RespondeoQA: a Benchmark for Bilingual Latin-English Question Answering

• URL: http://arxiv.org/abs/2604.20738v1
• Date: Wed, 22 Apr 2026 16:24:46 GMT
• Title: RespondeoQA: a Benchmark for Bilingual Latin-English Question Answering
• Abstract summary: We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings.<n>The questions are drawn from Latin pedagogical sources, including exams, quizbowl-style trivia, and textbooks ranging from the 1800s to the present.<n>The dataset covers a diverse range of question types: knowledge- and skill-based, multihop reasoning, constrained translation, and mixed language pairs.
• Score: 2.5578258168516816
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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

Related papers

• OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context [4.39796591456426]
  We present OMoS-QA, a dataset of German and English questions paired with trustworthy documents and manually annotated answers. Questions are automatically generated with an open-source large language model (LLM) and answer sentences are selected by crowd workers. We find high precision and low-to-mid recall in selecting answer sentences, which is a favorable trade-off to avoid misleading users.
  arXiv  Detail & Related papers  (2024-07-22T15:40:17Z)
• CaLMQA: Exploring culturally specific long-form question answering across 23 languages [58.18984409715615]
  CaLMQA is a dataset of 51.7K culturally specific questions across 23 different languages.<n>We evaluate factuality, relevance and surface-level quality of LLM-generated long-form answers.
  arXiv  Detail & Related papers  (2024-06-25T17:45:26Z)
• From Multiple-Choice to Extractive QA: A Case Study for English and Arabic [51.13706104333848]
  We explore the feasibility of repurposing an existing multilingual dataset for a new NLP task.<n>We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic.<n>We aim to help others adapt our approach for the remaining 120 BELEBELE language variants, many of which are deemed under-resourced.
  arXiv  Detail & Related papers  (2024-04-26T11:46:05Z)
• Question Translation Training for Better Multilingual Reasoning [108.10066378240879]
  Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. A typical solution is to translate instruction data into all languages of interest, and then train on the resulting multilingual data, which is called translate-training. In this paper we explore the benefits of question alignment, where we train the model to translate reasoning questions into English by finetuning on X-English parallel question data.
  arXiv  Detail & Related papers  (2024-01-15T16:39:10Z)
• Evaluating and Modeling Attribution for Cross-Lingual Question Answering [80.4807682093432]
  This work is the first to study attribution for cross-lingual question answering. We collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. We find that a substantial portion of the answers is not attributable to any retrieved passages.
  arXiv  Detail & Related papers  (2023-05-23T17:57:46Z)
• Bridging the Language Gap: Knowledge Injected Multilingual Question Answering [19.768708263635176]
  We propose a generalized cross-lingual transfer framework to enhance the model's ability to understand different languages. Experiment results on real-world datasets MLQA demonstrate that the proposed method can improve the performance by a large margin.
  arXiv  Detail & Related papers  (2023-04-06T15:41:25Z)
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering [124.16250115608604]
  We present Science Question Answering (SQA), a new benchmark that consists of 21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations. We show that SQA improves the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in fine-tuned UnifiedQA. Our analysis further shows that language models, similar to humans, benefit from explanations to learn from fewer data and achieve the same performance with just 40% of the data.
  arXiv  Detail & Related papers  (2022-09-20T07:04:24Z)
• ELQA: A Corpus of Metalinguistic Questions and Answers about English [24.006858451437534]
  Collected from two online forums, the >70k questions cover wide-ranging topics including grammar, meaning, fluency, and etymology. Unlike most NLP datasets, this corpus is metalinguistic -- it consists of language about language.
  arXiv  Detail & Related papers  (2022-05-01T04:29:50Z)
• QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers [68.9964449363406]
  We extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before.
  arXiv  Detail & Related papers  (2022-01-31T22:19:55Z)
• Multilingual Answer Sentence Reranking via Automatically Translated Data [97.98885151955467]
  We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems. The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
  arXiv  Detail & Related papers  (2021-02-20T03:52:08Z)
• XOR QA: Cross-lingual Open-Retrieval Question Answering [75.20578121267411]
  This work extends open-retrieval question answering to a cross-lingual setting. We construct a large-scale dataset built on questions lacking same-language answers.
  arXiv  Detail & Related papers  (2020-10-22T16:47:17Z)
• DaNetQA: a yes/no Question Answering Dataset for the Russian Language [4.33757980378616]
  DaNetQA, a new question-answering corpus, follows (Clark et al. design: it comprises natural yes/no questions). Each question is paired with a paragraph from Wikipedia and an answer, derived from the paragraph. The task is to take both the question and a paragraph as input and come up with a yes/no answer, i.e. to produce a binary output.
  arXiv  Detail & Related papers  (2020-10-06T10:30:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.