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dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
- name: split
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- name: mode
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- name: category
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- name: file_id
dtype: string
- name: ref_count
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- name: language
dtype: string
splits:
- name: train
num_bytes: 3012211
num_examples: 1708
- name: validation
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num_examples: 115
download_size: 887241
dataset_size: 3211930
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Reference Parsing Finetuning Dataset
A fine-tuning dataset for bibliographic reference extraction and parsing, combining LinkedBooks, CEX, and EXCITE datasets into conversation-style examples for LLM SFT.
## Dataset Description
This dataset teaches models to extract and parse bibliographic references from text into structured JSON format. Examples follow a conversational format with system/user/assistant messages, using various prompt variants for diversity.
**Data Sources:**
- **LinkedBooks**: Multi-language reference strings with structured metadata
- **CEX**: English academic papers with TEI XML parsed references
- **EXCITE**: Multi-language academic papers with parsed references
## Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `messages` | list | Conversation messages with `role` (system/user/assistant) and `content` |
| `source` | string | Data source: `linkedbook`, `cex`, or `excite` |
| `split` | string | Dataset split: `train` or `valid` |
| `mode` | string | Example type: `single` (1 reference) or `group` (multiple references) |
| `language` | string | Language code (e.g., `en`, `de`, `fr`) |
| `ref_count` | int | Number of references in the example |
| `file_id` | string\|null | Source document ID (for CEX/EXCITE) |
| `category` | string\|null | Document category (for CEX) |
## Splits
| Split | Examples | Description |
|-------|----------|-------------|
| `train` | ~1,708 | Main training data |
| `valid` | ~115 | Validation set |
**Distribution:**
- ~70% single-reference examples, ~30% multi-reference groups
- ~10-15% LinkedBook, ~30-35% CEX, ~50-55% EXCITE
## Data Creation and Processing
1. **Data Loading**: Loads references from LinkedBooks (Training and Validation JSONL), CEX (JSON + TEI XML), and EXCITE (JSON + XML)
2. **Validation**: Filters invalid references (missing titles/authors, unparsed authors, mismatched counts)
3. **Sampling**: Stratified sampling by category/class (30% train rate for CEX/EXCITE)
4. **Grouping**: Groups references into batches (3-20 refs per group with weighted probabilities)
5. **Prompt Variants**: Applies 5 prompt variants with weighted distribution (40% detailed, 25% minimal, 25% task-based, 5% ultra-minimal, 5% no prompt)
6. **Format Conversion**: Converts to conversation-style format with structured JSON output
## Credits
The dataset is being developed by [Yurui Zhu](https://github.com/RuiaRui) ([Odoma](https://github.com/odoma-ch)). This work is carried out in the context of the EU-funded [GRAPHIA project](https://graphia-ssh.eu/) (grant ID: [101188018](https://cordis.europa.eu/project/id/101188018)). |