We introduce SAGE; a Generative LLM for inferring attribute values for
products across world-wide e-Commerce catalogs. We introduce a novel
formulation of the attribute-value prediction problem as a Seq2Seq
summarization task, across languages, product types and target attributes. Our
novel modeling approach lifts the restriction of predicting attribute values
within a pre-specified set of choices, as well as, the requirement that the
sought attribute values need to be explicitly mentioned in the text. SAGE can
infer attribute values even when such values are mentioned implicitly using
periphrastic language, or not-at-all-as is the case for common-sense defaults.
Additionally, SAGE is capable of predicting whether an attribute is
inapplicable for the product at hand, or non-obtainable from the available
information. SAGE is the first method able to tackle all aspects of the
attribute-value-prediction task as they arise in practical settings in
e-Commerce catalogs. A comprehensive set of experiments demonstrates the
effectiveness of the proposed approach, as well as, its superiority against
state-of-the-art competing alternatives. Moreover, our experiments highlight
SAGE's ability to tackle the task of predicting attribute values in zero-shot
setting; thereby, opening up opportunities for significantly reducing the
overall number of labeled examples required for training.

我们介绍了 SAGE，这是一个用于在全球电子商务目录中推断产品属性值的生成型 LLM。我们引入了一种新颖的属性值预测问题的公式化，作为一个跨语言、产品类型和目标属性的 Seq2Seq 总结任务。我们的新颖建模方法消除了在预测属性值时必须从预定选择集合中进行选择的限制，以及必须在文本中明确提及所需属性值的要求。SAGE 甚至可以推断那些用迂回语言含蓄提及或根本未提及的属性值，例如普遍常识默认值。此外，SAGE 可以预测某个属性在手头产品中是否不适用或无法从可用信息中获得。SAGE 是首个能够应对电子商务目录中实际设置中出现的属性 - 值预测任务的方法。一系列综合实验证明了所提方法的效果以及其对现有最先进方法的优越性。此外，我们的实验突出了 SAGE 在零样本情境中预测属性值的能力，从而为大幅减少所需的标记示例总数提供了机会。

SAGE：面向十亿级产品目录的结构化属性值生成

SAGE: Structured Attribute Value Generation for Billion-Scale Product  Catalogs

Linking biomedical entities is an essential aspect in biomedical natural
language processing tasks, such as text mining and question answering. However,
a difficulty of linking the biomedical entities using current large language
models (LLM) trained on a general corpus is that biomedical entities are
scarcely distributed in texts and therefore have been rarely seen during
training by the LLM. At the same time, those LLMs are not aware of high level
semantic connection between different biomedical entities, which are useful in
identifying similar concepts in different textual contexts. To cope with
aforementioned problems, some recent works focused on injecting knowledge graph
information into LLMs. However, former methods either ignore the relational
knowledge of the entities or lead to catastrophic forgetting. Therefore, we
propose a novel framework to pre-train the powerful generative LLM by a corpus
synthesized from a KG. In the evaluations we are unable to confirm the benefit
of including synonym, description or relational information.

将基因组学实体链接到自然语言处理任务中，提出了一种使用知识图谱信息预训练生成式大型语言模型的新框架。

基于三元注意力预训练的生物医学实体链接

Biomedical Entity Linking with Triple-aware Pre-Training

Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant
attention in the domain of Recommendation Systems (RS). These models, trained
on massive amounts of data using self-supervised learning, have demonstrated
remarkable success in learning universal representations and have the potential
to enhance various aspects of recommendation systems by some effective transfer
techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect
of harnessing the power of language models in enhancing recommendation quality
is the utilization of their high-quality representations of textual features
and their extensive coverage of external knowledge to establish correlations
between items and users. To provide a comprehensive understanding of the
existing LLM-based recommendation systems, this survey presents a taxonomy that
categorizes these models into two major paradigms, respectively Discriminative
LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation
(GLLM4Rec), with the latter being systematically sorted out for the first time.
Furthermore, we systematically review and analyze existing LLM-based
recommendation systems within each paradigm, providing insights into their
methodologies, techniques, and performance. Additionally, we identify key
challenges and several valuable findings to provide researchers and
practitioners with inspiration.

本文概论了基于大型语言模型的推荐系统，提出了两种主要类型（分别为判别型 LLM 推荐 DLLM4Rec 和生成型 LLM 推荐 GLLM4Rec），分别从方法、技术和性能等方面对现有的 LLM 推荐系统进行了系统的分类和评估，并指出了主要挑战和有价值的发现。