A new approach to evaluating Large Language Models (LLMs) for data extraction involves metamorphic testing, which identifies errors without needing ground truth data. This method involves altering inputs in minor ways (e.g., reordering lines, adding irrelevant text) and checking if the LLM's output remains consistent. While the technique is established and tools like METAL and LLMorph exist, a recent experiment on 535 scanned receipts aimed to determine the actual error rate when these metamorphic tests flag a discrepancy. The study sought to quantify how often a flagged output is genuinely incorrect, which is crucial for efficient human review of LLM-generated data. AI
IMPACT This approach could significantly improve the reliability of LLM-based data extraction in production by providing a way to detect errors without extensive manual labeling.
RANK_REASON The item discusses a research methodology (metamorphic testing) for evaluating LLMs and presents findings from an experiment applying this method. [lever_c_demoted from research: ic=1 ai=1.0]
- GPT-4
- Hermes 2 (Pauly-Wissowa)
- Hyun et al.
- ICDAR-2019 SROIE
- Llama 3
- LLM
- LLMorph
- METAL
- R. Giskard Reventlov
- Ruberto and Terragni
- Towards AI
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