提出首个嵌入式谎言数据集，用大模型识别真假混杂的谎言。
When lies are mostly truthful: automated verbal deception detection for embedded lies
- 构建2088条含嵌入谎言的语句数据集，真实还原说谎场景。
- 大模型在真假混杂语句上仅达64%分类准确率，难分真假。
- 适合研究心理、语言分析与反欺骗技术的学者参考。
背景：传统言语欺骗检测依赖完整真或假陈述，但现实中的谎言常夹杂在真实叙述中。本文首次构建了包含2,088条真/假陈述的数据集，其中每条陈述均包含被标注的嵌入式谎言。采用被试内设计，参与者先描述个人经历的真实陈述，再改写为含嵌入谎言的虚假陈述，并标注谎言的中心性、欺骗程度和来源。结果显示，微调后的Llama-3-8B语言模型在区分真实陈述与含嵌入谎言的陈述时，准确率为64%。个体差异、语言特征与可解释性分析表明，嵌入式谎言之所以难以识别，是因为其与真实陈述高度相似。典型虚假陈述中约2/3内容为真实信息，1/3为嵌入谎言，且多源自过往亲身经历，与真实版本的语言差异极小。本研究提供该数据集作为新资源，推动嵌入式谎言检测的研究发展。
原文摘要 · Abstract (English)
Background: Verbal deception detection research relies on narratives and commonly assumes statements as truthful or deceptive. A more realistic perspective acknowledges that the veracity of statements exists on a continuum with truthful and deceptive parts being embedded within the same statement. However, research on embedded lies has been lagging behind. Methods: We collected a novel dataset of 2,088 truthful and deceptive statements with annotated embedded lies. Using a within-subjects design, participants provided a truthful account of an autobiographical event. They then rewrote their statement in a deceptive manner by including embedded lies, which they highlighted afterwards and judged on lie centrality, deceptiveness, and source. Results: We show that a fined-tuned language model (Llama-3-8B) can classify truthful statements and those containing embedded lies with 64% accuracy. Individual differences, linguistic properties and explainability analysis suggest that the challenge of moving the dial towards embedded lies stems from their resemblance to truthful statements. Typical deceptive statements consisted of 2/3 truthful information and 1/3 embedded lies, largely derived from past personal experiences and with minimal linguistic differences with their truthful counterparts. Conclusion: We present this dataset as a novel resource to address this challenge and foster research on embedded lies in verbal deception detection.
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