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Complex Reasoning Dataset: COM2 for Commonsense Knowledge Graphs

Core Concepts
The author presents COM2, a dataset derived from CSKGs to enhance complex reasoning ability in language models without human annotations.
The content introduces COM2, a dataset created for complex commonsense reasoning using logical queries from CSKGs. It addresses the challenges of multi-hop reasoning and provides insights into training language models for enhanced performance across various tasks. The paper discusses the construction of COM2, sampling multi-hop logical queries from CSKGs, and verbalizing them to create a benchmark for complex reasoning. Experiments show significant improvements in language models' reasoning abilities trained on COM2. The study highlights the importance of addressing data scarcity in training language models for complex reasoning tasks. The results demonstrate the efficacy of leveraging existing knowledge graphs to enhance commonsense reasoning capabilities in AI systems. Overall, the research contributes to advancing AI capabilities in complex commonsense reasoning through innovative dataset creation and model training strategies.
Our final COM2 dataset comprises 790K question-answer pairs. Language models trained on COM2 exhibit significant improvements in complex reasoning ability. Vera filters out triples with a plausibility score lower than 0.5. The average number of answers for 2p queries is 7.93. The workers were paid an average of 16 USD per hour during crowdsourcing.
"Event commonsense reasoning requires the ability to reason about the relationship between events." - Tianqing Fang et al. "Our experiments show that language models trained on COM2 exhibit significant improvements in complex reasoning ability." - Tianqing Fang et al.

Deeper Inquiries

How can we ensure that biases present in existing knowledge graphs do not propagate into datasets like COM2?

To prevent biases from existing knowledge graphs like ATOMIC20 20 from propagating into datasets like COM2, several strategies can be implemented: Data Filtering: Implement rigorous filtering mechanisms to identify and remove biased or inaccurate triples from the knowledge graph before sampling queries for COM2. This could involve using plausibility scoring systems, as done with the Vera scorer in the context of COM2. Diverse Sampling: Ensure diverse sampling of queries to cover a wide range of scenarios and reduce the likelihood of reinforcing specific biases present in the original knowledge graph. Bias Detection Algorithms: Utilize bias detection algorithms to identify and mitigate any inherent biases within the sampled data for COM2. These algorithms can help flag potentially biased or sensitive content for further review. Human Oversight: Incorporate human oversight during dataset creation to manually verify samples, especially those involving complex reasoning or potential bias triggers, ensuring a more nuanced understanding and mitigation of biases. Regular Auditing: Conduct regular audits on both the source knowledge graph and derived datasets like COM2 to continually assess and address any emerging biases over time.

What are the implications of relying on lexical-overlap based automatic evaluation metrics for generative commonsense inference?

Relying solely on lexical-overlap based automatic evaluation metrics for generative commonsense inference has several implications: Limited Evaluation Scope: Lexical-overlap metrics such as BLEU-2, ROUGE-L, CIDEr, BERTScore primarily focus on surface-level similarities between generated text and reference answers. They may not capture deeper semantic nuances or logical coherence essential for assessing commonsense reasoning accurately. Biased Towards Surface-Level Matching: These metrics prioritize exact word matches without considering broader contextual understanding or logical consistency required in generating meaningful commonsense inferences. Inadequate Reflection of Quality: Generative models may produce valid responses that differ slightly from reference answers but still convey accurate information logically; however, they might receive lower scores due to strict matching criteria imposed by lexical-overlap metrics. Overlooking Creativity & Novelty: Metrics focused on lexical overlap may penalize creative expressions or novel interpretations that deviate slightly from standard phrasing found in reference texts but still provide relevant insights.

How can future research leverage datasets like COM2 to address real-world challenges beyond AI applications?

Future research can leverage datasets like COM2 beyond AI applications by exploring interdisciplinary collaborations and innovative approaches: Educational Tools: Develop educational tools leveraging complex reasoning tasks from COM2 to enhance critical thinking skills among students across various disciplines. For example: Creating interactive platforms where users engage with multi-hop logical queries similar to those found in COM2 could foster problem-solving abilities outside traditional AI contexts. 3Healthcare Decision Support Systems: Integrate common-sense reasoning capabilities trained on datasets like COM22 into healthcare decision support systems For instance: Enhancing medical diagnosis processes by incorporating advanced reasoning models capable of inferring complex relationships between patient symptoms. 5Policy Formulation: Utilize insights gained from analyzing large-scale commonsense reasoning data such asCOM22to inform evidence-based policy-making Example: Developing frameworks that analyze societal trends through multi-event narratives extractedfromCOM22to guide policymakersin addressing social issues effectively 7Ethics & Bias Mitigation: Apply learningsfromdatasetslikeCOM22toidentifyandmitigatebiasesacrossvariousdomains -For instance: Employing advanced logicalexplanationmodelsderivedfromCOM22todetectandaddressbiasesthroughoutdecision-makingprocessesinfinanceorlegalcontexts