Motivation: Pretrained language models struggle to reason about language in social settings because they treat words as detached from the people who use them. When speaker identity and relationships are ignored, interpretation becomes shallow. This limitation-often described as social-blindness\u2014creates particular problems for deception detection, where meaning and intent depend strongly on hierarchy and power. In this work, Power-aware Embedding (PAE), a model to reduce this blindness by embedding social context into representations, has been proposed. In PAE, each participant\u2019s communication history is used to learn a distinctive speaker identity vector that reflects consistent behavioral traits such as trustworthiness or manipulative tendency. During inference, this identity signal is adjusted by the relational power difference between speakers, allowing the model to interpret dialogue as a socially grounded exchange rather than an isolated text. To evaluate the generalization of these representations, a Synthetic Learning setup is used\u2013training has been performed on a moderately imbalanced synthetic corpus, and testing is carried out on a real-world dataset. On the Peskov Diplomacy corpus, the proposed model reaches an F1-score of 0.59 and consistently exceeds baseline systems. The results indicate that incorporating social and power cues enhances the context-awareness and reliability of language models.<\/p>\n","protected":false},"excerpt":{"rendered":"
Motivation: Pretrained language models struggle to reason about language in social settings because they treat words as detached from the people who use them. When speaker identity and relationships are ignored, interpretation becomes shallow. This limitation-often described as social-blindness\u2014creates particular problems for deception detection, where meaning and intent depend strongly on hierarchy and power. 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