Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks. However, when errors occur, current interaction approaches typically involve re-generating another response that may make mistakes again, or users laboriously flag the faulty step in follow-up turns that may get responses followed by similar errors recurring. To address this issue, we propose an efficient human intervention mechanism for precisely correcting reasoning errors in LLMs, termed Deep Inte
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- PossiblePossibly related (embedding) · 55%Northwind AI →
- LinkedLinked via arxiv author · 85%Hefeng Zhou →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Jinxuan Zhang →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Jiong Lou →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Yuxin Liu →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Chaochao Lu →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Jingjing Qu →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”
- LinkedLinked via arxiv author · 85%Jie Li →
“Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models”