EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks

Amrut Avhad; Danylo Smahliuk; Khaled Denfir; Marcus A. Castro; Pau Arnal

REVIEW 2 major objections 5 minor 31 references

EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks

T0 review · 2 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Frontier AI models fall short of expert judgment on European executive decision tasks, with the strongest model solving only 56.9% of tasks versus 92.4% for expert-written answers.

desk verdict A serious, expensive human-eval benchmark whose headline gap is partly a product of its own difficulty-gate construction; worth refereeing, but the abstract overclaims. read the letter →

arxiv 2608.04549 v2 pith:KNK3NDX6 submitted 2026-08-05 cs.CL cs.AIcs.LG

Pau Arnal , Khaled Denfir , Danylo Smahliuk , Amrut Avhad , Marcus A. Castro This is my paper · ORCID

classification cs.CLcs.AIcs.LG

keywords EuroExecopen-endedevaluationhumanexpertexecutivedecision-makingLLM-as-a-judgebenchmarkchecklistfulfillmentSolveRate

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Frontier large language models are increasingly used for open-ended expert work, yet most benchmarks measure closed tasks with one right answer. This paper introduces EuroExec, a set of 413 long-form European executive decision tasks authored by 47 vetted professionals from real cases, and spends more than 4,000 expert hours evaluating six frontier models with a rubric, item-specific checklists, and preference rankings. The central claim is that the best model solves only 56.9% of tasks, while expert-written reference answers solve 92.4% and are preferred over every model response in 74% of direct comparisons. The paper also argues that only human evaluation, checked statistically, captures the professional standard for this class of work, while deterministic metrics and LLM judges do not.

What carries the argument

The load-bearing mechanism is the joint construction of each question with a checklist of 5 to 10 verb-first requirements, plus a three-instrument human evaluation. A Difficulty Gate filters questions by requiring that two LLMs from different model families fail to auto-fulfill more than 60% of the checklist, with authors rewriting items (on average about 30 attempts) until that threshold holds. The aggregate Solve Rate then combines the mean of five rubric attributes (Domain, Localization, Reasoning, Communication, Actionability) with checklist fulfillment, while explicit preference rankings provide a third independent signal whose internal correlations (Kendall tau between 0.67 and 0.77) support the consistency of the judgment.

What would settle it

A decisive experiment would invite a fresh panel of European executives, not involved in writing the benchmark, to answer a random sample of the 413 items under the same blind evaluation; if their Solve Rate lands near the models' roughly 50% instead of the original experts' 92%, the gap is an artifact of who authored and graded the questions. A cheaper calculation: run the 33 expert-written reference answers through the Difficulty Gate's automatic checklist; if they are auto-fulfilled at high rates on items the models still fail, the gate is not measuring the professional standard.

Watch

Extended reading notes

Core claim

The paper's central discovery is a measured gap: on open-ended, real-case European executive decision tasks with no single golden answer but a recognizable professional standard, frontier LLMs do not meet that standard. Using a generous passing bar (mean rubric score at least 3.0 on a 5-point scale and at least 60% fulfillment of an item-specific checklist), the strongest evaluated model solves 56.9% of the 413 tasks, and the other five range from 51.3% down to 18.4%. On a 33-question subsample, expert-written ideal answers score a 92.4% solve rate and win 74.2% of direct preference rankings, with expert preference over every model significant at p < 0.01. All five evaluation instruments agree on the same model ordering, pairwise differences are significant at p <= 1.26e-6, and even the most lenient evaluators place the top models barely above 50% solve rate.

Load-bearing premise

The load-bearing premise is that the benchmark's filtering step, which discards any question an AI can satisfy more than 60% of the time on an automatic checklist, leaves questions that are representative of real European executive tasks rather than a selection engineered to make AI look worse.

Editorial extensions

If this is right

No evaluated model meets the paper's generous passing bar (rubric at least 3.0 and checklist at least 60%) on more than 56.9% of tasks, so none is ready for unsupervised European executive decision support.
Because expert-written answers are preferred over every model response in 74% of direct rankings and score a 92.4% solve rate, the shortfall reflects a professional-standard gap rather than a quirk of one metric.
The ranking of the six models is consistent across all five instruments and statistically significant at p <= 1.26e-6, so the relative model ordering is stable.
Even the most lenient human evaluators give the top model a solve rate barely above 50%, so the gap is not driven by harsh grading.
ROUGE, BLEU, and a frontier LLM judge fail to reproduce human rankings; the LLM judge overestimates the best models and underestimates the worst, so automatic evaluation is insufficient for this task class.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the Difficulty Gate's 60% automatic-fulfillment cutoff means EuroExec measures performance on questions specifically engineered to resist current LLMs, not a base rate for all executive work.
A testable extension the authors leave implicit would be to fit an LLM judge on the 33 human-ranked answers and check whether the model-human gap narrows; their data suggest naive judges inflate top-model scores.
If the three-instrument protocol generalizes, the same expert-evaluation design could transfer to legal or medical advisory work, whose subjective ground truth has the same professional-standard structure.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things about this paper. First, it is one of the most carefully executed human evaluations of LLMs on open-ended professional tasks that I have seen: 413 expert-authored questions, double expert evaluation with rubric, checklist, and preference ranking, plus real consistency checks. Second, the headline result—56.9% versus 92.4% Solve Rate—is not a clean measure of real-world performance, because the benchmark is deliberately filtered to be hard for LLMs. The authors are transparent about this, but the abstract and conclusion still overgeneralize.

What is genuinely new and good: the EuroExec dataset, the multi-instrument human evaluation protocol, the inter-annotator consistency analysis, the critical-vs-lenient evaluator breakdown, and the comparison with ROUGE/BLEU and an LLM judge. The 33-item subsample with blind human reference answers is a nice idea, though small. The paper also reports a genuinely useful negative: automatic metrics and LLM judges do not track human expert judgment well, and the LLM judge's high correlation with humans is shown to be an artifact of over-variability. That is a solid contribution to the evaluation literature.

Now the soft spots. The Difficulty Gate (Section 2.2) rejects any item with automatic checklist fulfillment above 60% and has authors rewrite until a cheap LLM fails. That means the dataset is selected for items where LLM-checklist matching fails, which inflates the human-model gap. The human experts who wrote the questions and checklists then answer a subset of them, so they are effectively grading from the answer key. The 92.4% expert score is impressive but not a fair baseline for "professional standard" in general. The paper's own Limitations section admits the small subsample and the reproducibility problem, and the authors do not release the data or responses, so the benchmark cannot be used or checked. Figure 2's caption is corrupted in the PDF—the text is replaced by glyph codes—which is a production bug that should have been caught. Finally, the headline Solve Rate is reported without a confidence interval; the resampling analysis is limited to pairwise model rankings.

These flaws do not destroy the internal comparison: among models, the ranking is robust, and the evaluation machinery is credible. But they cap what the paper can honestly claim. A revision should release the benchmark, fix Figure 2, add uncertainty estimates on the headline metric, and reframe the conclusion to say that models fall short on a deliberately hard, expert-curated benchmark—not that they fall short of the professional standard for European executive decisions in general.

This paper deserves a serious referee session, but I would want the data released and the claims reined in before accepting the results at face value.

Referee Report

2 major / 5 minor

Summary. The paper introduces EuroExec, a benchmark of 413 open-ended executive decision tasks authored by 47 European domain experts across Finance, Marketing, Business, and Product, and evaluates six frontier LLMs. Responses are assessed by two independent expert evaluators per item via a five-attribute rubric, an item-specific checklist, and a preference ranking; an aggregate Solve Rate is defined as mean rubric score ≥3.0 with checklist fulfillment ≥60%. The best model (Fable 5) attains 56.9% Solve Rate, while expert-written reference answers on a 33-item subset reach 92.4% and are preferred over all model responses in 74.24% of direct rankings. The paper also analyzes evaluator consistency with Kendall tau and Pearson correlations, compares the methodology against automatic metrics (ROUGE/BLEU and an LLM judge), and concludes that frontier LLMs fall short of professional standards for this class of open-ended work.

Significance. If the reported gap is representative, the paper provides one of the most extensive human-evaluated demonstrations that frontier LLMs are not yet reliable for open-ended executive decision support in European contexts. The study's strengths are substantial: more than 4,000 expert hours, two independent evaluators per response, explicit internal and cross-evaluator consistency checks, critical-versus-lenient evaluator decompositions, resampling-based significance analysis, and transparent appendices with a graded example and examples of rejected questions. The paper also makes a valuable methodological contribution by showing that automatic metrics, including an LLM judge, fail to capture the nuance of expert human evaluation. However, the central claim's generalization to 'real-world' executive tasks is conditional on the representativeness of the benchmark and on the validity of the human reference standard, both of which are challenged by the construction choices discussed below.

major comments (2)

[Section 2.2] The Difficulty Gate makes the benchmark adversarially selected on a proxy of the measured outcome. The pipeline rejects items whose automatic LLM checklist fulfillment exceeds 60%, and instructs authors to rewrite the question, checklist, or both until an automatic response fails the checklist, averaging about 30 attempts per item. Since checklist fulfillment is a direct component of the Solve Rate, the resulting dataset is not an unbiased sample of real European executive tasks; the headline contrast in Table 4 (56.9% vs. 92.4%) reflects a deliberately hardened subset. To support the abstract's claim that models fall 'below the professional standard of work they are already used for,' the authors should either report results on a random or un-gated sample (including items rejected by Automated QA and by the Difficulty Gate) to quantify the selection effect, or explicitly restrict all generalizing claims to 'curated hard items.'
[Appendix A / Section 4] The human expert reference standard is anchored on 33 items whose ideal answers were written by the same experts who authored the corresponding questions and checklists. The 92.4% human Solve Rate and 74.24% win rate therefore partly measure self-consistency between an author's ideal answer and the author's own checklist, not a professional standard that an independent expert would necessarily meet. The small sample also makes this estimate high-variance. To support the 'professional standard' claim, the authors should add independent reference answers from experts not involved in item creation, or demonstrate that a separate expert cohort can achieve comparably high Solve Rates when answering the same items from scratch. The Limitations section honestly acknowledges the 33-item constraint, but the abstract and conclusion do not carry this caveat.

minor comments (5)

[Section 2.2 / Table 2] It would be informative to report how many items were rejected at each validation stage (Automated QA and Difficulty Gate), and to show the full distribution of the automatic fulfillment scores for the final 413 items rather than only binned difficulty counts, so readers can assess attrition and the shape of the selected distribution.
[Section 4] The Solve Rate thresholds (mean rubric ≥3.0, checklist fulfillment ≥60%) and the partial checklist weight of 0.5 are arbitrary; a brief sensitivity analysis over reasonable threshold values would strengthen the conclusion that the model ordering and the large human-model gap are robust to these choices.
[Appendix D] The graded example shows two independent evaluators disagreeing markedly on the same item (e.g., Actionability 5 vs. 2, checklist 2/8 vs. 1.5/8); a sentence explaining how such divergent judgments are reconciled in the aggregate (averaging, adjudication, or other) would improve the reproducibility of the methodology.
[Figure 2] In the manuscript version received, Figure 2's caption and axis labels contain garbled '/uni00000027/...' character sequences; these must be fixed to render proper text.
[General] The paper does not provide a data availability statement or a link to the EuroExec dataset. For a benchmark introduction, a clear statement of intended release (or a reason for withholding) is important for reproducibility and community uptake.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Difficulty Gate is an external-validity threat, not a reduction of the reported results to the selection rule.

full rationale

The derivation chain is: item authorship (Sec. 2.1), automated QA and Difficulty Gate (Sec. 2.2), response generation (Sec. 3), expert rubric/checklist/ranking evaluation (Sec. 4), and aggregation into Solve Rate with consistency checks (Sec. 5). The only step that could appear circular is the Difficulty Gate, which rejects items whose automatic LLM checklist fulfillment exceeds 60% and asks authors to rewrite until an automatic LLM fails the checklist. However, the reported headline metric is not the output of that gate: Solve Rate is defined by human rubric score at least 3.0 and human checklist fulfillment at least 60% (Sec. 4), not by the gate's automatic fulfillment rate. The gate therefore does not force the reported values; Fable 5 actually reaches 61.9% human checklist fulfillment, above the gate's 60% threshold, while still being scored on the selected items. The 33-item human comparison is a separate subsample in which experts wrote ideal answers that were then blindly evaluated; this is a small-sample, answer-author advantage and a benchmark-design concern, but it is not an equation-level reduction of the central conclusion to its inputs. There is no load-bearing self-citation: the Panickssery et al. (2024) citation is external and is used to justify using two different model families as solver and grader. The paper's transparent admission that the Difficulty Gate involved automatic grading does not make the human evaluation redundant. Thus, under the quoted-evidence standard for circularity, the appropriate finding is no significant circularity; the Difficulty Gate is better classified as a threat to external validity than as a self-consistent derivation of the results.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The benchmark's ground truth is expert-authored checklists, and the Difficulty Gate intentionally selects for tasks LLMs struggle with. The threshold choices directly determine the headline Solve Rate, so they are free parameters rather than externally justified constants. No physical or formal entities are introduced.

free parameters (4)

Solve Rate rubric threshold = 3.0 / 5.0
A question counts as solved when the mean rubric score is at least 3.0. This hand-chosen cutoff directly determines the headline Solve Rate and is not externally justified.
Solve Rate checklist threshold = 60%
A question counts as solved when checklist fulfillment is at least 60%. The paper calls this a 'generous passing grade', but changing it changes the headline ranking and gap.
Difficulty Gate maximum auto-fulfillment = 60%
Items pass validation only if the average automatic checklist fulfillment over 5 passes does not exceed 60%. This parameter is the main selector of benchmark hardness and strongly shapes the reported 56.9% result.
Partial checklist weight = 0.5
Partial checklist hits are counted as 0.5 in the fulfillment rate. This weighting affects every checklist fulfillment number and therefore the Solve Rate.

assumptions (5)

domain assumption Checklists authored by experts define the ground truth for what a correct answer must contain.
Section 2.1 states the checklist 'plays the role of a ground truth', but it is self-authored within the same expert community that evaluates responses, with no external validation against actual workplace outcomes.
ad hoc to paper Questions filtered by the Difficulty Gate are representative of real European executive decision tasks.
Section 2.2 retains only items where automatic LLM checklist fulfillment is below 60% after an average of about 30 rewrites; no evidence shows this subset matches the distribution of real-world executive tasks.
domain assumption Expert evaluators can reliably recognize a professional standard despite the absence of a single objective answer.
Sections 4 and 5.3 rely on expert judgment and inter-evaluator correlations, assuming the Likert scale and checklist encode a stable professional standard rather than unverifiable personal preference.
domain assumption The 33-question expert-answer subsample is representative of the full 413-question benchmark.
Appendix A and Section 5 use the 33-item subset to estimate the human Expert Solve Rate and win rate, but the small sample may not mirror the difficulty or content distribution of the full set.
standard math Paired t-tests on rubric scores provide valid ranking significance estimates.
Section 5.1 uses paired t-tests and 500 resamples; the t-test assumes approximately normal paired differences, although the resampling analysis provides some robustness.

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Cite this review

Pith. "Pith review of EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks." pith.science (2026). https://pith.science/paper/KNK3NDX6

@misc{pith2026260804549,
  author       = {Pith},
  title        = {Pith review of: EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KNK3NDX6}},
  note         = {Machine review of arXiv:2608.04549}
}

read the original abstract

Frontier LLMs are increasingly put to use on open-ended complex questions, different in nature from the ones they are typically evaluated on. We dedicate more than 4,000 human expert hours to evaluate a selection of six frontier LLMs on a member of this class of problems: EuroExec, our introduced human expert-based benchmark composed of 413 open-ended long-form European executive tasks authored by 47 vetted domain experts, each question drawn from experience in a real case. Every response is manually evaluated through a multi-attribute rubric, an item-specific checklist of requirements, and a preference rank ordering, extracting an aggregate metric "Solve Rate". The strongest model solves only 56.9% of tasks, while expert-written reference answers judged blindly are solved at near-ceiling levels and are preferred over every model response in 74% of direct rankings, placing frontier generative systems well below the professional standard of work they are already used for. We see that the best way to extract this kind of conclusion is by employing human evaluators, carefully checking their consistency through rigorous statistical analysis, and observe that automatic measurements also fall short when evaluating on this case of real-world open-ended problems with a subjective ground truth.

Figures

Figures reproduced from arXiv: 2608.04549 by the authors.

**Figure 1.** Solve Rate (%) broken down by question difficulty from the Difficulty Gate impressive gap that qualified human experts still have with frontier generative models, especially in the Solve Rate and checklist fulfillment rates. Returning to the comparison among models, Figure 1 contains a breakdown of Solve Rate by difficulty of the questions according to the automatic “difficulty gate” (see Section 2.2). We can see … view at source ↗

**Figure 2.** Rubric scores (mean ± std dev), computed by attribute, aggregated by domain, and overall scores 0 25 50 75 100 125 150 10 −4 10 −3 10 −2 10 −1 Fable 5 > GPT-5.5 GPT-5.5 > Opus 4.8 Opus 4.8 > Gemini 3.1 Pro Gemini 3.1 Pro > GLM-5.2 GLM-5.2 > Mistral Large p = 0.01 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Median p-value (500 resamples) from paired [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Proportion of checklist evaluations within [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Breakdown of annotator pair assessments at the level of individual checklist item, by domain and overall [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Number of evaluators according to their own [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 8.** Figure 8: Cost of generating responses for all 413 ques [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

**Figure 9.** Figure 9: Length of responses against the “Solve Rate” [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

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Reference graph

Works this paper leans on

31 extracted references · 8 canonical work pages

[1]

States a clear recommendation (or a hybrid of two options) and explains why it rejects the other options with 1-2 reasons
[2]

InInternational Conference on Learning Representations

Measuring massive multitask language under- standing. InInternational Conference on Learning Representations. Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, jiayi lei, Yao Fu, Maosong Sun, and Junxian He. 2023. C-eval: A multi-level multi-discipline chinese evaluation suite for found...

work page 2023
[3]

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[4]

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[5]

Quantifies Italy’s 5 year contribution to rev- enue and EBITDA (for the recommended op- tion) and clearly states how it moves the com- pany towards the board’s target of 20% rev- enue growth and 7% EBITDA margin
[6]

for whichever option is recommended)

Estimates final capex for chosen option at the end of each 5 years and is broken down into major components (Italian store openings, Ital- ian distribution network, German automation, etc. for whichever option is recommended)
[7]

Provides a comparison of labor costs impacts between entering Italy compared to a non- 13 entry scenario, including change in total group labor cost

work page
[8]

Quantifies capex and EBITDA at the end of year 5 for chosen option (or hybrid option)

Show all 31 references

[11]

Our covenant ceiling of 4.0x leaves only C360M of immediate debt headroom

Compares revenue, margin, capex, labor costs, EBITDA of chosen option to non-entry alter- native hypothetical Model Response: Situation.We generate C12B in revenue at a 6% EBITDA margin ( C720M), carrying C2.52B of debt (3.5x EBITDA). Our covenant ceiling of 4.0x leaves only C...
[12]

Identifies gap as the need for native Spanish speakers on team in order to conduct business in Spanish culture
[13]

Calculates market size using a bottom-up, not top-down approachC
[14]

Shows a clear ROI of how the 100,000 eu- ros could generate potential revenues for the company
[15]

Identifies risk as needing to deploy their soft- ware in AWS’ Spain region in order to meet ENS security requirements
[16]

C” appended (“bottom-up, not top-down approachC

Identifies the Customer Acquisition Time at a minimum of 30 (20-30 new customers in 1 year is realistic) QA verdict: language. Checklist rows 2, 5 are poorly written. Row 2 has a stray “C” appended (“bottom-up, not top-down approachC”), which appears to be a formatting error m...

2027
[17]

Recommends avoiding personal information
[18]

Integrates the digital menu into a digital order- ing solution in order to facilitate tracking
[19]

Flags TKO as a chain, not needing to prioritize on customer experiences
[20]

Identifies TKO is widely spread across Spain and reaching a consensus amongst all venues is not feasible
[21]

not needing to 15 prioritize customer experience

Proposes integrating regional solutions rather than going national QA verdict: checklist mismatch. Checklist rows 3, 4, 5 test things the prompt never asks for. Row 3 is problematic: it says TKO “not needing to 15 prioritize customer experience” because it is a chain. This is ...
[22]

Rejects Cloud orchestration: rejects the de- ployment, identifying that relying on non- deterministic cloud connections (WebSockets) physical cobot timing sequences introduces catastrophic physical safety risks
[23]

Mandates Deterministic Edge: demands that all kinetic control loops remain on a local op- erational technology (OT) network utilizing real-time industrial protocols (EtherCAT), not standard web protocols
[24]

Proposes Architectural Decoupling: proposes a hybrid pivot: authorizes the serverless cloud tier only for asynchronous anomaly detection, while isolating the real-time cobot actuation entirely to the edge
[25]

p99 of 45ms

Overrides the latency decoy: clarifies that a “p99 of 45ms” is actually unacceptable for in- dustrial motion control, which requires sub – millisecond, highly deterministic jitter toler- ances to prevent physical collisions
[26]

impressive

Overrides the security decoy: notes that TLS 1.3 and zero-trust solve information security, but completely fail to address the operational resilience requirements of the EU machinery directive QA verdict: hint leakage. Checklist items 1, 2, 4, 5 are revealed in the prompt. The...
[27]

Must avoid introducing facts not supported by the scenario or clearly identified as assump- tions. 16
[28]

Should recommend exactly one country and exactly one market entry model without proposing hybrid or sequential approaches
[29]

Should demonstrate that the recommendation remains feasible within the 55 million euros investment cap and hiring limit
[30]

Should apply a consistent decision standard when evaluating both the selected and the re- jected options
[31]

See Table 12 for each individual item’s rate

Should maintain consistency between the rec- ommend, supporting rationale, stated board constraints Average auto-fulfillment rate: 0.96. See Table 12 for each individual item’s rate. Item 1 2 3 4 5 Avg. Fulfillment 1 1 0.8 1 1 Table 12: Average auto-fulfillment rate for every ...
[2021]

Yi Chen, Rui Wang, Haiyun Jiang, Shuming Shi, and Ruifeng Xu

Evaluating large language models trained on code.Preprint, arXiv:2107.03374. Yi Chen, Rui Wang, Haiyun Jiang, Shuming Shi, and Ruifeng Xu. 2023. Exploring the use of large lan- guage models for reference-free text quality evalua- tion: An empirical study. InFindings of the Ass...

2023 arXiv
[2024]

Solve Rate

Llm evaluators recognize and favor their own generations. InAdvances in Neural Information Pro- cessing Systems, volume 37, pages 68772–68802. Curran Associates, Inc. Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation ...

2002 arXiv

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

States a clear recommendation (or a hybrid of two options) and explains why it rejects the other options with 1-2 reasons

[2] [2]

InInternational Conference on Learning Representations

Measuring massive multitask language under- standing. InInternational Conference on Learning Representations. Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, jiayi lei, Yao Fu, Maosong Sun, and Junxian He. 2023. C-eval: A multi-level multi-discipline chinese evaluation suite for found...

work page 2023

[3] [3]

Balances economic and social factors, specif- ically reputation and political risk from job reductions

[4] [4]

Identifies the top 3-5 risks (union backlash, tech underperformance, political pressure, cost overruns, etc.) and gives a concrete miti- gation action for each option)

[5] [5]

Quantifies Italy’s 5 year contribution to rev- enue and EBITDA (for the recommended op- tion) and clearly states how it moves the com- pany towards the board’s target of 20% rev- enue growth and 7% EBITDA margin

[6] [6]

for whichever option is recommended)

Estimates final capex for chosen option at the end of each 5 years and is broken down into major components (Italian store openings, Ital- ian distribution network, German automation, etc. for whichever option is recommended)

[7] [7]

Provides a comparison of labor costs impacts between entering Italy compared to a non- 13 entry scenario, including change in total group labor cost

work page

[8] [8]

Quantifies capex and EBITDA at the end of year 5 for chosen option (or hybrid option)