Using synthetic data for benchmarking hallucinations may introduce certain limitations that need to be addressed to improve the effectiveness of the OpenCHAIR benchmark in reflecting real-world hallucination patterns. Some potential limitations of using synthetic data include:
Generalization to real-world data: Synthetic data may not fully capture the complexity and variability of real-world images, leading to discrepancies in hallucination patterns between synthetic and real data.
Limited diversity: Synthetic data generation techniques may not encompass the full range of visual scenarios and objects present in real-world images, potentially limiting the benchmark's coverage of hallucination patterns.
Biases in data generation: Synthetic data generation processes may inadvertently introduce biases or artifacts that do not reflect the true distribution of visual content, impacting the benchmark's validity.
To address these limitations and improve the OpenCHAIR benchmark, researchers can consider the following strategies:
Augmentation with real-world data: Incorporating real-world images and captions into the benchmark alongside synthetic data can enhance its diversity and alignment with real-world hallucination patterns.
Adversarial training: Introducing adversarial examples during data generation can help simulate challenging scenarios and improve the benchmark's robustness to hallucinations.
Human evaluation: Conducting human evaluations on both synthetic and real data can provide valuable insights into the effectiveness of the benchmark in capturing hallucination patterns and guide further improvements.
By iteratively refining the benchmark through a combination of synthetic and real data, researchers can create a more comprehensive and representative evaluation framework for hallucination mitigation in vision-language tasks.