Comparison of Text-Based Inputs for Human-in-the-Loop Feedback in Vision–Language Models

Human-in-the-loop methods leverage human feedback to enhance machine learning and artificial intelligence. Manual review of outputs can correct errors, identify model weaknesses, or expand labels to broaden model capabilities. Feedback collection methods range from simple flagging of outputs as correct or incorrect to more complex feature-level adjustments or natural language interpretations. This paper presents a user study evaluating changes in user performance over time and explores the trade-off between feedback quality and human effort. We compare four interactive input methods for reviewing and correcting outcomes in object detection and activity recognition in videos. Our findings indicate that while some complex input methods, such as free-text, require more time, the quality and impact of their feedback on model accuracy often surpass those of simpler methods that require less effort. However, more effort does not always lead to better-quality feedback, especially when aiming to improve the model. Our VLM experiments show that the most accurate models were trained using detailed natural language feedback or precise word-level corrections, while simple yes/no judgments also led to solid performance at a much lower annotation cost.