Summary of Dialectical Behavior Therapy Approach to Llm Prompting, by Oxana Vitman et al.
Dialectical Behavior Therapy Approach to LLM Prompting
by Oxana Vitman, Nika Amaglobeli, Paul Plachinda
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes a novel prompting strategy inspired by Dialectical Behavioral Therapy (DBT) to improve large language models’ (LLMs) performance on complex reasoning tasks. By applying DBT’s basic concepts of shaping dialog, the authors construct prompts that guide LLMs into breaking tasks into intermediate steps and provide step-by-step demonstrations. The proposed technique, dubbed CoT-DBT, is tested on various datasets and LLMs with different numbers of parameters, achieving significant accuracy improvements. Specifically, the 8b-parameter model shows a 4.8% increase in accuracy on the Aqua dataset, while the 14b-parameter model achieves a 16.2% increase on the StrategyQA and GSM8K datasets. The results demonstrate the effectiveness of CoT-DBT in improving LLMs’ performance on smaller models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps computers learn better by creating new ways to ask them questions. The authors take a therapy technique called DBT, which helps people think more clearly, and apply it to how we prompt language models to answer complex questions. They test this new approach with different types of computer models and question datasets, and find that it improves their accuracy by up to 16% in some cases. |
Keywords
» Artificial intelligence » Prompt » Prompting
