Soroush Vosoughi, Yuxin Wang, Saeed Hassanpour, Ivory Yang
We lifted 9 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| audreycs/MentalManip | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| convert_tech_to_list | Ran | audreycs/MentalManip/experiments/technique_vulnerability/finetune.py pointer only (licence: NOASSERTION) · get_code("cae4bfffde214b2a") |
| convert_vul_to_list | Ran | audreycs/MentalManip/experiments/technique_vulnerability/finetune.py pointer only (licence: NOASSERTION) · get_code("0a948f95f3548d9f") |
| get_stats | Ran | audreycs/MentalManip/statistic_analysis/mentalManip_stats.py pointer only (licence: NOASSERTION) · get_code("2fa574d009bd82d7") |
| preprocessing | Ran | audreycs/MentalManip/statistic_analysis/mentalManip_stats.py pointer only (licence: NOASSERTION) · get_code("81944bfd8e89a6b9") |
| read_file | Ran | audreycs/MentalManip/statistic_analysis/statistics_comparison.py pointer only (licence: NOASSERTION) · get_code("efea318b5b12f44f") |
| select_examples | Ran | audreycs/MentalManip/experiments/manipulation_detection/fewshot_prompt.py pointer only (licence: NOASSERTION) · get_code("b22fc8910566d3e9") |
| turn_num | Ran | audreycs/MentalManip/statistic_analysis/statistics_comparison.py pointer only (licence: NOASSERTION) · get_code("e7eee6af055814ca") |
| sentiment_analysis | Not yet run | audreycs/MentalManip/statistic_analysis/mentalManip_stats.py pointer only (licence: NOASSERTION) · get_code("4095f01be0bb0771") |
| token_size | Not yet run | audreycs/MentalManip/statistic_analysis/statistics_comparison.py pointer only (licence: NOASSERTION) · get_code("2eb0b88e5d660403") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Mental manipulation, a significant form of abuse in interpersonal conversations, presents a challenge to identify due to its contextdependent and often subtle nature. The detection of manipulative language is essential for protecting potential victims, yet the field of Natural Language Processing (NLP) currently faces a scarcity of resources and research on this topic. Our study addresses this gap by introducing a new dataset, named MENTALMANIP, which consists of 4, 000 annotated fictional dialogues. This dataset enables a comprehensive analysis of mental manipulation, pinpointing both the techniques utilized for manipulation and the vulnerabilities targeted in victims. Our research further explores the effectiveness of leading-edge models in recognizing manipulative dialogue and its components through a series of experiments with various configurations. The results demonstrate that these models inadequately identify and categorize manipulative content. Attempts to improve their performance by fine-tuning with existing datasets on mental health and toxicity have not overcome these limitations. We anticipate that MENTALMANIP will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.16584")
get_code_for_paper("2405.16584")
have("2405.16584")
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