# LAReQA: Language-agnostic answer retrieval from a multilingual pool

arXiv: https://arxiv.org/abs/2004.05484 · HTML: https://mlsanity.com/paper/2004.05484/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 30 of 42)

- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [A Primer on Pretrained Multilingual Language Models](https://mlsanity.com/paper/2107.00676/) (2021)
- [Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer](https://mlsanity.com/paper/2106.16171/) (2021)
- [Understanding Cross-Lingual Alignment -- A Survey](https://mlsanity.com/paper/2404.06228/) (2024)
- [Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval](https://mlsanity.com/paper/2311.05800/) (2023)
- [Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots](https://mlsanity.com/paper/2103.09593/) (2021)
- [Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://mlsanity.com/paper/2305.14989/) (2023)
- [Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models](https://mlsanity.com/paper/2205.06130/) (2022)
- [Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations](https://mlsanity.com/paper/2401.05792/) (2024)
- [Exploring the Relationship between Alignment and Cross-lingual Transfer in Multilingual Transformers](https://mlsanity.com/paper/2306.02790/) (2023)
- [A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations](https://mlsanity.com/paper/2109.04727/) (2021)
- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages](https://mlsanity.com/paper/2205.06356/) (2022)
- [Soft Prompt Decoding for Multilingual Dense Retrieval](https://mlsanity.com/paper/2305.09025/) (2023)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [XRAG: Cross-lingual Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.10089/) (2025)
- [From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanation](https://mlsanity.com/paper/2109.04171/) (2021)
- [How Transliterations Improve Crosslingual Alignment](https://mlsanity.com/paper/2409.17326/) (2024)
- [Analysis of Multi-Source Language Training in Cross-Lingual Transfer](https://mlsanity.com/paper/2402.13562/) (2024)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation](https://mlsanity.com/paper/2207.09076/) (2022)
- [Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data](https://mlsanity.com/paper/2305.05295/) (2023)
- [Language Agnostic Code Embeddings](https://mlsanity.com/paper/2310.16803/) (2023)
- [Learning Job Title Representation from Job Description Aggregation Network](https://mlsanity.com/paper/2406.08055/) (2024)
- [On the Entity-Level Alignment in Crosslingual Consistency](https://mlsanity.com/paper/2510.10280/) (2025)
- [Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval](https://mlsanity.com/paper/2302.01626/) (2023)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment](https://mlsanity.com/paper/2407.14878/) (2024)

## Abstract (arXiv)

We present LAReQA, a challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool. Unlike previous cross-lingual tasks, LAReQA tests for "strong" cross-lingual alignment, requiring semantically related cross-language pairs to be closer in representation space than unrelated same-language pairs. Building on multilingual BERT (mBERT), we study different strategies for achieving strong alignment. We find that augmenting training data via machine translation is effective, and improves significantly over using mBERT out-of-the-box. Interestingly, the embedding baseline that performs the best on LAReQA falls short of competing baselines on zero-shot variants of our task that only target "weak" alignment. This finding underscores our claim that languageagnostic retrieval is a substantively new kind of cross-lingual evaluation.