CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge

Commonsense knowledge is crucial for artificial intelligence systems to understand natural language. Previous commonsense knowledge acquisition approaches typically rely on human annotations (for example, ATOMIC) or text generation models (for example, COMET.) Human annotation could provide high-quality commonsense knowledge, yet its high cost often results in relatively small scale and low coverage. On the other hand, generation models have the potential to automatically generate more knowledge. Nonetheless, machine learning models often fit the training data well and thus struggle to generat…

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at http://nlp.jhu.edu/record.

ViCor：通过大语言模型衔接视觉理解与常识推理

ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models

本研究针对视觉常识推理中VLM与LLM各有优劣的问题，提出ViCor协作框架，由LLM分类问题类型并引导VLM获取视觉信息，在无需领域微调的情况下性能优于同类方法。

Decker：面向常识事实验证的异构知识双重校验

Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification

本文提出常识事实验证模型Decker，通过揭示结构化与非结构化异构知识间的潜在关系进行双重校验，并在CSQA2.0和CREAK数据集上验证了其有效性与推理能力。

CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge

Most benchmark datasets targeting commonsense reasoning focus on everyday scenarios: physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al., 2019], social knowledge like bumping into someone is awkward [Sap et al., 2019], and other generic situations. However, there is a rich space of commonsense inferences anchored to knowledge about specific entities: for example, deciding the truthfulness of a claim "Harry Potter can teach classes on how to fly on a broomstick." Can models learn to combine entity knowledge with commonsense reasoning in this fashion? We in…

LLM+AL：桥接大语言模型与动作语言以实现复杂的动作推理

LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions

本文提出LLM+AL方法，将大语言模型的自然语言理解与动作语言的符号推理结合，在复杂动作推理中仅需少量人工纠正即可持续得到正确答案，并支持动作语言的自动生成。