---
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: source
    dtype: string
  - name: split
    dtype: string
  - name: mode
    dtype: string
  - name: category
    dtype: string
  - name: file_id
    dtype: string
  - name: ref_count
    dtype: int64
  - name: language
    dtype: string
  splits:
  - name: train
    num_bytes: 3012211
    num_examples: 1708
  - name: validation
    num_bytes: 199719
    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)).