Citing pertinent literature is pivotal to writing and reviewing a scientific
document. Existing techniques mainly focus on the local context or the global
context for recommending citations but fail to consider the actual human
citation behaviour. We propose SymTax, a three-stage recommendation
architecture that considers both the local and the global context, and
additionally the taxonomical representations of query-candidate tuples and the
Symbiosis prevailing amongst them. SymTax learns to embed the infused
taxonomies in the hyperbolic space and uses hyperbolic separation as a latent
feature to compute query-candidate similarity. We build a novel and large
dataset ArSyTa containing 8.27 million citation contexts and describe the
creation process in detail. We conduct extensive experiments and ablation
studies to demonstrate the effectiveness and design choice of each module in
our framework. Also, combinatorial analysis from our experiments shed light on
the choice of language models (LMs) and fusion embedding, and the inclusion of
section heading as a signal. Our proposed module that captures the symbiotic
relationship solely leads to performance gains of 26.66% and 39.25% in Recall@5
w.r.t. SOTA on ACL-200 and RefSeer datasets, respectively. The complete
framework yields a gain of 22.56% in Recall@5 wrt SOTA on our proposed dataset.
The code and dataset are available at this https URL

SymTax 是一个考虑局部和全局上下文以及查询 - 候选元组的分类表示和它们之间的共生关系的三阶段推荐架构，其将融入的分类表示嵌入超伽马空间，并使用超伽马分离作为潜在特征来计算查询 - 候选相似性。

SymTax: 合作关系和分类融合以实现有效的引用推荐

SymTax: Symbiotic Relationship and Taxonomy Fusion for Effective  Citation Recommendation

The fields of both Natural Language Processing (NLP) and Automated Machine
Learning (AutoML) have achieved remarkable results over the past years. In NLP,
especially Large Language Models (LLMs) have experienced a rapid series of
breakthroughs very recently. We envision that the two fields can radically push
the boundaries of each other through tight integration. To showcase this
vision, we explore the potential of a symbiotic relationship between AutoML and
LLMs, shedding light on how they can benefit each other. In particular, we
investigate both the opportunities to enhance AutoML approaches with LLMs from
different perspectives and the challenges of leveraging AutoML to further
improve LLMs. To this end, we survey existing work, and we critically assess
risks. We strongly believe that the integration of the two fields has the
potential to disrupt both fields, NLP and AutoML. By highlighting conceivable
synergies, but also risks, we aim to foster further exploration at the
intersection of AutoML and LLMs.

本文探索了自然语言处理和自动化机器学习之间的共生关系，重点关注了 LLMs 能为 AutoML 方法提供的机会，以及 AutoML 对于改进 LLMs 所带来的挑战，目的在于促进两个领域更深入的探索。