用小模型修复大模型提取的表格，少样本下更准更稳
LIFT: Last-Mile Fine-Tuning for Table Explicitation
- 分两步：大模型初提表，小模型修正错误
- 仅需1000样本即超越端到端微调，最高提升0.144 TEDS
- 适合数据少或输入格式多变场景，抗干扰强
我们提出最后阶段微调（Lift），一种新流程：预训练大语言模型从非结构化剪贴板文本中提取初始表格，再由微调的小语言模型（1B-24B参数）修复错误。在包含2596张表格的基准测试中，Lift在基于树编辑距离的相似性（TEDS）指标上达到或超过端到端小模型微调的效果，且仅需1000个训练样本——此时相比端到端微调最高提升0.144 TEDS点。该方法对输入格式变化更具鲁棒性。与自调试和端到端微调对比表明，当训练数据有限或需兼顾准确性与输入稳定性时，最后阶段微调是优选方案。
原文摘要 · Abstract (English)
We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard text, and a fine-tuned small language model (1B-24B parameters SLM) repairs errors in the extracted table. On a benchmark of 2,596 tables from three datasets, Lift matches or exceeds end-to-end SLM fine-tuning on tree-edit-distance-based similarity (TEDS) metric while requiring as little as 1,000 training examples - where it outperforms end-to-end fine-tuning by up to 0.144 TEDS points. We term this approach last-mile fine-tuning and show it also more robust to input format variability. Comparisons with self-debug and end-to-end fine-tuning approaches show that last-mile fine-tuning provides an attractive option when training data is limited or when robustness to input variation is sought without compromising on accuracy.
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