在书面独白中训练的语言模型可以学习预测口语对话吗?
Muhammad Umair1, Julia B Mertens2, Lena Warnke2
1Department of Computer Science, Tufts University.
Cognitive science
|November 26, 2024
概括
大型语言模型 (LLM) 可以生成类似人类的文本,但难以预测受说话者身份影响的口语. 这些模型捕捉了语言模式,但没有捕捉到人类自然对话的细微差别.
科学领域:
- 计算语言学 计算语言学
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 基于变压器的大型语言模型 (LLM) 在文本生成方面表现出显著的能力.
- 法律法学模拟口语的能力,特别是在交互式环境中,仍然未被充分探索.
- 口语和书面语言在语法,务实学和对话规范方面表现出不同的特征.
研究的目的:
- 评估大型语言模型 (LLM) 作为口语对话的预测模型.
- 调查LLM是否可以了解说话者的身份会影响对话中的发言可预测性.
- 将LLM预测与自然口语交互中的人类行为数据进行比较.
主要方法:
- 微调GPT-2的两个变体在英语口语对话成绩单上.
- 计算两个回合对话序列的单词惊喜值.
- 将模型生成的惊喜数据与人类关于说话者身份的行为反应进行比较.
主要成果:
- 精心调整的LLM显示,单词的可预测性受到说话者的身份的影响.
- 然而,这些模型并没有复制人类利用语音身份信息来预测口语的方法.
- 士学位的表现表明与人类对话行为有所分歧.
结论:
- 法学士可以从口语对话数据中学习规范性语言结构.
- 当前的法律法学并没有完全捕捉到塑造人类自然对话的实用性和社会因素,例如说话者的身份.
- 需要进一步的研究来增强LLM模拟交互式口语复杂性的能力.
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