Papers with dynamic QA

1 papers
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)

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Challenge: Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical.
Approach: They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs.
Outcome: The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA.

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