Yes, the RAG-Novelty approach could be significantly enhanced by incorporating additional contextual information such as citation networks and peer review comments.
Citation Networks: Integrating citation data can provide insights into how a paper is situated within the broader academic discourse. By analyzing citation patterns, LLMs can better understand the influence and relevance of a paper relative to its predecessors and contemporaries. This could involve retrieving papers that cite the query paper or are cited by it, allowing the model to assess novelty based on the evolution of ideas and methodologies over time.
Peer Review Comments: Incorporating feedback from peer reviews can offer valuable qualitative insights into the perceived novelty and contributions of a paper. By analyzing common themes in peer review comments, LLMs can gain a deeper understanding of what aspects of a paper are considered novel or lacking. This could be achieved by training the model on a dataset that includes anonymized peer review comments alongside the papers themselves.
Contextual Metadata: Additional metadata such as the number of citations a paper has received since publication, the time since publication, and the number of related works published in the same timeframe could also be integrated. This would allow LLMs to assess novelty not just in isolation but in relation to the current research landscape.
Temporal Context: Incorporating temporal data, such as trends in research topics over time, could help LLMs understand shifts in novelty perception. For instance, a paper that introduces a concept that was previously overlooked may be deemed more novel if it aligns with emerging trends.
By extending the RAG-Novelty approach to include these additional contextual elements, researchers can enhance the accuracy and robustness of LLM-based novelty assessments, leading to more informed evaluations of scholarly contributions.