Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage
arXiv:2606.12767v1 Announce Type: new Abstract: Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use. We study how TMK-based question generation strategies affect dataset quality for procedural and multi-hop reasoning. We compare three strategies: strict generation from Task-Method-Knowledge (TMK) models, transcript-first generation with post-hoc TMK filtering, and TMK-aware generation that combines transcripts with structured guidance. To evaluate
The increasing complexity and adoption of AI systems necessitate better evaluation methods for their reasoning capabilities, especially in educational and task-oriented contexts.
Improved evaluation datasets for procedural reasoning will lead to more robust and reliable AI agents and educational tools, directly impacting their real-world performance and trustworthiness.
The focus on balancing naturalness, grounding, and multi-hop coverage in evaluation datasets marks a step towards more sophisticated and human-like AI reasoning assessment.
- · AI education platforms
- · AI agent developers
- · NLP researchers
- · Instructional design companies
- · Developers relying on simplistic evaluation metrics
- · AI systems with poor procedural reasoning
AI models will be developed and benchmarked against more rigorous and realistic reasoning tasks.
This could accelerate the deployment of AI agents capable of handling complex multi-step instructions in various domains.
Enhanced procedural reasoning may enable AI systems to autonomously learn and adapt to new, unscripted tasks more effectively.
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Read at arXiv cs.AI