IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
Towards Detecting Harmful Agendas in News Articles
4 Pith papers cite this work. Polarity classification is still indexing.
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LLMs outperform humans in expressing illocutionary intents and sycophancy in successful persuasive counter-arguments from ChangeMyView, with crowd workers preferring LLM versions.
Enhanced ABV framework applied to French AES, comparing 8 models on 27k and 961-essay corpora to assess generalizability, agreement, and validity.
A survey that compiles and taxonomizes more than 32 existing hallucination mitigation techniques for LLMs while analyzing their challenges and limitations.
citing papers explorer
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IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
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"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
LLMs outperform humans in expressing illocutionary intents and sycophancy in successful persuasive counter-arguments from ChangeMyView, with crowd workers preferring LLM versions.
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Automated Essay Scoring and Language Certification: Assessing Generalizability, Agreement and Validity for French
Enhanced ABV framework applied to French AES, comparing 8 models on 27k and 961-essay corpora to assess generalizability, agreement, and validity.
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A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
A survey that compiles and taxonomizes more than 32 existing hallucination mitigation techniques for LLMs while analyzing their challenges and limitations.