analiza aiqbench
This research introduces Calibrated Answer Repair (CAR), a method that optimizes the verification and revision process for financial question answering by balancing error recovery against the preservation of correct information. By localizing relevant evidence and limiting edits, CAR significantly reduces token costs while improving accuracy across multiple open-weight models on standard financial datasets. The study highlights critical trade-offs in self-correction mechanisms, demonstrating that targeted checks outperform generic iterative revisions in high-stakes domains like finance.
When Self-Verification Hurts: Calibrated Answer Repair for Financial Question Answering
arXiv:2611.05166v1 Announce Type: new Abstract: Financial question answering can fail even when a model extracts a plausible number from the supplied evidence: the answer may attach it to the wrong entity, period, unit, or accounting concept. Asking the model to verify and revise its draft can help, but open-ended revision may alter content that was already correct or inspect the wrong relation and reinforce the original error. We formulate repair as a decision that balances the information it may recover against the correct content it may damage. Based on this trade-off, we introduce Calibrated Answer Repair (CAR), which first estimates whether a draft is likely to benefit from repair, then selects a targeted check, localizes the relevant evidence or draft span, and limits the permitted edit. Across FinRank, Fin-RATE, and FinQA with three open-weight models, CAR outperforms generic verification in all dataset-model settings. At the system level, it recovers 14.6% of initially missed answer requirements while damaging 2.1% of initially correct ones. Its post-draft token cost is over 30 percent lower than the iterative revision alternatives. Ablations on two dataset-model settings show that evidence localization and bounded editing are the main contributors to CAR's gains.
Abstract:Financial question answering can fail even when a model extracts a plausible number from the supplied evidence: the answer may attach it to the wrong entity, period, unit, or accounting concept. Asking the model to verify and revise its draft can help, but open-ended revision may alter content that was already correct or inspect the wrong relation and reinforce the original error. We formulate repair as a decision that balances the information it may recover against the correct content it may damage. Based on this trade-off, we introduce Calibrated Answer Repair (CAR), which first estimates whether a draft is likely to benefit from repair, then selects a targeted check, localizes the relevant evidence or draft span, and limits the permitted edit. Across FinRank, Fin-RATE, and FinQA with three open-weight models, CAR outperforms generic verification in all dataset-model settings. At the system level, it recovers 14.6% of initially missed answer requirements while damaging 2.1% of initially correct ones. Its post-draft token cost is over 30 percent lower than the iterative revision alternatives. Ablations on two dataset-model settings show that evidence localization and bounded editing are the main contributors to CAR's gains.
Computation and Language (cs.CL)
arXiv:2611.05166 [cs.CL]
(or arXiv:2611.05166v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.05166 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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