ProfBench: LLM Performance in Professional Fields

• ProfBench is a multidimensional benchmark assessing LLMs on professional expertise through real-world tasks in domains like Physics, Chemistry, Finance, and Consulting.
• It uses expert-crafted rubrics and binary decision criteria to evaluate response quality, synthesis, and logical reasoning.
• The benchmark introduces scalable, low-bias evaluation techniques and dynamic sample allocation to enhance fairness and reduce costs.

ProfBench is a multidimensional benchmark specifically developed to assess and compare the performance of LLMs on tasks requiring authentic professional expertise, comprehensive document synthesis, and high-level domain-specific reasoning. By moving beyond traditional exam-style tasks, ProfBench structures its evaluation around real-world applications in core professional fields—Physics PhD, Chemistry PhD, Finance MBA, and Consulting MBA—emphasizing not only LLM generation capabilities but also their ability to judge and grade according to complex expert rubrics. The benchmark introduces scalable, low-bias evaluation techniques to provide a fair and accessible platform for rigorous model comparison in professional contexts (Wang et al., 21 Oct 2025).

1. Benchmark Scope and Motivation

ProfBench addresses a key limitation in LLM evaluation: the difficulty of verifying responses in complex professional and applied domains. Existing benchmarks typically focus on mathematics, programming, or short-answer QA, domains with concise, easily-validated responses. However, many critical real-world applications demand nuanced document analysis, synthesis of information across sources, and extended logical reasoning which are central to professional practice in science, finance, and business consulting.

By assembling domain-specific prompts and rubrics evaluated by credentialed experts (e.g., PhDs and MBAs with industry experience), ProfBench aims to measure an LLM’s performance in generating and judging responses that approximate actual professional problem-solving.

2. Dataset Composition and Annotation Methodology

The ProfBench dataset consists of more than 7,000 response-criterion pairs covering 80 unique prompts—20 per domain (Physics PhD, Chemistry PhD, Finance MBA, Consulting MBA). Each prompt is associated with a detailed grading rubric composed of multiple criteria, ensuring granularity in what constitutes a “good” answer.

Annotation proceeds as follows:

• Prompt Ideation: Domain experts craft tasks reflecting real professional demands.
• Rubric Construction: Each prompt spawns a set of explicit criteria capturing facets such as technical accuracy, conceptual integration, clarity, and relevance.
• Response Annotation: Model outputs from advanced LLMs (including systems such as OpenAI o3, Grok, DeepSeek) are judged by annotators against every rubric, using a binary (Yes/No) label for each criterion to facilitate conversion to a two-way decision paradigm.

For aggregation, overall performance metrics such as Macro-F1 are computed over the binary decision labels. Additionally, a bias index is defined to detect and correct self-enhancement bias in LLM judges (models evaluating their own or similar outputs).

Variance in score estimation is formally controlled using the expression:

$$\sigma^2(S) = \frac{1}{N^2} \sum_{i=1}^N \frac{v_i}{n_i}$$

where $v_i$ is the response variance on task $i$, and $n_i$ the number of responses allocated, with optimal allocation solved via dynamic programming.

3. Evaluation Strategy and Bias Mitigation

ProfBench fundamentally integrates robustness and accessibility in its evaluation framework. The use of binary decision tasks (Yes/No for each criterion) aligns the process with Natural Language Inference methodologies, thereby enabling systematic, high-throughput assessment.

A notable technical advancement is the introduction and quantification of the bias index for LLM judges. Self-enhancement bias, wherein models overrate their own answers, is explicitly measured and countered in final analysis, supporting fairness and reproducibility across both proprietary and open-weight models.

Evaluation cost is minimized by automating the judge process with calibrated LLMs, reducing annotation expense by 2–3 orders of magnitude relative to conventional rubric-based approaches (approximate cost of \$12 per evaluation using efficient configurations).

4. Findings: Challenges and Model Performance

Experimental results confirm that ProfBench tasks are difficult even for state-of-the-art LLMs. The highest overall performance observed—65.9%—was by GPT-5 with an explicit high reasoning effort configuration. This outcome signals the substantial complexity inherent in professional-domain benchmarking, where demands include:

• Extraction and synthesis of technical and often multi-part information.
• Accurate, stepwise reasoning (as in scientific problem solving or financial risk assessments).
• The ability to handle, process, and abstract from long, document-like inputs.

A practical challenge is context limitation: grounding documents for some tasks exceeded model input limits and required truncation, further stressing the systems’ capacity for comprehensive synthesis.

5. Performance Disparities and Domain Variability

The benchmark reveals systematic disparities between proprietary and open-weight models. While closed-source LLMs such as GPT-5, OpenAI o3, and Gemini-2.5-Pro generally surpass open models (e.g., by up to 15 percentage points in Finance tasks), this gap is context-sensitive. For extraction and logical validity-focused criteria, open-weight systems sometimes approach proprietary performance levels. This gradient of results is indicative of the differential impact commercial training data and techniques have on complex, high-stakes task domains.

Table 1: Illustrative Performance on ProfBench (selected domains)

Domain	Proprietary Models (e.g. GPT-5)	Open-Weight Models
Physics PhD	High (close to 65.9%)	Moderate-to-Low
Finance MBA	High (up to 15 pts higher)	Lower
Extraction/Logic	Moderate	Approaching proprietary

6. Extended Thinking and LLM Reasoning Depth

A salient insight is the critical role of “extended thinking” in LLM performance. Allowing models to devote more tokens to reasoning—generating detailed, multi-step argumentation—improves accuracy on tasks with layered complexity. For instance, chemistry titration problems or investment plan analyses demand multi-stage inferential traces; models configured for higher reasoning effort demonstrate measurable gains under such conditions.

Extended thinking, in this context, enables LLMs to better simulate expert behavior, navigating intermediate computations and conceptual transitions required by professional tasks.

7. Impact, Accessibility, and Future Research Directions

ProfBench advances professional-domain benchmarking by offering an accessible platform that applies dynamically optimized sample allocation (via explicit variance control), scalable LLM-based judging, and bias mitigation strategies. Its evaluation infrastructure lowers the cost barrier for comprehensive assessment, promoting wider adoption beyond proprietary research labs.

Future directions outlined include:

• Leveraging next-generation LLMs’ increased context windows to enable evaluation on full-length professional documents, obviating the need for truncation.
• Further refinements in optimal sample allocation and response aggregation to improve statistical reliability.
• Expansion of professional task coverage and criterion complexity to better simulate evolving real-world applications.

A plausible implication is that, as ProfBench continues to evolve, it will both catalyze improvements in LLM design for professional applications and reveal nuanced distinctions between model architectures and training regimes in complex, high-stakes domains.

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In summary, ProfBench establishes a robust, multidomain standard for evaluating LLMs in professional settings, integrating expert-authored rubrics, scalable and low-bias evaluation infrastructure, and direct measurement of advanced reasoning capabilities and disparities between proprietary and open-weight model classes. By emphasizing both output generation and judgment, it provides essential signals for model development in scientific, technical, and business applications (Wang et al., 21 Oct 2025).