The sequential process of conceptualization and instantiation is essential to
generalizable commonsense reasoning as it allows the application of existing
knowledge to unfamiliar scenarios. However, existing works tend to undervalue
the step of instantiation and heavily rely on pre-built concept taxonomies and
human annotations to collect both types of knowledge, resulting in a lack of
instantiated knowledge to complete reasoning, high cost, and limited
scalability. To tackle these challenges, we introduce CANDLE, a distillation
framework that iteratively performs contextualized conceptualization and
instantiation over commonsense knowledge bases by instructing large language
models to generate both types of knowledge with critic filtering. By applying
CANDLE to ATOMIC, we construct a comprehensive knowledge base comprising six
million conceptualizations and instantiated commonsense knowledge triples. Both
types of knowledge are firmly rooted in the original ATOMIC dataset, and
intrinsic evaluations demonstrate their exceptional quality and diversity.
Empirical results indicate that distilling CANDLE on student models provides
benefits across four downstream tasks. Our code, data, and models are publicly
available at this https URL

通过引入 CANDLE，一个渗透式谐谑缠绕呼出器，研究人员在常识知识库上进行了环境相关的概念化和实例化，并通过评估验证提出的这些知识类型的杰出质量和多样性，进而得出结论，在学生模型上提取 CANDLE 可以对四个下游任务带来好处。

CANDLE: 大型语言模型中的常识推理的迭代概念化与实例提炼

CANDLE: Iterative Conceptualization and Instantiation Distillation from  Large Language Models for Commonsense Reasoning

Answer Set Programming (ASP) is a powerful logic-based programming language,
which is enjoying increasing interest within the scientific community and (very
recently) in industry. The evaluation of ASP programs is traditionally carried
out in two steps. At the first step an input program P undergoes the so-called
instantiation (or grounding) process, which produces a program P' semantically
equivalent to P, but not containing any variable; in turn, P' is evaluated by
using a backtracking search algorithm in the second step. It is well-known that
instantiation is important for the efficiency of the whole evaluation, might
become a bottleneck in common situations, is crucial in several realworld
applications, and is particularly relevant when huge input data has to be dealt
with. At the time of this writing, the available instantiator modules are not
able to exploit satisfactorily the latest hardware, featuring
multi-core/multi-processor SMP (Symmetric MultiProcessing) technologies. This
paper presents some parallel instantiation techniques, including load-balancing
and granularity control heuristics, which allow for the effective exploitation
of the processing power offered by modern SMP machines. This is confirmed by an
extensive experimental analysis herein reported.
To appear in Theory and Practice of Logic Programming (TPLP).
KEYWORDS: Answer Set Programming, Instantiation, Parallelism, Heuristics

本研究介绍了一些并行的实例化技术，包括负载平衡和粒度控制启发式方法，可有效利用现代 SMP 机器所提供的处理能力。