A Framework for Real-time Safeguarding the Text Generation of Large Language

arXiv:2404.19048v1 Announce Type: new
Abstract: Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmful content. To address this, various approaches have been developed to safeguard LLMs from producing unsafe content. However, existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, that lead to quality degradation and increased computational overhead. To mitigate those limitations, we …

abstract advanced arxiv cs.ai cs.cl ethical framework generate however language language models language processing large language large language models llms natural natural language natural language processing nlp processing real-time risks tasks text text generation type