# INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

arXiv: https://arxiv.org/abs/2411.19799 · HTML: https://mlsanity.com/paper/2411.19799/

## Key facts

- Cited by 86 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 80 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 30 of 70)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [Detecting Pretraining Data from Large Language Models](https://mlsanity.com/paper/2310.16789/) (2023)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://mlsanity.com/paper/2409.12183/) (2024)
- [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://mlsanity.com/paper/2004.01401/) (2020)

## Built on by (most cited first, 30 of 86)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier](https://mlsanity.com/paper/2412.04261/) (2024)
- [Enhancing Multilingual LLM Pretraining with Model-Based Data Selection](https://mlsanity.com/paper/2502.10361/) (2025)
- [On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability](https://mlsanity.com/paper/2608.30320/) (2026)
- [MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languages](https://mlsanity.com/paper/2504.10356/) (2025)
- [BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2502.07346/) (2025)
- [Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models](https://mlsanity.com/paper/2605.11887/) (2026)
- [Localized Cultural Knowledge is Conserved and Controllable in Large Language Models](https://mlsanity.com/paper/2504.10191/) (2025)
- [Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization](https://mlsanity.com/paper/2508.04796/) (2025)
- [MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages](https://mlsanity.com/paper/2506.19468/) (2025)
- [ERNIE 5.0 Technical Report](https://mlsanity.com/paper/2602.04705/) (2026)
- [Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation](https://mlsanity.com/paper/2606.06712/) (2026)
- [The Multilingual Divide and Its Impact on Global AI Safety](https://mlsanity.com/paper/2505.21344/) (2025)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [Revisiting Multilingual Data Mixtures in Language Model Pretraining](https://mlsanity.com/paper/2510.25947/) (2025)
- [mR3: Multilingual Rubric-Agnostic Reward Reasoning Models](https://mlsanity.com/paper/2510.01146/) (2025)
- [Hunyuan-MT Technical Report](https://mlsanity.com/paper/2509.05209/) (2025)
- [HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models](https://mlsanity.com/paper/2511.01066/) (2025)
- [SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training](https://mlsanity.com/paper/2605.08738/) (2026)
- [SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala](https://mlsanity.com/paper/2509.03162/) (2025)
- [Bielik 11B v3: Multilingual Large Language Model for European Languages](https://mlsanity.com/paper/2601.11579/) (2025)
- [Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency](https://mlsanity.com/paper/2603.17102/) (2026)
- [The Art of Asking: Multilingual Prompt Optimization for Synthetic Data](https://mlsanity.com/paper/2510.19806/) (2025)
- [Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs](https://mlsanity.com/paper/2412.14471/) (2024)
- [CUS-QA: Local-Knowledge-Oriented Open-Ended Question Answering Dataset](https://mlsanity.com/paper/2507.22752/) (2025)
- [UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages](https://mlsanity.com/paper/2509.21294/) (2025)
- [Aligning Multilingual Reasoning with Verifiable Semantics from a High-Resource Expert Model](https://mlsanity.com/paper/2509.25543/) (2025)
- [TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior](https://mlsanity.com/paper/2512.20757/) (2025)
- [Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language](https://mlsanity.com/paper/2603.11881/) (2026)

## Abstract (arXiv)

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (ie, multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreover, current practices in multilingual benchmark construction often translate English resources, ignoring the regional and cultural knowledge of the environments in which multilingual systems would be used. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts. Our novel resource, INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across 44 written languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.