LLM-as-a-Judge Scores Are Unreliable Optimization Signals in Closed-Loop Table Recognition
LLM-as-a-judge is widely used to provide feedback and selection signals in closedloop regeneration, but this use remains insufficiently validated. We study it in table recognition, where deterministic TEDS evaluation provides a controlled testbed, using FinTabNet and OmniDocBench. Three findings emerge. First, judge signals were weak on both datasets: scores frequently tied, rankings were not reproducible, and no tested judge score policy, whether selecting among candidates or accepting revisions under a conservative score margin, improved on the first output on both datasets. Iteration produced better candidates, but the judge recovered them at most partially on one dataset and not at al...
| Authors | Donghwan Kim |
|---|---|
| Primary category | cs.CL |
| Published | 15 July 2026 |
| Last updated at source | 16 July 2026 |
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