Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis
TL;DR
A study reveals that assessment instruments designed for humans do not measure the same constructs when applied to large language models (LLMs). This finding challenges the validity of using human assessments to evaluate LLM capabilities.
✦ Why It Matters
Reassess the evaluation methods used for AI to ensure they accurately reflect LLM capabilities.
Key Takeaways
- Evaluate LLMs with tailored assessment tools, not human-designed ones.
- Consider latent structure differences when interpreting LLM performance.
- Use exploratory factor analysis to assess construct validity.
- Challenge existing evaluation practices for AI capabilities.
Full Summary
As large language models (LLMs) become more prevalent, understanding their capabilities is crucial. Researchers often use assessment tools created for humans, like standardized tests, to evaluate LLM performance.
However, this study investigates whether the underlying constructs measured by these assessments are consistent between humans and LLMs. Focusing on high-school chemistry and a university entrance exam, the researchers compared human responses with those generated by six multimodal LLMs.
Using exploratory factor analysis and other statistical methods, they found significant differences in the latent structures of responses, indicating that the assessments do not measure the same skills for both groups. These results suggest that current evaluation practices may misrepresent LLM capabilities, raising questions about the appropriateness of using human-designed assessments for AI.