---
license: mit
task_categories:
- text-generation
- text-classification
- conversational
language:
- hu
- et
- fi
tags:
- customer-support
- synthetic-data
- multilingual
- conversation
- cross-lingual
- finno-ugric
- gpt-4-1-mini
size_categories:
- 100K<n<1M
---

# Controlled Generated Conversations: gpt-4.1-mini

## Dataset Description

This dataset contains **synthetic customer support conversations** generated using **gpt-4.1-mini** as part of research on cross-lingual stability of LLM judges. The conversations are designed for evaluating how well language models maintain consistent performance across different languages, with a focus on Finno-Ugric languages (Estonian, Finnish, Hungarian) and English.

### Dataset Summary

- **Languages**: Hungarian, Estonian, Finnish
- **Domain**: Customer support conversations
- **Generator Model**: gpt-4.1-mini
- **Total Messages**: 259,985
- **Total Conversations**: 30,000
- **Generation Method**: Controlled synthetic generation with structured parameters

### Supported Tasks

- **Cross-lingual evaluation**: Compare model performance across languages
- **Conversation analysis**: Study dialogue patterns and structures
- **Quality assessment**: Evaluate linguistic quality (grammar, fluency, coherence)
- **Label recovery**: Classify conversation characteristics (industry, problem type, etc.)
- **Ranking stability**: Analyze consistency of model rankings across languages

## Languages

### Hungarian (hu)

- **Messages**: 79,928
- **Conversations**: 10,000
- **Avg. conversation length**: 8.0 messages
- **Conversation length range**: 1-18 messages
- **Industries covered**: 46
- **Problem types**: 20
### Estonian (et)

- **Messages**: 100,800
- **Conversations**: 10,000
- **Avg. conversation length**: 10.1 messages
- **Conversation length range**: 4-18 messages
- **Industries covered**: 46
- **Problem types**: 20
### Finnish (fi)

- **Messages**: 79,257
- **Conversations**: 10,000
- **Avg. conversation length**: 7.9 messages
- **Conversation length range**: 4-18 messages
- **Industries covered**: 46
- **Problem types**: 20

## Dataset Structure

### Data Instances

Each row represents a **single message** within a conversation, allowing for granular analysis of dialogue turns:

```json
{
  "conversation_id": "gpt-4.1-mini_et_20260127-173115_0000",
  "message_id": 0,
  "from_name": "Klient",
  "from_type": "customer",
  "message": "Tere! Minu tellimus pidi juba kohal olema...",
  "subject": "Saatmise hilinemine tellimusele",
  "model": "gpt-4.1-mini",
  "language": "et",
  "industry": "Music & Audio",
  "problem": "Shipping delay",
  "channel": "chat",
  "agent_experience": "senior",
  "agent_type": "bot"
}
```

### Data Fields

#### Core Message Fields
- `conversation_id` (string): Unique identifier for the conversation
- `message_id` (int): Position of message within conversation (0-indexed)
- `from_name` (string): Speaker name (e.g., "Customer", "Agent 1")
- `from_type` (string): Speaker role ("customer" or "agent")
- `message` (string): The actual message content

#### Conversation Metadata
- `subject` (string): Conversation topic/title
- `model` (string): Generator model used
- `language` (string): Language code (et, fi, hu, en-us)

#### Generation Parameters
- `industry` (string): Business domain (40+ categories)
- `problem` (string): Issue type (20+ categories)
- `channel` (string): Communication channel (email, chat)
- `agent_experience` (string): Agent level (junior, senior)
- `agent_type` (string): Agent type (human, bot)
- `n_messages` (int): Total messages in conversation
- `n_agents` (int): Number of agents involved

#### File Provenance
- `file_model` (string): Model from source filename
- `file_language` (string): Language from source filename
- `file_datetime` (string): Generation timestamp from filename

### Data Splits

This dataset uses a single `train` split containing all conversations. For evaluation purposes, users can create their own splits based on:
- Language (cross-lingual evaluation)
- Industry/problem type (domain adaptation)
- Conversation length (complexity analysis)

## Dataset Creation

### Curation Rationale

This dataset was created to study **cross-lingual stability** of language models in evaluation tasks. Finno-Ugric languages (Estonian, Finnish, Hungarian) were specifically chosen as they are:
- Morphologically complex with rich inflectional systems
- Less represented in training data compared to English
- Suitable for testing model robustness across linguistic diversity

### Source Data

#### Data Collection
Conversations were synthetically generated using structured prompts with controlled parameters:

1. **Industry Selection**: Random sampling from 40+ business domains
2. **Problem Generation**: 20+ customer support issue types
3. **Channel & Agent**: Varied communication contexts
4. **Language**: Native speaker patterns for each target language

#### Data Processing
- **Message-level structuring**: Each conversation split into individual messages
- **Unique ID generation**: Traceable identifiers linking messages to conversations
- **Metadata preservation**: All generation parameters maintained for analysis
- **Quality validation**: Automatic filtering for completeness and format

### Annotation Process

No human annotation was performed. All labels represent the **controlled generation parameters** used to create each conversation, enabling evaluation of how well models can recover the original intent.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset enables research into:
- **Language equity**: Understanding model performance disparities across languages
- **Evaluation fairness**: Developing more inclusive assessment methods
- **Multilingual capabilities**: Improving cross-lingual transfer in NLP systems

### Discussion of Biases

**Limitations:**
- **Synthetic nature**: May not capture all nuances of real customer support
- **Generation model bias**: Inherits biases from the generator model (gpt-4.1-mini)
- **Language representation**: Quality may vary based on model's training data per language
- **Cultural context**: May not reflect local business practices or communication styles

**Mitigation strategies:**
- Use alongside real conversation data when possible
- Consider cultural and linguistic context in analysis
- Validate findings across multiple generator models

### Other Known Limitations

- **Temporal coverage**: Generated at a single point in time
- **Domain scope**: Limited to customer support scenarios
- **Generator dependency**: Results tied to specific model capabilities
- **Language variety**: Standard varieties only, no dialectal variation

## Additional Information

### Dataset Curators

Created by the Cross-lingual Stability of LLM Judges research project.

### Licensing Information

Licensed under MIT License. See LICENSE file for details.

### Citation Information

If you use this dataset in your research, please cite:

```bibtex
@misc{chung2026crosslingualstabilityllmjudges,
      title={Cross-Lingual Stability of LLM Judges Under Controlled Generation: Evidence from Finno-Ugric Languages}, 
      author={Isaac Chung and Linda Freienthal},
      year={2026},
      eprint={2602.02287},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.02287}, 
}
```

### Contributions

Thanks to the contributors of this dataset and the broader research community working on cross-lingual NLP evaluation.

## Statistics

### Dataset Overview

| Metric | Value |
|--------|--------|
| **Total Languages** | 3 |
| **Total Messages** | 259,985 |
| **Total Conversations** | 30,000 |
| **Average Messages per Conversation** | 8.7 |
### Per-Language Statistics

| Language | Messages | Conversations | Avg. Length |
|----------|----------|---------------|-------------|
| Hungarian (hu) | 79,928 | 10,000 | 8.0 |
| Estonian (et) | 100,800 | 10,000 | 10.1 |
| Finnish (fi) | 79,257 | 10,000 | 7.9 |

## Usage Examples

### Loading the Dataset

```python
from datasets import load_dataset

# Load specific language subset
dataset = load_dataset("isaacchung/controlled-generated-convos-gpt-4.1-mini", "et")
print(f"Estonian subset: {len(dataset)} messages")

# Load all subsets
dataset = load_dataset("isaacchung/controlled-generated-convos-gpt-4.1-mini")
print(f"Available languages: {list(dataset.keys())}")

# Access conversation metadata
et_data = dataset["et"]
first_msg = et_data[0]
print(f"Industry: {first_msg['industry']}")
print(f"Problem: {first_msg['problem']}")
```

### Basic Analysis

```python
# Group messages by conversation
conversations = {}
for msg in dataset["et"]:
    conv_id = msg["conversation_id"]
    if conv_id not in conversations:
        conversations[conv_id] = []
    conversations[conv_id].append(msg)

# Analyze conversation lengths
lengths = [len(conv) for conv in conversations.values()]
print(f"Avg conversation length: {sum(lengths)/len(lengths):.1f} messages")
```

### Cross-lingual Analysis

```python
# Compare industry distribution across languages
for lang in dataset.keys():
    industries = [msg["industry"] for msg in dataset[lang]]
    unique_industries = set(industries)
    print(f"{lang}: {len(unique_industries)} unique industries")
```