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LIFT pipeline improves table extraction with fine-tuned small models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced LIFT, a novel pipeline for improving table extraction from unstructured text. This method first uses a large language model to generate an initial table, followed by a smaller, fine-tuned model that corrects errors. LIFT demonstrates comparable or superior performance to end-to-end fine-tuning on a benchmark of nearly 2,600 tables, particularly excelling when training data is scarce or input variability is high. AI

IMPACT Introduces a more efficient method for table extraction from unstructured text, especially beneficial for limited training data scenarios.

RANK_REASON The cluster contains an academic paper detailing a new method for table extraction using language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LIFT pipeline improves table extraction with fine-tuned small models

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The cluster contains an academic paper detailing a new method for table extraction using language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ashish Tiwari ·

    LIFT: Last-Mile Fine-Tuning for Table Explicitation

    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 2,5…

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