# AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning

arXiv: https://arxiv.org/abs/2602.09914 · HTML: https://mlsanity.com/paper/2602.09914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Towards Best Practices for Training Multilingual Dense Retrieval Models](https://mlsanity.com/paper/2204.02363/) (2022)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities](https://mlsanity.com/paper/2303.14406/) (2023)
- [A Static Pruning Study on Sparse Neural Retrievers](https://mlsanity.com/paper/2304.12702/) (2023)
- [An Amharic News Text classification Dataset](https://mlsanity.com/paper/2103.05639/) (2021)
- [Language Agnostic Multilingual Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2210.06633/) (2022)
- [Dense Retrieval for Low Resource Languages -- the Case of Amharic Language](https://mlsanity.com/paper/2503.18570/) (2025)
- [Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval](https://mlsanity.com/paper/2505.19356/) (2025)

## Abstract (arXiv)

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.