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AI Research
arXiv CS.AI·May 28·8 min read
Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
TL;DR
Reasoning models often fail to recognize when they lack sufficient information to answer a question, instead producing incorrect outputs rather than abstaining (declining to answer). This work addresses the detection-to-abstention gap—the mismatch between detecting insufficient information and actually refusing to respond.
✦ Why It Matters
Engineers can build more trustworthy AI systems that refuse uncertain answers instead of confidently providing wrong information.
Key Takeaways
- Adopt Judge-Then-Solve to improve model decision-making.
- Train models to recognize when to abstain from answering.
- Utilize reinforcement learning for better reasoning control.
- Focus on reducing unsupported assumptions in AI outputs.
How It Works
Judge-Then-Solve (JTS) trains models to first evaluate their ability to answer a question before attempting to solve it. This is achieved through a two-step process: an initial assessment of answerability followed by either proceeding to solve or abstaining.
The framework uses supervised warm-up and reinforcement learning, rewarding models for consistent and efficient decision-making.