To extend the PROX framework for more complex data refinement tasks, such as detecting and removing factual errors or biases in training data, several strategies can be employed. First, integrating advanced natural language processing (NLP) techniques, such as fact-checking models or bias detection algorithms, could enhance the framework's ability to identify inaccuracies or biased content. This could involve training specialized models that focus on recognizing factual inconsistencies or biased language patterns within the data.
Second, the PROX framework could incorporate a multi-step refinement process where initial data is screened for factual accuracy and bias before applying the existing document-level and chunk-level operations. This could be achieved by developing a pre-processing module that utilizes external knowledge bases or databases to verify the factual correctness of statements within the training data.
Additionally, leveraging user feedback or crowdsourced evaluations could provide a mechanism for continuous improvement. By allowing users to flag inaccuracies or biases, the system could learn from these inputs and refine its data selection and refinement processes over time. This iterative approach would not only enhance the quality of the training data but also ensure that the models trained on this data are more reliable and less prone to perpetuating biases.