Navigating the vast and rapidly increasing sea of academic publications to identify institutional synergies, benchmark research contributions and pinpoint key research contributions has become an increasingly daunting task, especially with the current exponential increase in new publications. Existing tools provide useful overviews or single-document insights, but none supports structured, qualitative comparisons across institutions or publications. To address this, we demonstrate Compare, a novel framework that tackles this challenge by enabling sophisticated long-context comparisons of scientific contributions. Compare empowers users to explore and analyze research overlaps and differences at both the institutional and publication granularity, all driven by user-defined questions and automatic retrieval over online resources. For this we leverage on Retrieval-Augmented Generation over evolving data sources to foster long context knowledge synthesis. Unlike traditional scientometric tools, Compare goes beyond quantitative indicators by providing qualitative, citation-supported comparisons.

Citation classification aims to identify the purpose of the cited article in the citing article. Previous citation classification methods rely largely on supervised approaches. The models are trained on datasets with citing sentences or citation contexts annotated for a citation's purpose or function or intent. Recent advancements in Large Language Models (LLMs) have dramatically improved the ability of NLP systems to achieve state-of-the-art performances under zero or few-shot settings. This makes LLMs particularly suitable for tasks where sufficiently large labelled datasets are not yet available, which remains to be the case for citation classification. This paper systematically investigates the effectiveness of different prompting strategies for citation classification and compares them to promptless strategies as a baseline. Specifically, we evaluate the following four strategies, two of which we introduce for the first time, which involve updating Language Model (LM) parameters while training the model: (1) Promptless fine-tuning, (2) Fixed-prompt LM tuning, (3) Dynamic Context-prompt LM tuning (proposed), (4) Prompt + LM fine-tuning (proposed). Additionally, we test the zero-shot performance of LLMs, GPT3.5, a (5) Tuning-free prompting strategy that involves no parameter updating. Our results show that prompting methods based on LM parameter updating significantly improve citation classification performances on both domain-specific and multi-disciplinary citation classifications. Moreover, our Dynamic Context-prompting method achieves top scores both for the ACL-ARC and ACT2 citation classification datasets, surpassing the highest-performing system in the 3C shared task benchmark. Interestingly, we observe zero-shot GPT3.5 to perform well on ACT2 but poorly on the ACL-ARC dataset.