Despite significant advancements in natural language processing (NLP), current AI models still face several limitations in fully understanding natural language context:
AI models often struggle with common sense reasoning, leading to misunderstandings or inappropriate responses in conversations that require contextual awareness beyond the provided text.
Words with multiple meanings (polysemy) and ambiguous sentences pose challenges for AI models, as they may not correctly infer the intended meaning without additional context.
Maintaining context over long conversations or understanding dependencies across multiple sentences is difficult for current models, resulting in potential loss of coherence and relevance in dialogue.
AI models can inherit and amplify biases present in the training data, leading to unfair or discriminatory outputs. Addressing these biases remains a significant challenge in NLP.
To learn more about the limitations and advancements in NLP, visit these links: