SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks . |
| Approach: | They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations. |
| Outcome: | The proposed method outperforms strong baselines on multilingual reasoning tasks. |
Similar Papers
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data. |
| Approach: | They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step . |
| Outcome: | The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance. |
Multilingual Reasoning via Self-training (2025.naacl-long)
Copied to clipboard
| 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. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
Copied to clipboard
| 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. |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Chain-of-Thought prompting is popular in reasoning tasks, but its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. |
| Approach: | They propose a Coarse-to-Fine Chain-of-Thought approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire essential concepts. |
| Outcome: | The proposed approach is effective in assisting the LLMs adapt to multi-grained NLU tasks under zero-shot and few-shot multi-domain settings. |
Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)
Copied to clipboard
| 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. |
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a framework that integrates cognitively inspired reasoning paradigms with linguistic constraints to reduce token usage while preserving reasoning accuracy. |
| Outcome: | The proposed framework reduces token usage while preserving reasoning accuracy across 18 reasoning datasets across multiple domains, languages, and modalities. |
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing prompting methods rely on only one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. |
| Approach: | They propose a semi-structured prompting approach that integrates parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. |
| Outcome: | The proposed prompting method surpasses existing prompting methods even exceeding those that require fine-tuning on open-domain multi-hop question answering datasets. |
DRT: Deep Reasoning Translation via Long Chain-of-Thought (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. |
| Approach: | They propose to use Qwen2.5 and LLama-3.1 as backbones to train long thought models to bring the success of long chain-of-thought (CoT) to neural machine translation. |
| Outcome: | The proposed model outperforms vanilla LLMs and LLM models which are fine-tuning on paired sentences without long thought and outperformed vanilla LRMs. |
Let’s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing efforts to improve CoT prompting have limitations that require extensive human effort or performance needs to be improved. |
| Approach: | They propose a prompt approach for automatic reasoning called LBS3 inspired by curriculum learning which better reflects human learning habits. |
| Outcome: | The proposed approach achieves strongly competitive performance compared to baselines in reasoning-intensive tasks with varying open- and closed-source LLMs. |
Disentangling Language Understanding and Reasoning Structures in Cross-lingual Chain-of-Thought Prompting (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study has shown that cross-lingual chain-of-thought prompting improves learning in low-resource languages. |
| Approach: | They examine whether benefits of cross-lingual prompting arise from language-specific reasoning structures . authors employ neuron intervention and perturbation techniques to analyze and deactivate language-related reasoning neurons . |
| Outcome: | The proposed study shows that language-specific reasoning structures are essential for reasoning in each language, but have minimal effect on reasoning in other languages. |