Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers
📰 ArXiv cs.AI
Learn how to transform LLMs into proactive inquirers using Proactive Interactive Reasoning (PIR) to improve reasoning capabilities
Action Steps
- Apply Proactive Interactive Reasoning (PIR) to existing LLMs to enhance their reasoning capabilities
- Use Chain-of-Thought (CoT) prompting as a baseline for comparison
- Configure PIR to handle ambiguous or missing information by actively inquiring about it
- Test PIR-based LLMs on various reasoning tasks to evaluate their performance
- Compare the results with traditional passive solver approaches to measure the improvement
Who Needs to Know This
NLP researchers and AI engineers can benefit from this concept to develop more efficient and effective LLMs
Key Insight
💡 Proactive Interactive Reasoning (PIR) can overcome the limitations of traditional passive solver approaches in LLMs by actively inquiring about missing or ambiguous information
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🤖 Transform LLMs into proactive inquirers with Proactive Interactive Reasoning (PIR) to boost reasoning capabilities! #LLMs #NLP #AI
Key Takeaways
Learn how to transform LLMs into proactive inquirers using Proactive Interactive Reasoning (PIR) to improve reasoning capabilities
Full Article
Title: Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers
Abstract:
arXiv:2601.22139v2 Announce Type: replace-cross Abstract: Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers tha
Abstract:
arXiv:2601.22139v2 Announce Type: replace-cross Abstract: Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers tha
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