Caesar AI Atlas

Recognizing Textual Entailment (RTE)

Also known as: RTE

Caesar AI Atlas Definition

Recognizing Textual Entailment (RTE) is a benchmark task and dataset for evaluating whether a hypothesis can be logically inferred from a passage. Each example typically contains a passage, a hypothesis, and a label indicating whether the entailment holds.

Other Definitions

Recognizing Textual Entailment Source

A dataset for evaluating an LLM's ability to determine whether a hypothesis can be entailed (logically drawn) from a text passage. Each example in an RTE evaluation consists of three parts: - A passage, typically from news or Wikipedia articles - A hypothesis - The correct answer, which is either: - True, meaning the hypothesis can be entailed from the passage - False, meaning the hypothesis can't be entailed from the passage For example: - Passage: The Euro is the currency of the European Union. - Hypothesis: France uses the Euro as currency. - Entailment: True, because France is part of the European Union. RTE is a component of the SuperGLUE ensemble.

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