REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models

arXiv:2404.11782v1 Announce Type: cross
Abstract: The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Addressing these challenges are necessary before using LLMs for applications with societal impact. Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable …

abstract aggregation application arxiv beyond biases concerns cs.ai cs.cl cs.cy cs.lg data domains equity importance language language models language processing large language large language models llms natural natural language natural language processing nature processing raises reliability responsibility stereotypes through type