| Challenge: | a study compares zero-shot role-playing, reasoning-optimized LLMs, and reasoning-based LLM. |
| Approach: | They propose to use reasoning-optimized LLMs to improve role-playing performance . they propose to develop a chain-of-thought-based learning system that can be used to improve LLM performance if reasoning is used . |
| Outcome: | The proposed research compares zero-shot role-playing, role-playering with Chain-of-Thought, and reasoning-optimized LLMs. |
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Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
How Likely Do LLMs with CoT Mimic Human Reasoning? (2025.coling-main)
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| Challenge: | Using chain-of-thought to elicit reasoning capabilities is not always effective and accurate. |
| Approach: | They compare the reasoning process of LLMs with humans to understand the causal chain . they find that LLM deviates from the ideal causal chain, resulting in spurious correlations . |
| Outcome: | The proposed method does not improve performance or accurately represent reasoning processes in LLMs. |
Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)
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| Challenge: | a recent study shows that large language models struggle with long-term, complex reasoning tasks. |
| Approach: | They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability. |
| Outcome: | The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model . |
Aligning Large and Small Language Models via Chain-of-Thought Reasoning (2024.eacl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting empowers Large Language Models to solve complex reasoning tasks in a step-wise manner. |
| Approach: | They propose a method for aligning and transferring reasoning abilities between larger and smaller Language Models by using CoT-Demonstrations. |
| Outcome: | The proposed method outperforms baselines on question-answering and mathematical reasoning benchmarks. |
Better Zero-Shot Reasoning with Role-Play Prompting (2024.naacl-long)
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Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong
| Challenge: | Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama. |
| Approach: | They propose a strategy for role-play prompting and assess its performance under the zero-shot setting. |
| Outcome: | The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks. |
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. |
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning (2025.naacl-long)
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| Challenge: | Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities. |
| Approach: | They propose to use supervised fine-tuning and Quantized Low-Rank Adapters to improve LLMs' task-specific performance to address privacy and safety risks. |
| Outcome: | The proposed model improves the accuracy of the chain-of-thought reasonings across four datasets and demonstrates that the faithfulness of CoT reasoning decreases. |
Evaluating the Deductive Competence of Large Language Models (2024.naacl-long)
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| Challenge: | Existing large language models have limited abilities to solve deductive reasoning problems . performance differences between conditions do not improve overall performance . |
| Approach: | They investigate whether several large language models can solve a deductive reasoning problem in their conventional form. |
| Outcome: | The proposed models can solve a classic type of deductive reasoning problem in their conventional form. |
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024.acl-long)
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| Challenge: | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLM. |
| Approach: | They propose a group discussion framework to enrich the set of discussion mechanisms. |
| Outcome: | The proposed framework performs better on a wide range of reasoning tasks and backbone LLMs. |
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering (2025.acl-long)
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| Challenge: | Existing work finds that long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. |
| Approach: | They propose a representation engineering method to unleash the general long CoT reasoning capabilities of LLMs. |
| Outcome: | The proposed method is effective in in-domain and cross-domain scenarios. |