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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.11432 |
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| _version_ | 1866909285557993472 |
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| author | Chen, Kang Lian, Zheng Sun, Haiyang Liu, Rui Yi, Jiangyan Liu, Bin Tao, Jianhua |
| author_facet | Chen, Kang Lian, Zheng Sun, Haiyang Liu, Rui Yi, Jiangyan Liu, Bin Tao, Jianhua |
| contents | Deception detection has attracted increasing attention due to its importance in real-world scenarios. Its main goal is to detect deceptive behaviors from multimodal clues such as gestures, facial expressions, prosody, etc. However, these bases are usually subjective and related to personal habits. Therefore, we extend deception detection to deception reasoning, further providing objective evidence to support subjective judgment. Specifically, we provide potential lies and basic facts and then analyze why this sentence may be a lie by combining factual inconsistencies and intent behind them. Compared with deception detection, this task is more applicable to real-world scenarios. For example, in interrogation, the police should judge whether a person is lying based on solid evidence. This paper presents our initial attempts at this task, including constructing a dataset and defining evaluation metrics. Meanwhile, this task can serve as a benchmark for evaluating the complex reasoning capability of large language models. Our code and data are provided in the supplementary material. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning Chen, Kang Lian, Zheng Sun, Haiyang Liu, Rui Yi, Jiangyan Liu, Bin Tao, Jianhua Computation and Language Deception detection has attracted increasing attention due to its importance in real-world scenarios. Its main goal is to detect deceptive behaviors from multimodal clues such as gestures, facial expressions, prosody, etc. However, these bases are usually subjective and related to personal habits. Therefore, we extend deception detection to deception reasoning, further providing objective evidence to support subjective judgment. Specifically, we provide potential lies and basic facts and then analyze why this sentence may be a lie by combining factual inconsistencies and intent behind them. Compared with deception detection, this task is more applicable to real-world scenarios. For example, in interrogation, the police should judge whether a person is lying based on solid evidence. This paper presents our initial attempts at this task, including constructing a dataset and defining evaluation metrics. Meanwhile, this task can serve as a benchmark for evaluating the complex reasoning capability of large language models. Our code and data are provided in the supplementary material. |
| title | Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.11432 |