Biases and Limitations:
Selection Bias: The choice of in-context examples may inadvertently introduce biases based on the dataset used for training the model, leading to overfitting on specific types of information.
Ordering Bias: The order in which in-context examples are presented can influence the model's response, potentially favoring certain types of reasoning patterns over others.
Factual Accuracy: Inclusion of incorrect or misleading in-context examples can lead to the model learning incorrect associations and producing inaccurate results.
Mitigation Strategies:
Diverse Dataset: Use a diverse dataset for selecting in-context examples to ensure a broad representation of scenarios and reduce dataset-specific biases.
Randomization: Randomize the selection and ordering of in-context examples during training and evaluation to minimize the impact of ordering bias and ensure robust performance.
Validation: Validate the accuracy and relevance of in-context examples to ensure that they align with the intended reasoning tasks and do not introduce misleading information.
Adversarial Testing: Incorporate adversarial examples during training to challenge the model's reasoning capabilities and improve its resilience to biases and limitations in the dataset.
By implementing these mitigation strategies, the biases and limitations introduced by the selection and formulation of in-context examples can be addressed, enhancing the reliability and generalizability of the latent knowledge estimation process.