Large language models like ChatGPT may introduce limitations and biases when used for annotating test set summaries:
Biases in Training Data: Large language models are trained on vast amounts of text data from the internet, which may contain biases related to gender, race, or other sensitive attributes. These biases can be inadvertently reflected in the generated summaries.
Lack of Contextual Understanding: Language models may lack contextual understanding and may generate summaries that are factually incorrect or misleading, especially in nuanced or complex topics.
Overfitting to Training Data: Models like ChatGPT may overfit to the specific patterns in the training data, leading to limited generalization to unseen data and potentially biased annotations.
To mitigate these limitations and biases, the following approaches can be considered:
Diverse Training Data: Ensure that the language model is trained on diverse and representative datasets to reduce biases and improve generalization.
Bias Detection and Mitigation: Implement bias detection algorithms to identify and mitigate biases in the generated summaries. This can involve post-processing steps to adjust the output based on identified biases.
Human Oversight: Incorporate human oversight and validation to review the model-generated summaries for accuracy, fairness, and ethical considerations. Human annotators can provide valuable feedback and corrections.
Regular Evaluation: Continuously evaluate the performance of the language model on a diverse set of test cases to identify and address any biases or limitations that may arise.
By implementing these strategies, the potential limitations and biases associated with using large language models for annotating test set summaries can be effectively mitigated.