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Deep Interaction: An Efficient Human-AI Interaction... | AI Research

Key Takeaways

  • Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models Large language models (LLMs) have become powerful tools for solving com...
  • The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks.
  • To address this issue, we propose an efficient human intervention mechanism for precisely correcting reasoning errors in LLMs, termed Deep Interaction.
  • Our approach enables direct editing of the original response, allowing erroneous parts to be corrected while preserving accurate reasoning steps.
  • We refine the edited CoT into a distilled prompt, which then steers the LLM along the corrected reasoning path.
Paper AbstractExpand

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 <You are right, I made a mistake here> 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 Interaction. Our approach enables direct editing of the original response, allowing erroneous parts to be corrected while preserving accurate reasoning steps. We refine the edited CoT into a distilled prompt, which then steers the LLM along the corrected reasoning path. Experimental results show that our method achieves over a 25% improvement in correction success rate and reduces token usage by approximately 40% on STEM tasks reasoning compared to baseline approaches.

Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
Large language models (LLMs) have become powerful tools for solving complex, multi-step problems using Chain-of-Thought (CoT) reasoning. However, when these models make mistakes, current interaction methods are often frustrating: the model may simply apologize and repeat the same error, or the user must engage in a long, tedious conversation to point out the flaw. This paper introduces "Deep Interaction," a new framework that allows users to directly edit the model’s reasoning steps, similar to using a "track changes" feature in a document editor. This approach aims to make human-AI collaboration more efficient, precise, and reliable.

A New Way to Correct Reasoning

Instead of relying on back-and-forth dialogue to fix errors, Deep Interaction treats the model's reasoning chain as a document that can be revised. When a user identifies an error in a specific step, they can directly edit that portion of the text. The system then uses a "track changes" mechanism to identify what was deleted, inserted, or kept. By focusing on the specific section that needs correction while preserving the accurate parts of the reasoning, the model is better equipped to understand the user's intent and follow the corrected path.

How the Framework Works

The process follows a structured pipeline to ensure the model stays on track. Once a user edits the reasoning chain, the system segments the text into three parts: the pre-edit section, the edited section, and the post-edit section. It then applies specific strategies to each:

  • Emphasis: The edited content is highlighted (using formatting like double asterisks) to ensure the model pays close attention to the correction.

  • Pruning: Redundant or low-value phrases are filtered out to keep the reasoning concise.

  • Removing: If the original reasoning was flawed, subsequent steps that might be contaminated by that error are removed to prevent confusion.

  • Delexicalization: Numerical values are masked to prevent the model from simply memorizing previous answers and to encourage it to perform actual reasoning.

Improved Performance and Efficiency

Experimental results on challenging STEM reasoning tasks demonstrate that Deep Interaction is significantly more effective than traditional conversational correction. The method achieves over a 25% improvement in the success rate of corrections. Furthermore, by streamlining the interaction and reducing the need for long, repetitive dialogue, the framework reduces token usage by approximately 40%, making the process both faster and more cost-effective.

Important Considerations

While Deep Interaction offers a more direct way to guide AI, the authors note that current LLM architectures do not natively support pausing and correcting at every single step of a reasoning chain. Consequently, the current implementation relies on a "one-shot" generation approach where the corrected reasoning is fed back into the model to produce a new, improved output. The researchers suggest that future developments could explore more integrated, staged output methods to further enhance the interaction between humans and AI.

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