A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
arXiv preprint arXiv:2103.12693 (2021)
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Diffusion-based localized editing framework for faithful summarization of evolving contexts, introducing the StreamSum benchmark and showing tradeoffs in faithfulness, speed, and preservation.
Aggregating imperfect factuality metrics into preference data from lexically similar summaries yields consistent factuality gains across model sizes, allowing smaller models to approach larger ones.
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On the Factual Consistency of Text-based Explainable Recommendation Models
A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
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Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts
Diffusion-based localized editing framework for faithful summarization of evolving contexts, introducing the StreamSum benchmark and showing tradeoffs in faithfulness, speed, and preservation.
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Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Aggregating imperfect factuality metrics into preference data from lexically similar summaries yields consistent factuality gains across model sizes, allowing smaller models to approach larger ones.