Papers by Sivan Milton

2 papers
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue (2022.tacl-1)

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Challenge: a new benchmark for hallucination-free dialogues is based on knowledge-based conversational models that generate unsupported utterances . a recent study shows that models that are trustworthy generate unverifiable or factually incorrect statements .
Approach: They propose a data-centric solution to edit hallucinated responses in the Wizard of Wikipedia benchmark.
Outcome: The proposed model improves on the Wizard of Wikipedia benchmark while maintaining engaging conversations.
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models? (2022.naacl-main)

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Challenge: Existing knowledge-grounded conversational benchmarks produce factually invalid statements, a phenomenon commonly called hallucination.
Approach: They conduct a human study on knowledge-grounded conversational benchmarks and state-of-the-art models.
Outcome: The findings raise important questions on the quality of existing datasets and models.

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