Dialectical Behavior Therapy Approach to LLM Prompting
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
Large language models demonstrated state-of-the-art results on various reasoning tasks when applying the chain-of-thought (CoT) prompting technique. CoT prompting guides the model into breaking tasks into a few intermediate steps and provides step-by-step demonstrations. However, solving complex reasoning tasks remains a challenge. In this paper, we propose a novel prompting strategy inspired by Dialectical Behavioral Therapy (DBT). DBT, a form of cognitive-behavioral therapy, aims to help individuals cope with stress by developing a system of reasoning. We applied DBT's basic concepts of shaping dialog to construct prompts and conducted experiments on different datasets and LLMs with various numbers of parameters. Our results show that prompts crafted with DBT techniques significantly improve results on smaller models, achieving a 7% increase in accuracy on the StrategyQA, 4.8% on Aqua dataset using 8b parameters model, and a 16.2% increase on the StrategyQA, 5.3% on GSM8K dataset with 14b parameters model.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 17 references that are in the map. First posted on arXiv 10 Oct 2024.
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- 2022Large Language Models are Zero-Shot Reasoners24 May
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
Other references · 8
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- 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
- 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
- 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
- 2023Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language modelscited by 16
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- 2023Towards Better Chain-of-Thought Prompting Strategies: A Survey
- 2023Stress Testing Chain-of-Thought Prompting for Large Language Models
- 2026Where do LLMs Fall Short in CBT-Guided Affective Reasoning?
- 2025Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting
- 2023Chain-Of-Thought Prompting Under Streaming Batch: A Case Study
- 2026Assessing the Effectiveness of LLMs in Delivering Cognitive Behavioral Therapy
- 2026Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve
- 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods