Papers › LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured...
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
Jiapeng Wang, Lianwen Jin, Kai Ding
Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure. Code and model are publicly available at https://github.com/jpWang/LiLT.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Image Classification | RVL-CDIP | LiLT[EN-R]BASE | Accuracy | 95.68% | #5 of 31 | Archive leaderboard | report |
| Key Information Extraction | CORD | LILT | F1 | 96.07 | #7 of 9 | Archive leaderboard | report |
| Key-value Pair Extraction | RFUND-EN | LiLT ([EN-R]_base) | key-value pair F1 | 54.33 | #8 of 13 | Archive leaderboard | report |
| Key-value Pair Extraction | RFUND-EN | LiLT ([InfoXLM]_base) | key-value pair F1 | 52.18 | #10 of 13 | Archive leaderboard | report |
| Key-value Pair Extraction | SIBR | LiLT ([InfoXLM]_base) | key-value pair F1 | 72.76 | #5 of 7 | Archive leaderboard | report |
| Semantic entity labeling | FUNSD | LILT | F1 | 88.41 | #10 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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