Opper

Context Reasoning

Context understanding and reasoning tasks test accurate answers grounded in provided context. This capability is essential for knowledge-base support bots, policy lookup systems, and internal knowledge Q&A applications. Models are evaluated on their ability to provide accurate answers that are properly grounded in the given context rather than hallucinating information.

Context Reasoning – mistral/mistral-tiny-eu Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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Cost

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Context

Input

Order all the cities named in the text by time of visiting them

Expected output

1.Cartagena (14 Jun 1995) 2.Leticia (mid-Jun 1995, the day after Cartagena) 3.Manchester (3 Oct 2002 entry) 4.Tangier (mid-Apr 2004, one week before 22 Apr) 5.Chefchaouen (mid-Apr 2004, just after Tangier) 6.Granada (22 Apr 2004) 7.Córdoba 8.Sevilla 9.Cádiz 10.Málaga 11.Jaén 12.Toledo 13.Madrid 14.Ávila 15.Salamanca 16.Zamora 17.San Sebastián 18.Bilbao (8 May 2004)

Model output

['Cartagena, Colombia', 'Bilbao, Spain', 'Granada, Spain', 'Cordoba, Spain', 'Sevilla, Spain', 'Cadiz, Spain', 'Malaga, Spain', 'Jaen, Spain', 'Toledo, Spain', 'Avila, Spain', 'Zamora, Spain', 'San Sebastian, Spain', 'Bilbao, Spain']