Evaluating Large Language Models for Health-related Queries with Presuppositions (2024.findings-acl)
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| Challenge: | a large number of health-related queries require factually accurate answers . however, the lack of accurate answers may cause real-world harm . |
| Approach: | They evaluate the factual accuracy and consistency of large language models using a dataset consisting of health-related queries with varying degrees of presuppositions. |
| Outcome: | The proposed model responses agree with 23-32% of existing false claims and 49-55% with novel fabricated claims. |
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