In this paper, we present CharacterGLM, a series of models built upon
ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM
is designed for generating Character-based Dialogues (CharacterDial), which
aims to equip a conversational AI system with character customization for
satisfying people's inherent social desires and emotional needs. On top of
CharacterGLM, we can customize various AI characters or social agents by
configuring their attributes (identities, interests, viewpoints, experiences,
achievements, social relationships, etc.) and behaviors (linguistic features,
emotional expressions, interaction patterns, etc.). Our model outperforms most
mainstream close-source large langauge models, including the GPT series,
especially in terms of consistency, human-likeness, and engagement according to
manual evaluations. We will release our 6B version of CharacterGLM and a subset
of training data to facilitate further research development in the direction of
character-based dialogue generation.

我们介绍了一系列基于 ChatGLM 的模型，名为 CharacterGLM，模型包含从 6B 到 66B 个参数。我们的 CharacterGLM 旨在生成基于角色的对话（CharacterDial），以满足人们固有的社交需求和情感需求。通过配置他们的属性和行为，我们可以自定义各种 AI 角色或社交代理。根据人工评估，我们的模型在一致性、人类相似度和互动方面优于大多数主流的封闭源大型语言模型，包括 GPT 系列。我们将发布我们的 6B 版本的 CharacterGLM 和一部分训练数据，以促进基于角色的对话生成方向的进一步研究发展。

CharacterGLM：使用大型语言模型定制中文对话人物

CharacterGLM: Customizing Chinese Conversational AI Characters with  Large Language Models

The advances in language-based Artificial Intelligence (AI) technologies
applied to build educational applications can present AI for social-good
opportunities with a broader positive impact. Across many disciplines,
enhancing the quality of mathematics education is crucial in building critical
thinking and problem-solving skills at younger ages. Conversational AI systems
have started maturing to a point where they could play a significant role in
helping students learn fundamental math concepts. This work presents a
task-oriented Spoken Dialogue System (SDS) built to support play-based learning
of basic math concepts for early childhood education. The system has been
evaluated via real-world deployments at school while the students are
practicing early math concepts with multimodal interactions. We discuss our
efforts to improve the SDS pipeline built for math learning, for which we
explore utilizing MathBERT representations for potential enhancement to the
Natural Language Understanding (NLU) module. We perform an end-to-end
evaluation using real-world deployment outputs from the Automatic Speech
Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to
understand how error propagation affects the overall performance in real-world
scenarios.

本研究采用 Spoken Dialogue System 通过多模式互动为幼儿教育提供基础数学概念的学习，使用 MathBERT 来增强自然语言理解模块，对于自动语音识别（ASR）、意图识别和对话管理器（DM）模块的实际部署输出进行端到端评估，以了解错误传播如何影响实际场景的综合表现。