Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)
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| Challenge: | Existing models for explainable recommendation have neglected faithfulness of KG reasoning . |
| Approach: | They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation. |
| Outcome: | The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation. |
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Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer. |
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
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| Challenge: | Knowledge graphs are used to express explanations for the model's answer choice. |
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| Challenge: | Current methods for generating faithful explanations overlook path decoding faithfulness, leading to divergence between graph encoder outputs and model predictions. |
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| Challenge: | Current approaches to interpretability evaluation focus on faithfulness criteria . current approaches focus on readability, plausibility and faithfulness . |
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
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| Challenge: | Existing methods for evaluating concepts from different perspectives lack a unified formalization. |
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