Papers with CSKBs
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases (2024.eacl-long)
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| Challenge: | Reasoning over Commonsense Knowledge Bases (CSKBs) is a way to acquire new commonsense knowledge based on reference knowledge in original CSKB and external prior knowledge. |
| Approach: | They propose a plugin to provide and check explicit relational constraints over prompting techniques. |
| Outcome: | The proposed method improves on existing prompting techniques and CSKB reasoning. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
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| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population (2022.findings-emnlp)
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| Challenge: | Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, but it requires out-of-domain generalization ability as the source CSMB for training is of a relatively smaller scale (1M) . |
| Approach: | They propose a semi-supervised learning framework that uses a teacher model to provide pseudo labels on the unlabeled candidate dataset for a student model to learn from. |
| Outcome: | The proposed framework can improve the backbone model KG-BERT (RoBERTa-large) by 3.3 points on the overall performance and especially, 5.3 points on out-of-domain performance. |
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021.emnlp-main)
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| Challenge: | Commonsense knowledge bases are mostly human-generated and reflect societal biases . a filtering-based approach can reduce the issues in both resources and models but leads to a performance drop . |
| Approach: | They propose a filtering-based approach to mitigating representational harms in ConceptNet and GenericsKB . they propose filtered-based approaches can reduce issues in both resources and models but leads to performance drop . |
| Outcome: | The proposed approach reduces issues in resources and models but leads to performance drop . the paper proposes a filtering-based approach that reduces biases but leaves room for future work . |
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)
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| Challenge: | Existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation). |
| Approach: | They propose a reasoning over commonsense knowledge bases (CSKBs) that are free-text and have a human annotation set to probe commonsensical reasoning. |
| Outcome: | The proposed model is based on a human-annotated evaluation set and is compared with existing models on the population task. |
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot commonsense question answering use incomplete CSKBs . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |
| Approach: | They propose a framework that abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of the CSKB and expands the ground-truth answer space. |
| Outcome: | Experiments show that CAR can generalize to zero-shot commonsense scenarios . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |
QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize. |
| Approach: | They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options. |
| Outcome: | The proposed framework outperforms baseline approaches while using only 33% of the synthetic data. |