Concretized Proposition Prompting Resolves Composition-Knowledge Dichotomy in Large Language Models
arXiv:2607.08018v1 Announce Type: new Abstract: LLMs often struggle to balance compositionality with knowledgeability, a challenge we define as Composition-Knowledge Dichotomy. To address this, we propose Concretized Proposition Prompting (CPP), a framework that explicitly concretizes propositions relevant to questions. The results demonstrate that CPP significantly enhances reasoning performance, particularly in medical benchmarks where precise knowledge is paramount, while being competitive on math benchmarks where deductive reasoning is prioritized. Additional experiments reveal that CPP is
The continuous development and scaling of LLMs necessitate advanced prompting techniques to overcome inherent limitations, making research into 'Composition-Knowledge Dichotomy' timely.
Improving LLM reasoning and knowledge application is critical for expanding their utility across complex domains, impacting industries from healthcare to finance.
This technique offers a pathway for LLMs to generate more reliable and precise outputs by explicitly concretizing propositions, potentially leading to more trustworthy AI applications.
- · AI developers
- · Healthcare sector
- · SaaS providers
- · Enterprises adopting AI
- · Companies relying on opaque AI solutions
- · Generative AI models with poor factuality
LLMs demonstrate enhanced reasoning and factual accuracy across complex tasks.
Increased adoption of LLMs in critical applications where precision and reliability are paramount.
Accelerated development of AI agents capable of higher-fidelity autonomous decision-making.
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Read at arXiv cs.AI