DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models
arXiv:2603.23514v1 Announce Type: new Abstract: Large Language Models appear competent when answering general questions but often fail when pushed into domain-specific details. No existing methodology provides an out-of-the-box solution for measuring how deeply LLMs can sustain accurate responses under adaptive follow-up questioning across arbitrary domains. We present DepthCharge, a domain-agnostic framework that measures knowledge depth through three innovations: adaptive probing that generates follow-up questions based on concepts the model actually mentions, on-demand fact verification from authoritative sources, and survival statistics with constant sample sizes at every depth level. The framework can be deployed on any knowledge domain with publicly verifiable facts, without requiring pre-constructed test sets or domain-specific expertise. DepthCharge results are relative to the evaluator model used for answer checking, making the framework a tool for comparative evaluation ra
arXiv:2603.23514v1 Announce Type: new Abstract: Large Language Models appear competent when answering general questions but often fail when pushed into domain-specific details. No existing methodology provides an out-of-the-box solution for measuring how deeply LLMs can sustain accurate responses under adaptive follow-up questioning across arbitrary domains. We present DepthCharge, a domain-agnostic framework that measures knowledge depth through three innovations: adaptive probing that generates follow-up questions based on concepts the model actually mentions, on-demand fact verification from authoritative sources, and survival statistics with constant sample sizes at every depth level. The framework can be deployed on any knowledge domain with publicly verifiable facts, without requiring pre-constructed test sets or domain-specific expertise. DepthCharge results are relative to the evaluator model used for answer checking, making the framework a tool for comparative evaluation rather than absolute accuracy certification. Empirical validation across four diverse domains (Medicine, Constitutional Law, Ancient Rome, and Quantum Computing) with five frontier models demonstrates that DepthCharge reveals depth-dependent performance variation hidden by standard benchmarks. Expected Valid Depth (EVD) ranges from 3.45 to 7.55 across model-domain combinations, and model rankings vary substantially by domain, with no single model dominating all areas. Cost-performance analysis further reveals that expensive models do not always achieve deeper knowledge, suggesting that domain-specific evaluation is more informative than aggregate benchmarks for model selection in professional applications.
Executive Summary
This article presents DepthCharge, a domain-agnostic framework for measuring the depth-dependent knowledge of Large Language Models (LLMs). DepthCharge employs three innovations: adaptive probing, on-demand fact verification, and survival statistics. Empirical validation across four diverse domains demonstrates that DepthCharge reveals depth-dependent performance variation hidden by standard benchmarks. The framework's results are relative to the evaluator model used for answer checking, making it a tool for comparative evaluation rather than absolute accuracy certification. The study highlights the importance of domain-specific evaluation for model selection in professional applications.
Key Points
- ▸ DepthCharge is a domain-agnostic framework for measuring depth-dependent knowledge in LLMs.
- ▸ The framework employs adaptive probing, on-demand fact verification, and survival statistics.
- ▸ Empirical validation demonstrates depth-dependent performance variation across domains.
Merits
Strength in Methodology
The framework's adaptive probing and on-demand fact verification innovations provide a robust method for measuring knowledge depth.
Domain-Agnostic Flexibility
DepthCharge can be deployed across arbitrary domains with publicly verifiable facts, without requiring pre-constructed test sets or domain-specific expertise.
Comparative Evaluation
The framework's results are relative to the evaluator model used for answer checking, making it a tool for comparative evaluation rather than absolute accuracy certification.
Demerits
Limitation in Scoping
The study only evaluates DepthCharge across four diverse domains, limiting its scope and generalizability.
Evaluation Dependence on Evaluator Model
The framework's results are dependent on the evaluator model used for answer checking, which may introduce bias and variability in the evaluation process.
Expert Commentary
The article presents a significant contribution to the field of AI evaluation and LLM research. The framework's innovations and empirical validation provide a robust method for measuring knowledge depth, which is essential for evaluating the performance of LLMs in professional applications. The study's findings on depth-dependent performance variation and the importance of domain-specific evaluation are particularly insightful, highlighting the need for more nuanced and context-dependent evaluation methods. However, the study's limitations in scoping and evaluation dependence on the evaluator model should be addressed in future research.
Recommendations
- ✓ Future research should expand the scope of the study to include a broader range of domains and LLMs.
- ✓ Developers and users of LLMs should prioritize domain-specific evaluation and consider the specific knowledge depth requirements of a task when selecting a model.
Sources
Original: arXiv - cs.CL
