Taking advantage of the widespread use of ontologies to organise and
harmonize knowledge across several distinct domains, this paper proposes a
novel approach to improve an embedding-Large Language Model (embedding-LLM) of
interest by infusing the knowledge formalized by a reference ontology:
ontological knowledge infusion aims at boosting the ability of the considered
LLM to effectively model the knowledge domain described by the infused
ontology. The linguistic information (i.e. concept synonyms and descriptions)
and structural information (i.e. is-a relations) formalized by the ontology are
utilized to compile a comprehensive set of concept definitions, with the
assistance of a powerful generative LLM (i.e. GPT-3.5-turbo). These concept
definitions are then employed to fine-tune the target embedding-LLM using a
contrastive learning framework. To demonstrate and evaluate the proposed
approach, we utilize the biomedical disease ontology MONDO. The results show
that embedding-LLMs enhanced by ontological disease knowledge exhibit an
improved capability to effectively evaluate the similarity of in-domain
sentences from biomedical documents mentioning diseases, without compromising
their out-of-domain performance.

通过注入本体知识来改进嵌入式大语言模型（embedding-LLM），本研究利用广泛的本体使用和基于对照学习框架，通过医学疾病本体的生物医学文档，展示了提高嵌入式 LLM 在描述疾病领域中的相似性评估能力的实验结果。

面向本体增强的大型语言模型表示学习

Towards Ontology-Enhanced Representation Learning for Large Language  Models

We describe a method, based on Jennifer Nado's definition of classification
procedures as targets of conceptual engineering, that implements such
procedures using a large language model. We then apply this method using data
from the Wikidata knowledge graph to evaluate concept definitions from two
paradigmatic conceptual engineering projects: the International Astronomical
Union's redefinition of PLANET and Haslanger's ameliorative analysis of WOMAN.
We discuss implications of this work for the theory and practice of conceptual
engineering. The code and data can be found on GitHub.

基于 Jennifer Nado 对分类程序作为概念工程的目标的定义，我们描述了一种使用大型语言模型实现此类程序的方法。然后，我们使用 Wikidata 知识图中的数据来评估两个典型概念工程项目的概念定义：国际天文学联合会对 “行星” 的重新定义和 Haslanger 对 “女性” 的改善分析。我们讨论了这项工作对概念工程理论和实践的意义。代码和数据可在 GitHub 上找到。

使用大型语言模型进行概念工程

Conceptual Engineering Using Large Language Models

Although large language models (LLMs) exhibit remarkable capacity to leverage
in-context demonstrations, it is still unclear to what extent they can learn
new concepts or facts from ground-truth labels. To address this question, we
examine the capacity of instruction-tuned LLMs to follow in-context concept
guidelines for sentence labeling tasks. We design guidelines that present
different types of factual and counterfactual concept definitions, which are
used as prompts for zero-shot sentence classification tasks. Our results show
that although concept definitions consistently help in task performance, only
the larger models (with 70B parameters or more) have limited ability to work
under counterfactual contexts. Importantly, only proprietary models such as
GPT-3.5 and GPT-4 can recognize nonsensical guidelines, which we hypothesize is
due to more sophisticated alignment methods. Finally, we find that
Falcon-180B-chat is outperformed by Llama-2-70B-chat is most cases, which
indicates that careful fine-tuning is more effective than increasing model
scale. Altogether, our simple evaluation method reveals significant gaps in
concept understanding between the most capable open-source language models and
the leading proprietary APIs.

大语言模型通过指导文本对句子进行标记涉及了利用上下文示例的广泛语言模型，根据我们的简单评估方法，我们发现开源语言模型与领先的专有 API 之间在概念理解方面存在显著差距。