Beyond Similarity: Grounded Agentic Extraction and Expert-Adjudicated Evaluation of Intertextuality in Classical Chinese Histories
Quick Answer
This study introduces a novel agentic extraction method for intertextuality in classical Chinese texts using large language models (LLMs), achieving precision rates between 56%-93% across 12 models.
Quick Take
It validates findings against a benchmark of 2,533 intertextual pairs, revealing a cost spread of 51x at comparable quality. The approach uncovers deeper structural insights beyond similarity scores, demonstrating stability in citation practices over 18 centuries.
Key Points
- Introduces agentic extraction for intertextuality using , enhancing traditional methods.
- Validated against a benchmark of 2,533 intertextual pairs with expert adjudication.
- Achieves precision rates from 56% to 93% across twelve LLMs.
- Demonstrates a 51x cost spread at similar quality levels.
- Reveals stability in citation practices over 18 centuries despite less literal quoting.
Paper Resources
Source Excerpt
Computational approaches to intertextuality have advanced from string matching to neural retrieval, yet their outputs, similarity scores and parallel-passage lists, identify where texts reuse one another without characterizing how or why. We recast fine-grained intertextuality extraction as an agentic task in which a (LLM) reads two text units in full and, through a constrained tool interface, must ground each proposed reuse in exact character spans on both sides and label i
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