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arXiv NLP research summaries for May 14, 2024.
Today's Research Themes (AI-Generated):
• Exploring a novel model for joint extraction of entities and relations with enhanced information interaction in NLP.
• Investigating adversarial robustness and countermeasures of multimodal speech-language models.
• Introducing Seal-Tools, a self-instruct learning dataset for agent tuning and benchmarking in language models.
• Addressing error correction in clinical text using ensembles of large language models and error categorization.
• Proposing stylometric watermarks to distinguish between human and large language model-generated texts.
arXiv NLP research summaries for May 14, 2024.
Today's Research Themes (AI-Generated):
• Exploring a novel model for joint extraction of entities and relations with enhanced information interaction in NLP.
• Investigating adversarial robustness and countermeasures of multimodal speech-language models.
• Introducing Seal-Tools, a self-instruct learning dataset for agent tuning and benchmarking in language models.
• Addressing error correction in clinical text using ensembles of large language models and error categorization.
• Proposing stylometric watermarks to distinguish between human and large language model-generated texts.