DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models
Summary: arXiv:2603.23514v1 Announce Type: cross
Abstract
Large Language Models (LLMs) exhibit impressive capabilities in responding to general inquiries; however, they often struggle when faced with domain-specific questions requiring nuanced understanding. Current methodologies lack a comprehensive solution to assess the depth of knowledge LLMs maintain when subjected to adaptive follow-up queries across various fields. This article introduces DepthCharge, a groundbreaking framework that evaluates knowledge depth through three distinct innovations:
- Adaptive Probing: This feature generates follow-up questions based on concepts that the model has mentioned, allowing for a more tailored assessment of knowledge depth.
- On-Demand Fact Verification: DepthCharge employs authoritative sources for fact-checking, ensuring that the information provided by the model is accurate and reliable.
- Survival Statistics: The framework maintains constant sample sizes at every depth level to provide a consistent evaluation metric.
Framework Overview
DepthCharge can be implemented across any knowledge domain with publicly verifiable facts, eliminating the need for pre-constructed test sets or specialized domain knowledge. The results generated by the framework are relative to the evaluator model employed for answer verification, positioning DepthCharge as a comparative evaluation tool rather than an absolute measure of accuracy.
Empirical Validation
The framework has undergone empirical validation across four diverse domains: Medicine, Constitutional Law, Ancient Rome, and Quantum Computing. Five leading models were assessed, revealing that DepthCharge uncovers depth-dependent performance variations that are often obscured by traditional benchmarks. The Expected Valid Depth (EVD) across different model-domain combinations ranged from 3.45 to 7.55.
Moreover, the rankings of the models demonstrated significant variability depending on the domain, implying that no single model excels universally across all fields. This insight underscores the importance of contextual evaluation in assessing LLM capabilities.
Cost-Performance Analysis
In addition to evaluating knowledge depth, a cost-performance analysis was conducted to ascertain the relationship between model expense and knowledge depth. The findings indicated that higher-cost models do not necessarily equate to deeper knowledge, highlighting the need for domain-specific evaluations in professional applications.
Conclusion
DepthCharge presents a significant advancement in measuring the depth of knowledge within LLMs, offering a flexible, domain-agnostic framework that provides valuable insights into model capabilities. As the demand for accurate and reliable AI-driven responses increases, DepthCharge could serve as a crucial tool for developers and researchers aiming to ensure that LLMs are effective in specialized fields.