Reasoning over Commonsense Knowledge Bases (CSKB), i.e. CSKB reasoning, has
been explored as a way to acquire new commonsense knowledge based on reference
knowledge in the original CSKBs and external prior knowledge. Despite the
advancement of Large Language Models (LLM) and prompt engineering techniques in
various reasoning tasks, they still struggle to deal with CSKB reasoning. One
of the problems is that it is hard for them to acquire explicit relational
constraints in CSKBs from only in-context exemplars, due to a lack of symbolic
reasoning capabilities (Bengio et al., 2021). To this end, we proposed
**ConstraintChecker**, a plugin over prompting techniques to provide and check
explicit constraints. When considering a new knowledge instance,
ConstraintChecker employs a rule-based module to produce a list of constraints,
then it uses a zero-shot learning module to check whether this knowledge
instance satisfies all constraints. The acquired constraint-checking result is
then aggregated with the output of the main prompting technique to produce the
final output. Experimental results on CSKB Reasoning benchmarks demonstrate the
effectiveness of our method by bringing consistent improvements over all
prompting methods. Codes and data are available at
https://github.com/HKUST-KnowComp/ConstraintChecker.

使用新开发的 ConstraintChecker 插件结合提示技术进行 CSKB 推理，通过引入关系约束来处理具有前所未有的隐式相关约束的知识实例，实验结果表明这种方法能够在各种提示方法上带来一致的改进。

ConstraintChecker：用于大型语言模型推理常识知识库的插件

ConstraintChecker: A Plugin for Large Language Models to Reason on  Commonsense Knowledge Bases

In spite of the rapidly evolving landscape of text-to-image generation, the
synthesis and manipulation of multiple entities while adhering to specific
relational constraints pose enduring challenges. This paper introduces an
innovative progressive synthesis and editing operation that systematically
incorporates entities into the target image, ensuring their adherence to
spatial and relational constraints at each sequential step. Our key insight
stems from the observation that while a pre-trained text-to-image diffusion
model adeptly handles one or two entities, it often falters when dealing with a
greater number. To address this limitation, we propose harnessing the
capabilities of a Large Language Model (LLM) to decompose intricate and
protracted text descriptions into coherent directives adhering to stringent
formats. To facilitate the execution of directives involving distinct semantic
operations-namely insertion, editing, and erasing-we formulate the Stimulus,
Response, and Fusion (SRF) framework. Within this framework, latent regions are
gently stimulated in alignment with each operation, followed by the fusion of
the responsive latent components to achieve cohesive entity manipulation. Our
proposed framework yields notable advancements in object synthesis,
particularly when confronted with intricate and lengthy textual inputs.
Consequently, it establishes a new benchmark for text-to-image generation
tasks, further elevating the field's performance standards.

通过创新性的渐进综合和编辑操作，该论文引入了一种系统地将实体合并到目标图像中，并确保它们在每个顺序步骤中遵守空间和关系约束的方法。