| Challenge: | Recent studies have introduced eclectic strategies to improve reasoning beyond English, but these methods are related to specific language that is not always optimal for reasoning. |
| Approach: | They propose a modular approach that instructs models to structure reasoning passages in a different problem space and then self-refines their capabilities to deliver step-wise reasoning passage. |
| Outcome: | The proposed approach achieves significant improvements in multilingual reasoning of various models and task, with improved reasoning consistency across languages. |
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A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)
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| Challenge: | This survey provides the first in-depth review of multilingual reasoning in Language Models. |
| Approach: | This survey provides the first in-depth review of multilingual reasoning in LMs. |
| Outcome: | The present study provides the first in-depth review of multilingual reasoning in LMs. |
Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)
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| Challenge: | xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. |
| Approach: | They propose a multilingual structured reasoning and explanation dataset that covers four tasks across six languages and extends the English STREET benchmark to 5 additional diverse languages. |
| Outcome: | The proposed models show improved multilingual performance on scientific commonsense reasoning subtasks and no regression on non-reasoning tasks. |
R2-MultiOmnia: Leading Multilingual Multimodal Reasoning via Self-Training (2025.acl-long)
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| Challenge: | Recent studies have introduced eclectic strategies to enhance MLLMs’ reasoning capabilities, but they remain related to a single language. |
| Approach: | They propose a modular approach that instructs models to abstract key elements of the reasoning process and refine reasoning trajectories via self-correction. |
| Outcome: | The proposed approach improves multimodal reasoning, gets aligned performances among the languages approaching strong models and improves the model's performance. |
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models (MultiLMs) are capable of logically reasoning over natural language statements, reasoning with their implicit knowledge, and performing multi-step reasoning when the model size is large enough. |
| Approach: | They propose a mechanism that encourages cross-lingual attention in code-switched sequences and improves reasoning performance by up to 14%. |
| Outcome: | The proposed approach improves reasoning performance by 14% and 4% on the RuleTaker and LeapOfThought datasets. |
MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration (2025.findings-emnlp)
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| Challenge: | Program-of-Thought is an important way for LLMs to solve mathematical problems. |
| Approach: | They propose a multilingual programme reasoning method that uses program instead of natural language in reasoning and proposes to integrate multilingual integration into the training and inference. |
| Outcome: | The proposed method improves individual language’s reasoning accuracy by 2.5% and improves performance by 8%. |
Demystifying Multilingual Reasoning in Process Reward Modeling (2025.findings-emnlp)
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| Challenge: | a recent study focuses on the use of large language models to solve multi-step reasoning tasks. |
| Approach: | They propose to extend large language models to multilingual settings by extending process reward models to English . they train multilingual PRMs on a dataset spanning seven languages, which is translated from english . |
| Outcome: | The proposed model improves accuracy and reduces early-stage reasoning errors. |
Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal (2025.naacl-srw)
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| Challenge: | a proposed thesis examines the role that multilinguality occupies in the development of practical and knowledgeable LLMs. |
| Approach: | They propose to use multilingual knowledge to improve LLM performance on NLP tasks . they extend the territorial disputes benchmark to retrieval-augmented generation setting . |
| Outcome: | The proposed methods improve LLMs' performance on standard natural language processing tasks by leveraging their existing multilingual knowledge. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |