Using LLMs to Repair Excel Formulas: A New Benchmark for Error Correction

Excel’s power has always rested on formulas, but even seasoned analysts know the frustration of debugging broken logic. Circular references, mismatched parentheses, and incorrect ranges can derail hours of work. In 2025, large language models (LLMs) are becoming essential not just for generating formulas, but for repairing them intelligently. A new benchmark is emerging around this capability, focused on how reliably LLMs can detect, explain, and fix Excel formula errors. At Cell Fusion Solutions Inc., we see this as one of the most practical and immediate applications of AI in everyday spreadsheet workflows.

The Problem with Formula Debugging in Excel

Even simple models can accumulate complexity quickly. Analysts often chain multiple IF statements, combine lookups with aggregation, or nest dynamic arrays. When something breaks, Excel’s default error messages—#REF!, #N/A, #VALUE!—provide little guidance.

The real challenge is not spotting that a formula failed, but diagnosing why. Was the reference range misaligned? Did the logic create an unintended circular dependency? Was the wrong function applied altogether? Traditional debugging requires painstaking manual checks and often trial-and-error.

How LLMs Approach Error Repair

LLMs add value by interpreting formulas as both syntax and intent. When given a broken formula, they can parse the structure, infer the user’s likely goal, and propose corrections. For example:

• A miswritten formula:

=SUMIF(A:A,"Revenue",C:C,D:D)

• Excel will throw an error without much explanation.

• An LLM, however, recognizes that SUMIF only accepts a single sum range. It would suggest:

=SUMIFS(D:D,A:A,"Revenue",C:C,"<>")

…and explain why this change works.

This interpretability—linking corrections to clear reasoning—makes LLMs more powerful than static formula wizards.

The Benchmark for Formula Error Correction

To measure this capability, a new benchmark for Excel error correction is being developed, structured around three pillars:

1. Detection Accuracy – Can the model identify exactly where the formula is breaking?

2. Correction Reliability – Does the suggested fix work as intended, without introducing new issues?

3. Explanation Clarity – Can the model articulate why the fix is correct in plain, actionable terms?

Test cases span from simple syntax errors (e.g., missing parentheses) to advanced logic corrections (e.g., misapplied XLOOKUP criteria).

Practical Use Cases in Finance and Operations

Error correction is not just a convenience—it directly impacts productivity and decision-making. In financial modeling, an unnoticed formula error can distort valuations or lead to compliance risks. In operational dashboards, broken references can misstate KPIs.

LLM-powered repair helps teams:

• Accelerate reporting cycles by removing manual debugging bottlenecks.

• Reduce audit risks by providing transparent explanations of formula changes.

• Enable non-technical users to maintain complex models without relying solely on Excel experts.

At Cell Fusion Solutions Inc., we’re already piloting LLM-based error correction workflows for clients who manage large-scale Excel models across finance, infrastructure, and operations.

Raising the Standard for AI in Excel

The significance of this benchmark extends beyond fixing formulas. It represents a broader trend toward AI-assisted trustworthiness in spreadsheets. If professionals can rely on LLMs not only to generate but also to repair logic accurately, adoption of AI-powered workflows will accelerate dramatically.

The future of Excel will not be error-free, but it will be error-resilient. Organizations that adopt LLM repair tools early will minimize downtime, improve reporting accuracy, and empower their teams to focus on analysis rather than troubleshooting.

At Cell Fusion Solutions Inc., we believe benchmarking LLMs on error correction is critical to building confidence in AI as a dependable partner in Excel workflows.