的一个特点是组合性 —— 通过重复使用相对较小的基本单元，创建出较为庞大、越发复杂的结构。本文探索了语言中组合性反映了人类对符号系统中表现效率的归纳偏见这一观点。我们使用先进的库学习和程序合成技术，开发了一个计算框架来发现一种写作系统中的结构。计算框架在中国的写作系统中发现了已知的语言结构，并揭示了系统在表现效率的压力下向简化方向演化的过程。我们演示了如何通过利用学到的抽象和压缩的图书馆学习方法，揭示出构成人类认知中组合结构创造的基本计算原则，并对有效沟通系统的演化提供更广泛的见解。

利用图书馆学习找出表意文字书写中的结构

Finding structure in logographic writing with library learning

We study how to apply large language models to write grounded and organized
long-form articles from scratch, with comparable breadth and depth to Wikipedia
pages. This underexplored problem poses new challenges at the pre-writing
stage, including how to research the topic and prepare an outline prior to
writing. We propose STORM, a writing system for the Synthesis of Topic Outlines
through Retrieval and Multi-perspective Question Asking. STORM models the
pre-writing stage by (1) discovering diverse perspectives in researching the
given topic, (2) simulating conversations where writers carrying different
perspectives pose questions to a topic expert grounded on trusted Internet
sources, (3) curating the collected information to create an outline.
For evaluation, we curate FreshWiki, a dataset of recent high-quality
Wikipedia articles, and formulate outline assessments to evaluate the
pre-writing stage. We further gather feedback from experienced Wikipedia
editors. Compared to articles generated by an outline-driven
retrieval-augmented baseline, more of STORM's articles are deemed to be
organized (by a 25% absolute increase) and broad in coverage (by 10%). The
expert feedback also helps identify new challenges for generating grounded long
articles, such as source bias transfer and over-association of unrelated facts.

应用大型语言模型写作系统在预写阶段，通过主题概述的综合来检索和多维度提问以生成有深度和广度与维基百科页面相当的长篇文章，评估结果显示，STORM 的文章在组织性和涵盖范围方面相较于基线模型更为出色。

用大型语言模型从零开始编写类似维基百科的文章

Assisting in Writing Wikipedia-like Articles From Scratch with Large  Language Models

We explore the use of segments learnt using Byte Pair Encoding (referred to
as BPE units) as basic units for statistical machine translation between
related languages and compare it with orthographic syllables, which are
currently the best performing basic units for this translation task. BPE
identifies the most frequent character sequences as basic units, while
orthographic syllables are linguistically motivated pseudo-syllables. We show
that BPE units modestly outperform orthographic syllables as units of
translation, showing up to 11% increase in BLEU score. While orthographic
syllables can be used only for languages whose writing systems use vowel
representations, BPE is writing system independent and we show that BPE
outperforms other units for non-vowel writing systems too. Our results are
supported by extensive experimentation spanning multiple language families and
writing systems.

本文研究了使用字节对编码（称为 BPE units）以及拼音音节作为翻译任务基本单元的效果， 结果表明，BPE units 作为翻译单元略优于拼音音节单元， 并且更适用于非元音书写系统的语言。