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cloud.google.com·7h ago
AI Research
arXiv CS.AI·24d ago·5 min read
SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
TL;DR
SCICONVBENCH is a new benchmark designed to evaluate Large Language Models (LLMs) on their ability to clarify and refine ill-posed scientific requests through multi-turn dialogue. It reveals that while current models excel at resolving inconsistencies, they struggle with disambiguation, achieving only 52.7% success in fluid mechanics.
✦ Why It Matters
Evaluate your LLM's performance on multi-turn clarification tasks to enhance scientific task formulation.
Key Takeaways
- Implement SCICONVBENCH to assess LLMs in scientific applications.
- Focus on improving disambiguation capabilities in your models.
- Utilize structured task ontologies for better task formulation.
- Monitor LLM assumptions to enhance user interaction quality.
How It Works
SCICONVBENCH combines a structured task ontology with a rubric-based evaluation framework to systematically assess LLM performance. It focuses on two main tasks: eliciting missing information through dialogue (disambiguation) and correcting contradictory requests (inconsistency resolution).
This approach allows for a nuanced understanding of how well LLMs can engage in multi-turn conversations to clarify scientific tasks.