To enhance sentence-level citation generation for long-context question answering (LQAC), several strategies can be implemented. First, integrating advanced natural language processing techniques, such as anaphora resolution, can help clarify references within the text, ensuring that citations accurately point to the intended supporting evidence. This would reduce ambiguity in citations, making them more precise.
Second, employing a more sophisticated retrieval mechanism that utilizes semantic similarity measures could improve the selection of relevant sentences. By leveraging embeddings or transformer-based models to assess the contextual relevance of sentences, the system can better identify and cite the most pertinent information.
Third, incorporating user feedback loops into the citation generation process can facilitate continuous improvement. By analyzing user interactions and preferences regarding citation quality, the model can adapt and refine its citation strategies over time, leading to more comprehensive and user-friendly outputs.
Lastly, expanding the training dataset to include a wider variety of contexts and citation styles can enhance the model's ability to generate citations that are not only accurate but also tailored to different domains and user needs. This would ensure that the citation generation process is robust and versatile across various applications.