Proficiency LLM Overview

Updated 15 November 2025

Proficiency LLM is a large language model assessed on multiple dimensions including linguistic, cognitive, and domain-specific proficiencies.
They utilize comprehensive benchmark frameworks to measure general language ability, task-specific skills, and native-like performance with precise metrics.
These models enable controllable and targeted outputs for applications in education, multilingual tasks, and specialized domains.

A Proficiency LLM (Proficiency LLM) refers to any LLM evaluated, fine-tuned, or designed to optimize and measure its skill in one or more well-defined dimensions of linguistic, cognitive, or task-oriented proficiency. Across research domains, "proficiency" can signal general language ability (often human-referenced), mastery of specialized academic or professional competencies, or explicit controllability over style or skill level. In the LLM context, proficiency benchmarking increasingly forms the foundation for model evaluation, development, and deployment in multilingual, multimodal, educational, and applied settings.

1. Multidimensional Proficiency: Definitional Frameworks

LLM proficiency is not a monolithic concept but comprises multiple interacting dimensions, which are formalized, measured, and operationalized according to the benchmark or application. Major axes observed in the literature include:

General Language Proficiency: Coverage of vocabulary, grammar, reading and listening comprehension, and writing/speaking capability as measured with human language proficiency exams or synthetic tasks (e.g., CEFR, TOEFL, standardized language exams) (Lothritz et al., 2 Apr 2025, Bannò et al., 14 Jul 2025).
Task and Domain Proficiency: Skill in specialized domains such as mathematical reasoning, scientific literature analysis, or code synthesis, often referencing structured benchmarks and psychometric frameworks (Mahdavi et al., 1 Apr 2025, Cai et al., 4 Mar 2024, Xia et al., 28 Mar 2024).
Native-like and Cultural Proficiency: Sociolinguistic competence, native-level fluency and factuality, and audience alignment across linguistic, tonal, and localized factual expectations (Whitehouse et al., 30 Sep 2025).
Codec and Tool Use Proficiency: Capability for accurate multimodal information retrieval, API/tool invocation, and planning—measured via benchmarks such as MultiAPI or LLM+P (Liu et al., 2023, Liu et al., 2023).
Controllable Proficiency: The ability for users or systems to steer the output's level (e.g., CEFR-aligned text generation for language learners or varying programming complexity for developers) (Malik et al., 5 Jun 2024, Rojpaisarnkit et al., 6 Nov 2025).

Proficiency LLMs are thus explicitly benchmarked, designed, or trained to optimize for one or more such axes.

2. Benchmarks and Evaluation Methodologies

A central development is the proliferation of benchmarks and formal frameworks designed to quantify proficiency across multiple axes and languages:

Benchmark	Principal Metrics	Key Axes & Range
AI Language Proficiency Monitor (Pomerenke et al., 11 Jul 2025)	$P_{m,l}$ , $P_m$ , $P_l$ (see below)	Translation, Classification, QA, Math, Truthfulness; 200 languages
MENLO (Whitehouse et al., 30 Sep 2025)	Fluency, Tone, Localized Tone, Factuality	Native-like quality; 47 language varieties
PATCH (Fang et al., 2 Apr 2024)	IRT-based latent trait $\theta$	Human population referencing; item and population norms
Speech LLM Grading (Ma et al., 27 May 2025)	RMSE, MAE, $r$ , $\kappa$ , $\rho$	Spoken L2 proficiency (pronunciation, fluency, lexis, structure), cross-task generalizability
Multimodal/Tool Use (Liu et al., 2023)	InvAcc, DmAcc, FuncAcc, ArgAcc	Invocation, domain/function/argument correctness
Proficiency Control (Malik et al., 5 Jun 2024)	ControlError, QualityScore, Cost	CEFR-controllable text generation; regression-aligned scoring

Scoring example (AI Language Proficiency Monitor):

Raw scores $S_{m,l,t}$ (model, language, task) are min-max normalized per task:

$\widehat S_{m,l,t} =\frac{S_{m,l,t}-\min_{m',l'}S_{m',l',t}}{\max_{m',l'}S_{m',l',t}-\min_{m',l'}S_{m',l',t}}$

Proficiency for model–language pair:

$P_{m,l} =\frac{1}{|T|}\sum_{t\in T}\widehat S_{m,l,t}$

Model-level: $P_m =\frac{1}{|L|}\sum_{l\in L}P_{m,l}$ ; Language-level: $P_l =\frac{1}{|M|}\sum_{m\in M}P_{m,l}$ .

3. Proficiency in Multilingual and Low-Resource Contexts

Proficiency LLMs serve a critical role in characterizing and tracking the degree of coverage and capability across the world's languages:

On the AI Language Proficiency Monitor, English achieves $P_{\text{English}}\approx0.96$ , while many African and Indigenous languages fall below $P\approx0.5$ (e.g., Swahili $0.45$, Quechua $0.30$), despite coverage in translation, classification, QA, math, and factuality tasks (Pomerenke et al., 11 Jul 2025).
Commercial LLMs derived from Google's PaLM reach $P_m\approx0.95$ on high-resource languages, but open-source models (Llama 3, Gemma 3) lag on low-resource languages.
Among proficiency measurement tools for low-resource languages, standardized exams (Luxembourgish A1–C2) demonstrate that large LLMs approach near-native accuracy, while medium-sized models (15–200B) sometimes perform below random guess (Lothritz et al., 2 Apr 2025).
Correlation between exam-based proficiency and downstream (LuxGen) generation task quality is strong for small and large model clusters ( $r\approx0.6$ –$0.8$).

4. Controllable and Targeted Proficiency

Proficiency LLMs can be constructed or tuned for explicit control over output skill level:

CEFR-aligned generation ("CALM" model) allows control of text difficulty for language learning by encoding the target level as a control token, optimized via supervised finetuning + RL to minimize $($ predicted CEFR $-t)^2$ (Malik et al., 5 Jun 2024).
Prompt-based proficiency control is effective for large LLMs (GPT-4: ControlError $0.28$–$0.57$), but fine-tuning and RL are required to achieve comparable control in smaller, open models (Mistral-7B, LLaMA-2-7B), with best CtrlErr $0.39$–$0.60$.
In software engineering, prompt proficiency (CEFR level of prompt) influences correctness and complexity of generated code; higher-proficiency prompts yield higher pass@1 across models, with statistical significance except for highly robust models (Claude Sonnet 4) (Rojpaisarnkit et al., 6 Nov 2025).

5. Task- and Domain-Oriented Proficiency

LLM proficiency is context-sensitive and must be evaluated with respect to specific knowledge or reasoning domains:

Mathematical proficiency evaluated via proof validity (not just answer correctness) shows that LLMs achieve high final-answer rates (DeepSeek: 63%), yet almost 0% of solutions are valid proofs at the Olympiad level; most errors fall into "proof by example," "invented facts," or heuristic pattern matching (Mahdavi et al., 1 Apr 2025).
In scientific literature, proficiency involves stratified cognitive tasks: Memorization (L1), Comprehension (L2), and Analysis (L3). LLMs perform well on L1/L2 (factual recall/extraction), but struggle with multimodal or molecule-resonant L3 challenges (Cai et al., 4 Mar 2024).
Tool use and multimodal proficiency benchmarks such as MultiAPI distinguish between invocation, domain identification, function selection, and argument filling. Even GPT-3.5 achieves >99% invocation accuracy, but only ~53% function match and ~43% argument match, with frequent domain confusions (Liu et al., 2023).
AI coding proficiency (the ability for an LLM to use libraries effectively) exposes up to 84% performance difference between libraries with similar functionality, significant gaps between LLMs, and the necessity of integrating such assessments into technical stack evaluation (Zhang et al., 14 Sep 2025).

6. Evaluation, Fairness, and Psychometric Alignment

Advanced proficiency evaluation frameworks incorporate measurement and fairness constructs from psychometrics:

The PATCH framework leverages validated testing and Item Response Theory (IRT) to estimate latent proficiency traits ( $\theta$ ), enabling norm-referenced comparison between LLMs and 56 human population distributions (e.g., TIMSS 2011) (Fang et al., 2 Apr 2024).
Raw accuracy and psychometric (IRT) trait orderings can diverge; patching LLM benchmarking with item-level and population-level norms provides uncertainty quantification, construct validity, and diagnostic utility.
The MENLO framework foregrounds sociolinguistic audience design and crowd-validated dimension-specific scoring to quantify "native-like" quality across fluency, tone, and factuality (Whitehouse et al., 30 Sep 2025).

7. Limitations, Recommendations, and Future Research

Proficiency gaps persist in low-resource languages and specialized domains, often worsened as high-resource performance improves more rapidly over time (Pomerenke et al., 11 Jul 2025).
Fine-grained, interpretable proficiency measurement (e.g., localized tone, CEFR-controlled outputs, analytic descriptors) increases transparency and supports integration into downstream applications, education, and policy.
Training strategies such as reinforcement learning, multi-task learning, and reward shaping improve proficiency alignment but remain below human inter-annotator reliability.
Explicit integration of proficiency-aware tools into technology selection, curricular design, and assessment policy is recommended; procedural fairness (norm-referenced, justified item inclusion, transparent scoring) is essential for high-stakes and equitable deployment.
The field calls for expansion of task and language diversity, systematic collection of high-quality evaluation data, and research on the correlation of proficiency benchmarks with end-user outcomes and real-world application performance.

In summary, a Proficiency LLM is a LLM systematically evaluated, specialized, or controlled with respect to discrete, often multidimensional measures of linguistic, cognitive, or tool/task competence. Contemporary research shows substantial advances in benchmarking, controlling, and dissecting LLM proficiency, but persistent deficits remain—particularly for low-resource languages, advanced reasoning tasks, and reliable, human-aligned assessment—driving ongoing methodological innovation and infrastructural investment across academic, industrial, and applied AI communities.