Paper List
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
-
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
-
Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
-
Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
-
Cross-Species Antimicrobial Resistance Prediction from Genomic Foundation Models
This paper addresses the core challenge of predicting antimicrobial resistance across phylogenetically distinct bacterial species, where traditional m...
Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
Marywood University | The University of Scranton | University of North Carolina Wilmington | California State University Dominguez Hills
30秒速读
IN SHORT: This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that integrate psychometric and cognitive science methodologies for non-technical stakeholders.
核心创新
- Methodology Introduces the first cloud-based platform applying Classical Test Theory (CTT) and psychometric validity principles (Cronbach's α > .70, AVE > .50) to systematically evaluate LLMs as cognitive entities rather than mere tools.
- Methodology Implements a three-cycle Action Design Science framework (Relevance-Rigor-Design) with nested Build–Intervene–Evaluate loops, bridging Popperian falsifiability, Cognitive Load Theory, and stakeholder requirements into a unified evaluation system.
- Biology Validates that modern LLMs (GPT-4, LLaMA-3) satisfy core psychometric validity criteria—including convergent, discriminant, predictive, and external validity—and outperform earlier models (GPT-3.5, LLaMA-2) across these dimensions.
主要结论
- The PsyCogMetrics™ AI Lab successfully operationalizes psychometric principles with demonstrated reliability metrics (Cronbach's α > .70) and validity frameworks (convergent/discriminant validity) for LLM evaluation.
- The platform addresses three critical pain points: mitigates benchmark saturation through dynamic evaluation, reduces data contamination via reproducible workflows, and expands coverage through cognitive science methodologies.
- Design validation shows GPT-4 and LLaMA-3 satisfy psychometric validity criteria and outperform earlier models, with GPT-4 reaching six-year-old human parity on Theory of Mind vignettes (Strachan et al., 2024).
摘要: This study presents the development of the PsyCogMetrics™ AI Lab (https://psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometric and cognitive-science methodologies for Large Language Model (LLM) evaluation. Framed as a three-cycle Action Design Science study, the Relevance Cycle identifies key limitations in current evaluation methods and unfulfilled stakeholder needs. The Rigor Cycle draws on kernel theories such as Popperian falsifiability, Classical Test Theory, and Cognitive Load Theory to derive deductive design objectives. The Design Cycle operationalizes these objectives through nested Build–Intervene–Evaluate loops. The study contributes a novel IT artifact, a validated design for LLM evaluation, benefiting research at the intersection of AI, psychology, cognitive science, and the social and behavioral sciences.