大型语言模型的思维理论性能:土耳其语和英语的比较分析

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Speech and Language Pub Date : 2024-07-25 DOI:10.1016/j.csl.2024.101698
Burcu Ünlütabak, Onur Bal
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引用次数: 0

摘要

心智理论(ToM),即理解他人的心理状态,是人类的一项决定性技能。评估本地语言学习者心智理论表现的研究得出了相互矛盾的结论,并引发了关于他们是否以及如何表现出心智理论理解能力的讨论。心理学研究表明,特定语言的特点会影响心理状态的表达和交流方式。因此,我们有理由相信,语言特点会影响 LLM 与人类交流的方式,尤其是当对话涉及到心理状态时。本研究通过评估 GPT 3.5 和 4 在英语和土耳其语中的表现,探讨了这些语言特点如何影响本地语言学家的 ToM 表现。土耳其语与英语形成了很好的对比,因为土耳其语具有不同的句法结构和特殊动词 san- 和 zannet-,意为 "虚假地相信"。我们使用 Open AI 的聊天完成 API,收集了 GPT 模型在英语和土耳其语的一阶和二阶 ToM 场景中的反应。我们的创新方法在同一聊天会话中结合了完成提示和开放式问题,从而深入了解了模型的推理过程。我们的数据显示,虽然 GPT 模型可以准确地响应标准 ToM 任务(准确率为 100%),但只要稍加修改,其性能就会下降(低于偶然水平)。这种高敏感性表明 ToM 性能缺乏稳健性。GPT 4 的表现优于其前身 GPT 3.5,在一定程度上提高了 ToM 性能。当任务以英语呈现时,模型的表现普遍优于以土耳其语呈现时。这些发现表明,GPT 模型还不能可靠地通过两种语言中的一阶和二阶 ToM 任务。这些发现对 LLM 的可解释性具有重要意义,因为它们强调了 LLM 在不同语言中模拟类人 ToM 理解时所面临的挑战和偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Theory of mind performance of large language models: A comparative analysis of Turkish and English

Theory of mind (ToM), understanding others’ mental states, is a defining skill belonging to humans. Research assessing LLMs’ ToM performance yields conflicting findings and leads to discussions about whether and how they could show ToM understanding. Psychological research indicates that the characteristics of a specific language can influence how mental states are represented and communicated. Thus, it is reasonable to expect language characteristics to influence how LLMs communicate with humans, especially when the conversation involves references to mental states. This study examines how these characteristics affect LLMs’ ToM performance by evaluating GPT 3.5 and 4 performances in English and Turkish. Turkish provides an excellent contrast to English since Turkish has a different syntactic structure and special verbs, san- and zannet-, meaning “falsely believe.” Using Open AI's Chat Completion API, we collected responses from GPT models for first- and second-order ToM scenarios in English and Turkish. Our innovative approach combined completion prompts and open-ended questions within the same chat session, offering deep insights into models’ reasoning processes. Our data showed that while GPT models can respond accurately to standard ToM tasks (100% accuracy), their performance deteriorates (below chance level) with slight modifications. This high sensitivity suggests a lack of robustness in ToM performance. GPT 4 outperformed its predecessor, GPT 3.5, showing improvement in ToM performance to some extent. The models generally performed better when tasks were presented in English than in Turkish. These findings indicate that GPT models cannot reliably pass first-order and second-order ToM tasks in either of the languages yet. The findings have significant implications for Explainability of LLMs by highlighting challenges and biases that they face when simulating human-like ToM understanding in different languages.

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来源期刊
Computer Speech and Language
Computer Speech and Language 工程技术-计算机:人工智能
CiteScore
11.30
自引率
4.70%
发文量
80
审稿时长
22.9 weeks
期刊介绍: Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.
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