Papers with Program-of-Thought
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models (2024.emnlp-industry)
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| Challenge: | Existing approaches to enhance mathematical reasoning and problem-solving abilities of Large Language Models (LLMs) despite their remarkable performance across domains, a notable challenge persists in the realm of mathematical reasoning. |
| Approach: | They propose a sequential learning approach that integrates the Chain-of-Thought and the Program-ofThough. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance by integrating CoT and PoT learning. |
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. |
How Do Humans Write Code? Large Models Do It the Same Way Too (2024.emnlp-main)
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| 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. |
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%. |
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)
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| Challenge: | Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics. |
| Approach: | They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
| Outcome: | The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
Self-Consistency from Only Two Samples: CoT–PoT Ensembling for Efficient LLM Reasoning (2026.findings-acl)
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| Challenge: | Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models but it comes at a high computational cost due to extensive sampling. |
| Approach: | They propose a hybrid ensembling approach that leverages the complementary strengths of Chain-of-Thought and Program-of -Thus . they propose encapsulating two different modes of reasoning to create a single output and a final answer is selected as the most frequently occurring one among these outputs. |
| Outcome: | The proposed approach reduces the number of samples required for SC by 9.3x . the majority of tasks can be addressed with only two samples, which has not been possible with prior methods. |