👉 The review fluid, also known as review noise or review drift, refers to the phenomenon where a model's predictions can be influenced by the presence of review text in its input, even when the review itself is not directly relevant to the task at hand. This can occur because models, especially those based on transformer architectures like BERT, learn to associate certain patterns or structures in text with specific outcomes. As a result, the model may incorporate or amplify these patterns from review text when generating its predictions, leading to biased or suboptimal performance. This is particularly problematic in tasks like sentiment analysis, where the review's content is not directly indicative of the task's objective but still impacts the model's output. Understanding and mitigating review fluid is crucial for improving the robustness and reliability of text-based AI systems.
