Abstract
Neural retrieval and GPT-style generative models rely on large, high-quality supervised data, which is still scarce for low-resource languages such as Amharic. We release an Amharic data resource consisting of two datasets that supports research on (i) neural retrieval-ranking and (ii) instruction-following text generation. The retrieval-ranking dataset contains 1,091 manually verified query-positive-negative document triplets drawn from diverse Amharic sources and constructed to support contrastive training and benchmarking of neural retrievers (e.g., DPR, ColBERT-style late interaction and SPLADE-style sparse neural retrieval). Triplets are created through a combination of expert-curated queries, web-derived queries, and LLM-assisted generation, with positive/negative documents selected from the web or synthesized by LLMs and then validated by native speakers. The instruction prompt-response dataset comprises 6,285 Amharic prompt-response pairs spanning multiple domains and instruction types, generated with several LLMs and refined through manual review and correction for grammaticality, relevance, fluency, and factual plausibility. We release both datasets with standardized splits and formats (CSV,JSON,JSONL) to enable reproducible work on Amharic retrieval, ranking, and generative modelling. These datasets also come with a methodology that can be generalized to other low-resource languages.
Abstract (translated)
神经检索和GPT风格的生成模型依赖于大量高质量的监督数据，但对于像阿姆哈拉语这样的低资源语言来说，这种类型的数据仍然稀缺。我们发布了一个包含两个数据集的阿姆哈拉语数据资源，支持关于（i）神经检索排序研究以及（ii）指令跟随文本生成的研究。 **检索-排名数据集**包含了1,091个人工验证过的查询-正样本-负样本三元组，这些三元组是从多种阿姆哈拉语来源中提取并构建出来的，用于支持对比训练和神经检索器（例如DPR、ColBERT风格的后期交互以及SPLADE风格的稀疏神经检索）的基准测试。这些建立了查询-文档对的数据集通过专家策划的查询、网络衍生的查询及LLM辅助生成相结合的方法创建，而正样本/负样本文档则从互联网上选择或由LLMs合成后，再经过母语者的验证。 **指令提示响应数据集**包含6,285个阿姆哈拉语的指令-响应对，这些数据覆盖了多个领域和多种类型的指令，并通过几个大型语言模型生成并经手工审核修正以确保语法正确性、相关性、流畅性和事实上的合理性后得到完善。 我们以标准化的分割格式（CSV、JSON、JSONL）发布这两个数据集，以便于在阿姆哈拉语检索、排名及生成建模方面进行可重复的研究。此外，这些数据集还附带了一种可以推广到其他低资源语言的方法论。
URL
https://arxiv.org/abs/2602.09914
