Semantic Variation

Semantic variation, the study of how word and sentence meaning changes across contexts, is a crucial area of research in natural language processing (NLP). Current efforts focus on understanding and mitigating the impact of this variation on large language models (LLMs) performance, particularly in applications like clinical NLP and knowledge graph completion, using techniques like self-consistency checks and analysis of contextualized word embeddings. This research is vital for improving the reliability and robustness of LLMs, addressing issues like hallucinations and ensuring fair and accurate outputs across diverse linguistic inputs, ultimately leading to more effective and trustworthy AI systems.