VQV Signal
The paper explores using agreement between two local large language model judges to better curate merchant-matching training data by distinguishing true no-match cases from teacher abstentions. This approach aims to reduce false no-match labels that contaminate pseudo-labeled data while maintaining...
The paper explores using agreement between two local large language model judges to better curate merchant-matching training data by distinguishing true no-match cases from teacher abstentions. This approach aims to reduce false no-match labels that contaminate pseudo-labeled data while maintaining...
AI-assisted summary based on the listed source.
Merchant matching resolves a noisy payment descriptor to a retrieved merchant entity or returns no match. A key challenge in curating training labels is distinguishing teacher abstention from evidence that no acceptable entity exists: false no-match labels contaminate pseudo-labeled data, while conservative...
Accurate training labels are crucial for improving merchant-matching systems that resolve noisy payment descriptors. Using local LLM judges to gate confidence can enhance data quality without sacrificing label coverage.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to arXiv.
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