REVIEW 3 major objections 6 minor 4 references
Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE
T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read This paper introduces FLARE, a spreadsheet benchmark, and uses it to show that while LLMs have improved on routine spreadsheet formulas since GPT-3.5, they still fail at logical auditing, error detection, and constraint-satisfaction tasks.
desk verdict Useful provisional benchmark with a robust qualitative finding, but the headline progress claim overreaches the data. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
FLARE is the central object: a modular benchmark built from real-world spreadsheet challenges, including the Wall cost-modelling brief, the Wall and Ball safety-critical engineering task, error-seeded auditing spreadsheets, bank-method interest calculations, rolling averages, deterministic text transformation, BiEntropy computation, and a Latin-square constraint puzzle. Each task is scored from 0 to 1 for partial or full correctness, weighted by complexity (BiEntropy weighs 2.0, Triangle and text processing weigh 1.5), and combined into a weighted GPA leaderboard that is meant to reward partial reasoning while separating template matching from genuine logic.
What would settle it
Run each FLARE task ten times per model with default sampling temperature and blind independent scoring. If any model then detects and fixes all four Triangle spreadsheet errors and solves the astronaut assignment puzzle, the paper's claim that current LLM reasoning over spreadsheet logic is brittle and shallow would be contradicted, because the failures would be attributable to single-shot randomness rather than a reasoning ceiling.
Extended reading notes
Core claim
The paper's central claim, stated in its conclusion, is that while LLM performance on spreadsheet tasks has improved markedly since GPT-3.5, especially in structured and templated domains, the underlying reasoning remains brittle, shallow, and fragile across logic-intensive tasks. Evidence includes the error-seeded Triangle spreadsheet, where most models detected none of the four seeded logic errors; the astronaut assignment puzzle, which no model solved completely; and the bank-method interest task, where only a few models applied the correct 360-day convention. The top model in the weighted leaderboard reached roughly two-thirds of available points, so the best current systems still fall well short of production reliability for financial or engineering spreadsheets.
Load-bearing premise
The benchmark assumes that a single, unaided answer from each model, scored by the author against reference answers, is enough to represent that model's spreadsheet competence; without repeated runs, temperature control, or independent scoring, some leaderboard differences could be noise rather than real ability.
Editorial extensions
If this is right
- LLMs should not be deployed as autonomous spreadsheet generators or auditors in finance, engineering, or safety-critical settings until they can self-check, reason symbolically, and explain their outputs.
- Expect continued strong performance on spreadsheet formulas that resemble training templates, such as common financial calculations and rolling averages, and persistent failures on novel logic, constraint-satisfaction, and error-auditing tasks.
- Error-seeded auditing tasks like the Triangle and Computer Chip Factory spreadsheets expose plausible-but-wrong outputs that formula-generation-only benchmarks miss, so they should be included in future LLM evaluations.
- The FLARE weighted-GPA leaderboard provides a longitudinal measure that can show whether new model generations close the logic gap or merely improve on templated tasks.
Reading between the lines
- A natural next test would be to run the same FLARE tasks with iterative prompting, chain-of-thought, or agent-style decomposition, since the paper deliberately used single-shot responses and explicitly lists interactive use as future work.
- If the pattern of failures holds, spreadsheet risk researchers could build hybrid tools that let an LLM propose formulas but delegate validation to a symbolic solver or constraint checker, converting the paper's main failure mode into a containing boundary.
- The task weights are the author's choices; reweighting auditing tasks more heavily would change the leaderboard, so FLARE scores should be read as reflecting a particular view of spreadsheet competence rather than an absolute ranking.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces FLARE, a spreadsheet-oriented benchmark for LLMs, and evaluates roughly twenty models on six tasks: an error-seeded profit/loss sheet, the Triangle formula-repair task, the Wall and Wall-and-Ball modelling problems, bank-method interest, a text-processing formula, rolling averages, BiEntropy calculations, and an astronaut Latin-square puzzle. Each model is prompted once per task, responses are manually scored with partial credit, and a weighted GPA leaderboard is produced. The paper's central conclusion (Section 3.5) is that LLM spreadsheet performance has improved markedly since GPT-3.5 in structured and templated domains, but that reasoning remains brittle, shallow, and fragile on logic-intensive tasks.
Significance. If the benchmark is taken as a current-model comparison, it is a useful complement to existing spreadsheet benchmarks such as SpreadsheetBench and MiMoTable: it adds error-seeded auditing, symbolic reasoning, domain-specific financial conventions, and constraint-satisfaction tasks that are largely absent from prior work. The qualitative finding that models fail on multi-step logic is well supported by task-level examples, especially the Triangle task (Table 4), Bank Method (Table 5), Text Processing (Table 6), BiEntropy (Table 8), and the Astronaut puzzle (Table 9). The public Google Drive link to prompts and responses is a reproducibility strength, and the paper candidly lists limitations in Section 3.4. However, the temporal progress claim is not supported by the reported data, and the leaderboard's precision is undermined by the single-shot, single-rater methodology and by internal scoring inconsistencies. These issues are fixable but affect the paper's central conclusion.
major comments (3)
- [§3.1, §3.5, Table 10] The claim that LLM performance has 'improved markedly since GPT-3.5' is not supported by any data in the paper. Table 10, the only quantitative leaderboard, contains no GPT-3.5 row; its oldest models are ChatGPT 4 Legacy and Gemini 2.0 Flash. The only GPT-3.5 mention in the results is in Table 2 (Wall and Ball error bands), which is not one of the six weighted tasks and reports no numeric score. Thorne (2023) is cited as the earlier test set, but those earlier responses are not rescored on FLARE tasks, so there is no within-benchmark temporal comparison. Since RQ1 asks exactly this question and the conclusion rests on both conjuncts, the paper should either add a GPT-3.5 baseline (or a rescored baseline from the original responses) or remove the temporal progress claim and state RQ1 as unanswered.
- [§1.8, §3.4, Table 10] The evaluation relies on a single one-shot response per model per task, scored manually by one rater, with no inter-rater reliability check, no temperature control, and no repeated runs. The leaderboard differences in Table 10 are often very small (e.g., 0.542 vs. 0.542, 0.398 vs. 0.390, 0.195 vs. 0.191) and could easily be within run-to-run or rater-judgment noise. Section 3.4 acknowledges that static prompts do not reflect interactive use, but it does not address the variance problem. The paper should either report multiple runs with variance or confidence intervals, provide the raw responses for independent scoring, or explicitly label the leaderboard as an illustrative single-sample comparison rather than a definitive ranking.
- [§2.10, Table 10] The reported summary statistics are internally inconsistent. The text states that Gemini 2.5 Pro has a Weighted GPA of 0.636, but Table 10 shows 0.667; recalculating with the weights in §1.8 (0.125×1.5 + 1.0×1.0 + 0.5×1.5 + 1.0×1.0 + 1.0×2.0 + 0.4×1.0 = 5.3375; divided by 8.0 gives 0.667) confirms the table. The same paragraph says 'the highest average scores were observed in rolling average and bank method tasks,' but the table implies rolling average is clearly the highest (roughly 0.7 average) while bank method has a much lower average (roughly 0.36). These discrepancies undermine the reliability of the task-level summary and should be corrected.
minor comments (6)
- [§1.7.8] There is a typo in 'astronaut specilisation'; it should read 'specialisation'.
- [§2.8, Table 8] The text refers to 'see table 7' when presenting BiEntropy results, but the table is numbered Table 8. The model 'Gemini 2.4 Pro' in Table 8 is inconsistent with 'Gemini 2.5 Pro' used elsewhere in the paper.
- [§2.7, Table 7] Gemini 2.5 Pro appears in both the 'No' and 'Yes' categories in Table 7; if a single model produced both a correct and an incorrect formula, this should be explained or the rows should be split by response variant.
- [Table 10 and Tables 5–6] Model names are inconsistent across tables (e.g., 'ChatGPT-4o mini-high' in Table 10 versus 'ChatGPT o4-mini-high' in Table 5 and 'ChatGPT 04 mini high' in Table 6). Please unify the naming and pin model versions and access dates, since LLM behavior can change between releases.
- [§2.10] The paragraph introducing Table 10 says the benchmark reflects performance across 'five spreadsheet competency areas,' but six tasks are listed and scored; please correct the count.
- [§2.10, Figure 1] Figure 1 is mentioned in the text but does not appear in the manuscript; please include it or remove the citation.
Circularity Check
No significant circularity: FLARE is an empirical benchmark scored against external reference answers; the unsupported 'progress since GPT-3.5' claim is a missing-baseline evidential gap, not a circular derivation.
full rationale
The paper's central empirical finding—that current LLMs produce plausible but incorrect spreadsheet outputs—is directly measured on tasks whose correct answers come from external sources (Panko & Sprague 1998; Irons 2003; Croll 2013; O'Beirne's EuSpRIG triangle challenge). No model output is fed back as a reference answer, and no parameter is fitted to a subset of the data and then renamed as a prediction. The weighted-GPA formula (Section 1.8) is a transparent scoring rule, not a fitted model. The only same-author citation is Thorne (2023), used for task provenance and as background evidence that GPT-3.5 hallucinated on spreadsheet prompts; current per-model scores are not derived from that prior work. The conclusion that performance has 'improved markedly since GPT-3.5' is not supported by the reported leaderboard, which contains no GPT-3.5 row, but that is a missing-evidence/correctness problem, not circularity: the absence of a baseline does not make the derivation equivalent to its inputs. Limitations stated in Section 3.4 (single-shot prompts, interpretive partial-credit scoring) are validity concerns, not circular steps. Because the benchmark is self-contained against external benchmarks and no load-bearing claim reduces by construction to its inputs, the circularity score is 0.
Assumptions & free parameters
free parameters (3)
- Weighted GPA task weights =
Triangle 1.5, Text processing 1.5, BiEntropy 2.0, Bank method 1.0, Rolling average 1.0, Astronaut 1.0
- Wall and Ball error band thresholds =
Aviation <=0.1%, Suitable <=0.5%, Preliminary <=1%, Borderline <=1.5%
- BiEntropy correctness tolerance =
0.01
assumptions (5)
- domain assumption A single one-shot response per model per task is enough to estimate capability.
- domain assumption Manual scoring by the author against reference answers is accurate.
- domain assumption Reference answers from prior literature are correct ground truth.
- domain assumption The chosen tasks represent real-world spreadsheet competence.
- standard math Mathematical and financial background (triangle inequality, Boyle's law, BiEntropy, 360-day year) is standard.
Cite this review
Pith. "Pith review of Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE." pith.science (2026). https://pith.science/paper/GCWLSTE3
@misc{pith2026250617330,
author = {Pith},
title = {Pith review of: Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE},
year = {2026},
howpublished = {\url{https://pith.science/paper/GCWLSTE3}},
note = {Machine review of arXiv:2506.17330}
}
read the original abstract
Large Language Models (LLMs) have demonstrated some significant capabilities across various domains; however, their effectiveness in spreadsheet related tasks remains underexplored. This study introduces a foundation for a comprehensive benchmark framework to evaluate the performance of leading LLMs in executing spreadsheet functions, formula generation and data manipulation tasks. The benchmark encompasses tasks ranging from basic formula creation to complex, real world spreadsheet scenarios. Our findings reveal that while LLMs exhibit proficiency in straightforward tasks, they often falter in complex, multi step operations, frequently producing plausible yet incorrect outputs. These results underscore the limitations of current LLMs in handling spreadsheet tasks that require precise logical reasoning and highlight the need for integrating symbolic reasoning capabilities into LLM architectures. To support this, we introduce FLARE (Formula Logic, Auditing, Reasoning and Evaluation) a new benchmark for evaluating LLM performance on real-world spreadsheet logic, auditing, and reasoning tasks.
Reference graph
Works this paper leans on
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[1]
Baker, K., Powell, S., Lawson, B., & Foster-Johnson, L. (2006). Comparison of Characteristics and Practices . Proceedings of the 6th annual conference for the European Spreadsheets Risks Interest Group (EuSpRIG). London, UK. Chen, J., Rao, L., Lin, Z., & Xu, T. (2025). SheetAgent: A Generalist Agent for Multi-Step Spreadsheet Reasoning. WWW '25 Proceeding...
work page 2006
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[630]
doi:doi: https://doi.org/10.1038/d41586-024-01641-0 Zhang, Y., Li, S., Liu, J., Y u, P., Han, C., Mckeown, K., . . . Ji, H. (2025a). The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination. Arxiv. doi:https://doi.org/10.48550/arXiv.2502.16143 Zhang, Z., Wang, Y ., Wang, C., Chen, J., Zheng, Z., & Yat-sen, S....
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[2023]
ChatGPT and Excel -- trust, but verify
London. Retrieved from https://eusprig.org/wp-content/uploads/2309.00120.pdf Panko, R. (2008). Spreadsheet errors: What we know and what we think we can do. Proceedings of The European Spreadsheets Risks Interest Group annual conference (pp. 9-18). London: The European Spreadsheets Risks Interest Group. doi:https://doi.org/10.48550/arXiv.0802.3457 Panko, ...
work page Pith review arXiv 2008
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[2025]
doi:https://doi.org/10.1145/3696410.3714962 Croll, G. (2013). BiEntropy - The Approximate Entropy of a Finite Binary String. doi:https://doi.org/10.48550/arXiv.1305.0954 Croll, G. (2017). The Reification of an Incorrect and Inappropriate. Proceedings of the annual conference of The European Spreadsheets Risks Interest Group (pp. 63-77). https://doi.org/10...
arXiv 2013
Reviewed August 6, 2026 · model on record in the stance chip above.
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