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    <abstract>We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question–answer pairs. The questions are drawn from Latin pedagogical sources, including exams, quizbowl-style trivia, and textbooks ranging from the 1800s to the present. After automated extraction, cleaning, and manual review, the dataset covers a diverse range of question types: knowledge- and skill-based, multihop reasoning, constrained translation, and mixed language pairs. To our knowledge, this is the first QA benchmark centered on Latin. As a case study, we evaluate three large language models–LLaMa 3, Qwen QwQ, and OpenAI’s o3-mini–finding that all perform worse on skill-oriented questions. Although the reasoning models perform better on scansion and literary-device tasks, they offer limited improvement overall. QwQ performs slightly better on questions asked in Latin, but LLaMa3 and o3-mini are more task dependent. This dataset provides a new resource for assessing model capabilities in a specialized linguistic and cultural domain, and the creation process can be easily adapted for other languages. The dataset is available at: https://github.com/slanglab/RespondeoQA</abstract>
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%0 Conference Proceedings
%T RespondeoQA: A Benchmark for Bilingual Latin-English Question Answering
%A Hudspeth, Marisa
%A Burns, Patrick J.
%A O’Connor, Brendan
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F hudspeth-etal-2026-respondeoqa
%X We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question–answer pairs. The questions are drawn from Latin pedagogical sources, including exams, quizbowl-style trivia, and textbooks ranging from the 1800s to the present. After automated extraction, cleaning, and manual review, the dataset covers a diverse range of question types: knowledge- and skill-based, multihop reasoning, constrained translation, and mixed language pairs. To our knowledge, this is the first QA benchmark centered on Latin. As a case study, we evaluate three large language models–LLaMa 3, Qwen QwQ, and OpenAI’s o3-mini–finding that all perform worse on skill-oriented questions. Although the reasoning models perform better on scansion and literary-device tasks, they offer limited improvement overall. QwQ performs slightly better on questions asked in Latin, but LLaMa3 and o3-mini are more task dependent. This dataset provides a new resource for assessing model capabilities in a specialized linguistic and cultural domain, and the creation process can be easily adapted for other languages. The dataset is available at: https://github.com/slanglab/RespondeoQA
%R 10.63317/58p5htfv3nad
%U https://aclanthology.org/2026.lrec-1.80/
%U https://doi.org/10.63317/58p5htfv3nad
%P 1031-1043

Markdown (Informal)

[RespondeoQA: A Benchmark for Bilingual Latin-English Question Answering](https://aclanthology.org/2026.lrec-1.80/) (Hudspeth et al., LREC 2026)

• RespondeoQA: A Benchmark for Bilingual Latin-English Question Answering (Hudspeth et al., LREC 2026)

ACL

• Marisa Hudspeth, Patrick J. Burns, and Brendan O’Connor. 2026. RespondeoQA: A Benchmark for Bilingual Latin-English Question Answering. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1031–1043, Palma de Mallorca, Spain. ELRA Language Resource Association.