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arxiv: 2510.18941 · v2 · pith:S6QZIOVAnew · submitted 2025-10-21 · 💻 cs.CL · cs.AI· cs.LG

ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge

Zhilin Wang , Jaehun Jung , Ximing Lu , Shizhe Diao , Ellie Evans , Jiaqi Zeng , Pavlo Molchanov , Yejin Choi
show 2 more authors
Jan Kautz Yi Dong
This is my paper

Pith reviewed 2026-05-21 19:31 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords LLM evaluationprofessional benchmarksrubric scoringLLM judgesmulti-domain tasksexpert knowledgeresponse quality
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The pith

ProfBench creates expert rubrics across physics, chemistry, finance and consulting that expose large gaps in how current LLMs handle professional documents and reports.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ProfBench, a collection of more than 7000 response-criterion pairs drawn from four professional domains and scored by human experts with PhD or MBA credentials. It then constructs LLM-based judges that reduce evaluation cost by two to three orders of magnitude while attempting to limit self-enhancement bias. When these judges are applied, even the strongest model tested reaches only 65.9 percent overall, with clear differences between proprietary and open-weight systems and evidence that extended reasoning helps on complex tasks. A sympathetic reader would see this as evidence that standard benchmarks miss the synthesis and judgment demands of real professional work. The authors therefore position ProfBench as a practical, accessible way to track progress toward models that can reliably process and report on domain-specific material.

Core claim

ProfBench supplies over 7000 human-expert-scored response-criterion pairs in Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA domains, then supplies affordable LLM judges that mitigate self-enhancement bias; when these judges are run on current models the highest score is 65.9 percent, proprietary systems outperform open-weight ones, and extended thinking improves results on the hardest items.

What carries the argument

ProfBench rubrics consisting of response-criterion pairs that require domain-specific synthesis and are scored first by human experts then by the authors' bias-mitigated LLM judges.

If this is right

  • Models will need stronger mechanisms for long-context synthesis and professional judgment before they can be trusted on report-style tasks.
  • Extended chain-of-thought or thinking budgets become a measurable lever for closing the gap on complex professional queries.
  • Open-weight models will continue to trail proprietary ones on these rubrics until training data or alignment techniques close the observed disparity.
  • Evaluation budgets can drop dramatically once reliable LLM judges replace repeated human review.
  • Future benchmarks in additional professional fields can reuse the same rubric-plus-LLM-judge pattern.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the benchmark holds, organizations that rely on LLMs for research summaries or client reports will need human oversight for longer than current accuracy numbers suggest.
  • The rubric format could be adapted to measure whether models can maintain consistency when updating reports after new documents arrive.
  • Performance gaps between model families may widen further when the rubrics are expanded to fields that require quantitative modeling or regulatory reasoning.
  • The cost reduction achieved by the LLM judges makes repeated evaluation during model development feasible for smaller research groups.

Load-bearing premise

The constructed LLM judges can stand in for human experts across these four domains without introducing large distortions from self-enhancement or domain mismatch.

What would settle it

A side-by-side study in which the same set of model outputs is scored both by the authors' LLM judges and by fresh human experts from the same four fields, checking whether agreement rates stay above 80 percent on average.

Figures

Figures reproduced from arXiv: 2510.18941 by Ellie Evans, Jaehun Jung, Jan Kautz, Jiaqi Zeng, Pavlo Molchanov, Shizhe Diao, Ximing Lu, Yejin Choi, Yi Dong, Zhilin Wang.

Figure 1
Figure 1. Figure 1: Example from ProfBench (Finance MBA) is substantially more challenging and detailed [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of rubrics by category. Reasoning dominates (62.9%), with most on logical [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Score Distribution and Optimal Samples for various tasks. Each blue box represents 25th, [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Optimal ProfBench performance at each price-point. OpenAI models are on the Pareto [PITH_FULL_IMAGE:figures/full_fig_p022_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Standard deviation of overall perfor￾mance using multiple samples per task. Optimal allocation of samples consistently reduce the vari￾ance across all budget levels. As expected, the optimal allocation consistently achieves smaller variance compared to heuristic baselines, and we set B to be 160 (i.e. average ni = 4) with N = 40, reducing down the stan￾dard deviation to only 50% of the naive point estimate… view at source ↗
read the original abstract

Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to tasks like mathematics, programming, and short-form question-answering. However, many real-world applications require evaluating LLMs in processing professional documents, synthesizing information, and generating comprehensive reports in response to user queries. We introduce ProfBench: a set of over 7000 response-criterion pairs as evaluated by human-experts with professional knowledge across Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA. We build robust and affordable LLM-Judges to evaluate ProfBench rubrics, by mitigating self-enhancement bias and reducing the cost of evaluation by 2-3 orders of magnitude, to make it fair and accessible to the broader community. Our findings reveal that ProfBench poses significant challenges even for state-of-the-art LLMs, with top-performing models like GPT-5-high achieving only 65.9% overall performance. Furthermore, we identify notable performance disparities between proprietary and open-weight models and provide insights into the role that extended thinking plays in addressing complex, professional-domain tasks. Data: https://huggingface.co/datasets/nvidia/ProfBench and Code: https://github.com/NVlabs/ProfBench and Leaderboard: https://huggingface.co/spaces/nvidia/ProfBench

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. ProfBench introduces a benchmark consisting of over 7000 response-criterion pairs in Physics, Chemistry, Finance, and Consulting domains, with rubrics created by human experts with professional knowledge. The authors develop LLM-Judges to score model responses against these rubrics, claiming to mitigate self-enhancement bias and reduce evaluation costs by 2-3 orders of magnitude. The central empirical claim is that even state-of-the-art models such as GPT-5-high achieve only 65.9% overall performance, with additional findings on proprietary vs. open-weight model gaps and the role of extended thinking. The dataset, code, and leaderboard are released publicly.

Significance. If the LLM-Judges are shown to be reliable proxies for human expert judgment, ProfBench would address a clear gap in current LLM evaluation by targeting professional document processing and report synthesis. The public data release, code, and leaderboard are explicit strengths that enable reproducibility and community use. The work could support more realistic assessment of LLMs on complex, domain-specific tasks beyond mathematics and short-form QA.

major comments (2)
  1. [LLM-Judge Construction and Validation] The abstract and methods description state that LLM-Judges are constructed to evaluate the rubrics while mitigating self-enhancement bias. No domain-specific agreement statistics (Pearson r, Cohen's kappa, or exact-match rate) are reported between LLM-Judge scores and human-expert labels on the >7000 response-criterion pairs. Because the headline 65.9% result for GPT-5-high and the claim that ProfBench poses significant challenges are produced by these judges rather than direct human scoring, the absence of quantified validation is load-bearing for the central performance claims.
  2. [Results] Results section: the overall 65.9% figure and model comparisons should be accompanied by domain-level breakdowns and any available inter-judge or judge-human agreement numbers; without them the disparity claims between proprietary and open-weight models rest on unverified judge reliability.
minor comments (2)
  1. [Abstract] Abstract: the sentence 'response-criterion pairs as evaluated by human-experts' is ambiguous about whether humans scored the model responses or only created the rubrics; a single clarifying clause would improve precision.
  2. [Methods] The cost-reduction claim of 2-3 orders of magnitude is stated without a concrete baseline comparison (e.g., human-expert hours vs. LLM-Judge API cost); adding this detail would strengthen the accessibility argument.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of validation and presentation. We address each major comment below and have revised the manuscript to incorporate the requested details on LLM-Judge reliability and domain-level results.

read point-by-point responses

  1. Referee: [LLM-Judge Construction and Validation] The abstract and methods description state that LLM-Judges are constructed to evaluate the rubrics while mitigating self-enhancement bias. No domain-specific agreement statistics (Pearson r, Cohen's kappa, or exact-match rate) are reported between LLM-Judge scores and human-expert labels on the >7000 response-criterion pairs. Because the headline 65.9% result for GPT-5-high and the claim that ProfBench poses significant challenges are produced by these judges rather than direct human scoring, the absence of quantified validation is load-bearing for the central performance claims.

    Authors: We agree that explicit domain-specific agreement statistics are necessary to substantiate the reliability of the LLM-Judges and the resulting performance claims. The manuscript describes the LLM-Judge construction process, including the use of separate models to reduce self-enhancement bias, but does not report quantitative agreement metrics against human experts. In the revised manuscript we will add a new validation subsection that reports Pearson r, Cohen's kappa, and exact-match rates computed on a representative sample of response-criterion pairs for each domain. These metrics will directly support the 65.9% headline result and the broader claim that ProfBench presents significant challenges. revision: yes

  2. Referee: [Results] Results section: the overall 65.9% figure and model comparisons should be accompanied by domain-level breakdowns and any available inter-judge or judge-human agreement numbers; without them the disparity claims between proprietary and open-weight models rest on unverified judge reliability.

    Authors: We concur that domain-level breakdowns and agreement statistics would strengthen the presentation of results and the interpretation of proprietary versus open-weight model gaps. The revised Results section will include per-domain performance tables for all models evaluated, together with the judge-human agreement numbers referenced in the response to the first comment. This addition will provide a clearer empirical foundation for the reported disparities and the role of extended thinking. revision: yes

Circularity Check

0 steps flagged

No significant circularity; evaluation chain grounded in human-expert labels

full rationale

The paper constructs ProfBench from over 7000 response-criterion pairs directly evaluated by human experts with domain-specific professional knowledge (Physics PhD, Chemistry PhD, Finance/Consulting MBA). LLM-Judges are subsequently developed to approximate these human judgments at lower cost while explicitly mitigating self-enhancement bias. Reported model performances (e.g., GPT-5-high at 65.9%) are obtained by applying the judges to model outputs on the fixed benchmark. No derivation step reduces by construction to its own inputs: there are no self-definitional equations, fitted parameters renamed as predictions, load-bearing self-citations, or ansatzes smuggled via prior work. The chain remains self-contained against the external human-expert benchmark and does not rely on circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the reliability of domain-expert rubrics as ground truth and the effectiveness of the proposed LLM judges in matching human judgment without introducing new biases.

axioms (1)
  • domain assumption Human experts holding Physics PhD, Chemistry PhD, Finance MBA, or Consulting MBA degrees produce reliable and representative rubrics for evaluating LLM responses on professional tasks.
    The benchmark construction and performance numbers depend on these experts creating the 7000+ response-criterion pairs.

pith-pipeline@v0.9.0 · 5807 in / 1109 out tokens · 107115 ms · 2026-05-21T19:31:01.144288+00:00 · methodology

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Forward citations

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  4. Reward Hacking in Rubric-Based Reinforcement Learning

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