Papers with MGSM
The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI (2026.eacl-short)
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| Challenge: | Large Reasoning Models (LRMs) are highly effective on mathematical, scientific, and other question-answering tasks. |
| Approach: | They compare an LRM's reasoning in English to that of a multilingual question . they find that English reasoning traces exhibit a substantially higher presence of cognitive behaviors . |
| Outcome: | The LRMs generate reasoning sequences in English, but the language of the question is not. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
Layer-wise Swapping for Generalizable Multilingual Safety (2026.eacl-long)
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| Challenge: | Existing safety datasets are predominantly English-centric, limiting progress in multilingual safety alignment. |
| Approach: | They propose a safety-aware layer swapping method that transfers alignment from an English safety expert to low-resource language experts without additional training. |
| Outcome: | The proposed method preserves performance on general language understanding tasks while enhancing safety in the target languages. |
MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning (2026.eacl-long)
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Kosei Uemura, David Guzmán, Quang Phuoc Nguyen, Jesujoba Oluwadara Alabi, En-Shiun Annie Lee, David Ifeoluwa Adelani
| Challenge: | Existing methods to align large language models with multilingual encoders raise accuracy for low-resource languages (LRLs) but performance of LLMs in low- and high-resourced languages remains a problem. |
| Approach: | They propose a model-stacking framework that iteratively refines in 2-stages based on a curriculum strategy and adapts only a small set of DoRA weights. |
| Outcome: | The proposed framework improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini by 15.2 pp on the AfriMGSM benchmark. |
Question Translation Training for Better Multilingual Reasoning (2024.findings-acl)
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| Challenge: | Large language models have shown compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. |
| Approach: | They propose to train a model to translate reasoning questions into English by fine tuning on X-English parallel question data. |
| Outcome: | The proposed approach improves on LLaMA2-13B on the MGSM and MSVAMP multilingual reasoning benchmarks. |
MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)
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| Challenge: | Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data . |
| Approach: | They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language. |
| Outcome: | The proposed framework improves multilingual reasoning across languages on three benchmarks. |
MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages. |
| Approach: | They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance . |
| Outcome: | The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages. |
POLARIS: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair (2026.findings-acl)
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| Challenge: | Gödel agent Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark. |
| Approach: | They propose a Gödel agent that performs policy repair via experience abstraction . Polaris makes policy level changes with small, auditable patches that persist in the policy . |
| Outcome: | The proposed agent improves on MGSM, DROP, GPQA, and LitBench models over the base policy and competitive baselines. |