| Challenge: | Program-of-Thought (PoT) replaces natural language-based Chain-ofThough (CoT) but introduces more reasoning errors, such as incorrect formulas or flawed logic, compared to CoT. |
| Approach: | They propose a method that integrates CoT and Program-of-Thought to achieve more accurate reasoning and reinforcement learning. |
| Outcome: | The proposed method achieves an average improvement of 6.5% on the Llama-Base model and 4.3% on the Mistral-Bass model across 8 mathematical calculation datasets. |
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Chain-of-Thought Reasoning in Tabular Language Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to extend chain-of-thought reasoning into large language models are not viable in the scenario of privatization deployment or limited resources. |
| Approach: | They propose a framework that extends chain-of-thought reasoning into tabular language models . framework coordinates two TaLMs responsible for CoT generation and answer inference . |
| Outcome: | The proposed framework outperforms the state-of-the-art ChatGPT on the TABMWP dataset by 9.55% (82.60%92.15% in accuracy) with less parameters (0.8B). |
Think Like You Execute: Verifiable Chain of Thought from Program Traces (2026.acl-industry)
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Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, Shivdeep Singh, Parameswaran Selvam, Hiroshi Kanayama, Hima Patel
| Challenge: | Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, not verifiable accounts of actual program behavior. |
| Approach: | They propose to ground CoT generation directly in program execution traces to improve reasoning capabilities. |
| Outcome: | The proposed pipeline improves performance on live code benchmarks and on cruxEval-output and cruxeval-input. |
Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)
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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python. |
| Approach: | They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability. |
| Outcome: | The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline. |
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. |
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models. |
| Approach: | They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification. |
| Outcome: | The proposed framework surpasses all baselines on 8 datasets and 5 LLMs. |
Markov Chain of Thought for Efficient Mathematical Reasoning (2025.naacl-long)
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| Challenge: | Existing studies have sought to enhance the mathematical reasoning capabilities of large language models. |
| Approach: | They propose a Markov Chain of Thought (MCoT) that compresses previous reasoning steps into a simplified question. |
| Outcome: | The proposed method improves efficiency and maintains comparable accuracy. |
GoT: Effective Graph-of-Thought Reasoning in Language Models (2024.findings-naacl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have been advancing at an unprecedented pace. |
| Approach: | They propose a graph-based approach which models human thought processes as a chain and as 'graphs' by representing thought units as nodes and connections between them as edges, they capture the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. |
| Outcome: | The proposed model improves on a text-only reasoning task and a multimodal reasoning task. |
Program-of-Thought Reveals LLM Abstraction Ceilings (2026.findings-eacl)
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| Challenge: | Large language models exhibit reasoning ability when supervised with chain-of-thought (CoT) traces. |
| Approach: | They evaluate large language models with CoT traces and fine-tune them with Program-of-Thought supervision. |
| Outcome: | The proposed model performance degrades sharply under numeric perturbations under isomorphic variants. |
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)
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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian
| Challenge: | Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic. |
| Approach: | They propose a chain-of-thought reasoning framework with three key designs to address these issues. |
| Outcome: | The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG. |
Over-Reasoning and Redundant Calculation of Large Language Models (2024.eacl-short)
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| Challenge: | Large language models (LLMs) can solve problems step-by-step, but it is unclear whether they know when to use CoT and whether they are always necessary. |
| Approach: | They propose to use LLMs to generate redundant calculations and reasoning on a manually constructed math QA dataset, GSM8K-Zero. |
| Outcome: | The proposed model generates redundant calculations and reasoning on a manually constructed math QA dataset, but it is unclear whether it is necessary to use CoT reasoning. |