The CoF (Coarse to Fine) pipeline, while effective in constructing LQAC datasets, has several potential limitations. One significant drawback is the reliance on the quality of the initial long-context documents. If the source material contains inaccuracies or is poorly structured, the resulting QA instances may inherit these flaws, leading to misleading citations and responses.
To address this, a rigorous pre-processing step could be introduced to evaluate and filter the quality of the source documents before they are used in the CoF pipeline. Implementing automated quality checks or human-in-the-loop validation could ensure that only high-quality texts are utilized.
Another limitation is the potential for overfitting during the fine-tuning of models on the constructed datasets. If the models become too specialized on the specific instances generated by CoF, they may struggle to generalize to unseen contexts. To mitigate this, it would be beneficial to incorporate a diverse range of training examples from various domains and contexts, ensuring that the models maintain their adaptability.
Additionally, the filtering stage that discards instances with insufficient citations may inadvertently remove valuable data. A more nuanced filtering criterion could be developed to retain instances that, while having fewer citations, still provide meaningful context and insights.