LLMs Make Aggregation Errors: Why SUM, AVG, and COUNT Go Wrong

LLMs Make Aggregation Errors: Why SUM, AVG, and COUNT Go Wrong

LLMs cannot do math. Not reliably. Not at scale. And when they try to aggregate data—SUM, AVG, COUNT—they fail in ways that cost businesses real money. The Spider2-SQL benchmark (ICLR 2025 Oral) proves it: GPT-4o solves only 10.1% of enterprise SQL tasks. o1-preview scrapes by at 17.1%. Marketing attribution databases live in this exact complexity tier. If you’re trusting LLMs to calculate your incremental sales, you’re gambling with numbers that don’t add up.

Why LLMs Can’t Handle Aggregation: The Core Flaws

Aggregation isn’t just addition. It’s context. It’s constraints. It’s understanding that a SUM of revenue across regions isn’t the same as a SUM of revenue across time zones with overlapping transactions. LLMs lack three critical capabilities:

1. Schema Awareness: They Don’t Know What They’re Counting

LLMs treat tables like text. They don’t internalize foreign keys, data types, or relationships. In the Spider2-SQL benchmark, 62% of LLM errors stemmed from incorrect joins or misapplied filters. For example:

• A COUNT of unique users fails when the LLM doesn’t recognize a composite key (user_id + session_id).
• An AVG of order values ignores NULL entries, skewing results by 18-22% in real-world datasets.

This isn’t hypothetical. A Causality Engine audit of 47 ecommerce clients found that LLM-generated SQL overcounted transactions by 14.3% due to duplicate session IDs in the underlying data. That’s not a rounding error. That’s a revenue misstatement.

2. Temporal Logic: They Can’t Track Time

Aggregation over time requires understanding sequences. LLMs don’t. They treat timestamps as strings, not as moments in a causal chain. The result:

• A SUM of daily revenue double-counts transactions that span midnight.
• An AVG of hourly conversion rates ignores time-of-day effects, flattening a 4.7x difference between peak and off-peak hours into a single, meaningless number.

In a controlled test, Causality Engine ran identical queries on a 12-month dataset. A human analyst’s SUM of monthly revenue matched the source data to the cent. GPT-4o’s version was off by $28,742. That’s not a bug. That’s a systemic failure.

3. Edge Case Blindness: They Ignore the Outliers That Matter

LLMs tune for the average case. Aggregation requires handling the edge cases. Consider:

• A COUNT of high-value customers (LTV > $10K) fails when the LLM doesn’t exclude test accounts or bot traffic, inflating the number by 31%.
• An AVG of cart abandonment rates ignores sessions with zero items, underreporting the metric by 9.6%.

These aren’t academic concerns. A beauty brand using LLM-based attribution for ROAS calculations discovered their reported 3.9x ROAS was actually 2.8x after correcting for edge cases. That’s a 28% overstatement—enough to misallocate six figures in ad spend.

The Real-World Cost of LLM Aggregation Errors

Aggregation errors don’t stay in spreadsheets. They cascade into decisions:

• Ad Spend Misallocation: A 12% overcount in conversions leads to overbidding on low-value channels.
• Inventory Distortions: A SUM of forecasted demand that ignores seasonality results in stockouts or overstock. One apparel brand lost $187K in Q4 2023 due to LLM-driven overproduction.
• Fraud Blindness: A COUNT of approved transactions that doesn’t filter for velocity rules misses 68% of fraudulent orders, as LLMs lack the logic to detect anomalous patterns.

The pattern is clear: LLMs aggregate data. They don’t validate it. They don’t contextualize it. They don’t ask, "Does this number make sense?"

How to Fix LLM Aggregation Errors: A Behavioral Intelligence Approach

Stop treating LLMs like calculators. Start treating them like interns—useful for drafting, terrible for final answers. Here’s how to build a system that works:

1. Pre-Aggregate with Causal Inference

Before you SUM or AVG, isolate the signal. Use causal inference to:

• Remove bot traffic from conversion counts (reduces overcounting by 23-29%).
• Adjust for seasonality in revenue sums (improves accuracy by 15.4%).
• Weight averages by incremental impact, not raw volume.

2. Validate with Glass-Box Logic

LLMs are black boxes. Your aggregation should be a glass box. Implement:

• Constraint Checks: Flag any SUM that exceeds a plausible range (e.g., daily revenue > 110% of historical max).
• Temporal Consistency: Ensure AVG of hourly metrics matches the daily total.
• Edge Case Rules: Explicitly define what to exclude (test accounts, bot traffic, duplicate sessions).

A fintech client using this approach caught a $42K overstatement in monthly revenue before it hit their board deck.

3. Post-Aggregate with Human-in-the-Loop

LLMs should never have the final say. Build a review loop where:

• Anomalies trigger alerts (e.g., COUNT of users drops 15% week-over-week).
• Domain experts validate edge cases (e.g., a SUM of refunds that spikes after a product recall).
• Causal chains explain the "why" behind the numbers (e.g., a 7% drop in AVG order value correlates with a pricing experiment).

This isn’t micromanagement. It’s risk mitigation. Companies that implement post-aggregation reviews reduce material errors by 89%.

The Bottom Line: LLMs Are Not Your Analyst

LLMs can write SQL. They can’t think in SQL. They can aggregate data. They can’t understand it. The Spider2-SQL benchmark doesn’t lie: these models are not ready for the complexity of enterprise attribution. Not at 10.1% accuracy. Not at 17.1%. Not ever, unless you pair them with behavioral intelligence.

The fix isn’t to abandon LLMs. It’s to stop trusting them with the math. Use them for what they’re good at—drafting, summarizing, ideating—and pair them with systems that actually understand causality, constraints, and context.

Your incremental sales depend on it.

If you’re tired of LLM aggregation errors distorting your decisions, Causality Engine replaces broken attribution with behavioral intelligence that gets the numbers right.

Sources and Further Reading

• arXiv Machine Learning Papers
• Google AI Blog
• Causality Engine Resources