The current LLM-assisted data generation pipeline may have the following limitations:
Hallucination: LLMs may generate unrealistic or incorrect information, leading to data with inaccuracies or inconsistencies.
Limited Diversity: The generated data may lack diversity in terms of scenes, objects, and interactions, limiting the model's exposure to various scenarios.
Quality Control: The pipeline may struggle with ensuring the quality of the generated data, leading to issues like grammatical errors, irrelevant information, or incomplete descriptions.
To improve the pipeline and ensure higher-quality and more diverse 3D VL data, the following steps can be taken:
Refinement Procedures: Implement robust refinement procedures to filter out incorrect or irrelevant data generated by LLMs. This can involve human validation, automated checks, and data cleaning techniques.
Data Augmentation: Introduce data augmentation techniques to enhance the diversity of the generated data. This can include introducing new objects, scenes, and interactions to enrich the dataset.
Feedback Loop: Establish a feedback loop where the model's performance on the generated data is used to iteratively improve the data generation process. This can help in identifying and addressing any recurring issues or patterns in the generated data.
Human-in-the-Loop: Incorporate human validation and oversight in the data generation process to ensure the accuracy and relevance of the generated data. Human annotators can provide valuable insights and corrections to enhance the quality of the dataset.