In this video I test Sparrow table only extraction mode on a large financial statement table. I use a 6 month property management sample data, with 51 rows and 7 columns, including section headers, blank spacer rows and multiple levels of subtotals.
Table only mode uses dots.ocr for table structure detection, combined with Sparrow custom processing logic on top, to produce a JSON array, with column headers mapped as keys. Section headers like INCOME and OPERATING EXPENSES are extracted too, with empty values, this way row order and table structure stay intact.
I go through the extraction result and compare it against the source table.
More parameters doesn't automatically mean better extraction.
Same insurance pivot table as the last video, same generic "*" query, no schema, but this time run through Sparrow's Advanced mode, backed by Gemma 4 31B Dense, instead of Standard mode Ministral 14B.
Result: Gemma 4 31B, despite being over twice the size, was less accurate on this pivot table than Ministral 14B was. It hallucinated on column values — assigning null to cells that had real values, then shifting the actual value into the next column instead. A column-alignment failure Ministral 14B didn't have.
The takeaway: model size isn't a proxy for fit. Structured extraction on dense tabular layouts seems to favor whatever model was better tuned for structure-following, not the model with more parameters. Worth testing both tiers against your actual documents before assuming "Advanced" means "more accurate."
Pivot tables are a genuinely hard case for LLM-based extraction — row/column headers, merged cells, and aggregated values instead of a flat list. In this video I test whether Ministral 14B, running in Sparrow's Standard tier, can handle one.
The document: an insurance pivot table. The query: a generic "*", no predefined schema. Sparrow's UI (sparrow.katanaml.io/process) sends it to Ministral 14B running locally via MLX-VLM, which has to infer the structure itself and return clean, structured JSON.
No schema tuning, no hints file — just upload and extract.
Built with Sparrow — open-source document intelligence platform.
