vanta-research/human-ai-collaboration-2

---
license: apache-2.0
tags:
- human-ai-collaboration
- human-ai-collaboration-2
- LoRA
- problem-solving
- conversational-ai
- vanta-research
- reasoning-datasets
- thinking-models
- collaboration
- warm
- friendly
- chat
task_categories:
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---

<div align="center">


<h1>VANTA Research</h1>

<p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</p>

<p>
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<a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
</p>
</div>

---

# Human-AI Collaboration-2

This dataset is an expansion of our previous release, *[human-ai-collaboration-1](https://huggingface.co/datasets/vanta-research/human-ai-collaboration-1).* This dataset contains the entirety of *human-ai-collaboration-1,* and expands on it further by adding over twice as many collaborative examples than before.

*Note: It's not recommended to use both datasets simultaneously as there is overlap between the two. Choose 1 or 2 depending on your needs.*

**Dataset Size:** 6,324 entries
**License:** Apache 2.0
**Author:** VANTA Research

---

## Overview

This dataset captures authentic human-AI collaboration patterns across diverse domains including:

- **Conversational Problem-Solving:** Multi-turn dialogues tackling complex questions and creative challenges
- **Knowledge Synthesis:** Combining human expertise with AI capabilities to explore topics in depth
- **Reasoning Transparency:** Clear explanation of thought processes, uncertainty acknowledgment, and collaborative exploration
- **Domain Diversity:** Technical topics, creative writing, scientific reasoning, strategic thinking, and more

Each entry is a carefully curated example of effective human-AI collaboration, optimized for training models that can:
- Engage in substantive dialogue
- Acknowledge and explore uncertainty
- Synthesize information from multiple perspectives
- Maintain context and coherence across exchanges
- Collaborate productively toward shared understanding

---

## Dataset Structure

Each entry in the dataset follows a standard conversational format with **thinking tags** that reveal the AI's reasoning process:

```json
{
"messages": [
{
"role": "user",
"content": "Can you help me optimize this Python function for processing large datasets?"
},
{
"role": "assistant",
"content": "<think>\nThe user needs performance optimization help. I should:\n1. Analyze their current approach for bottlenecks\n2. Suggest concrete improvements with explanations\n3. Ask clarifying questions about their use case\n4. Provide multiple solutions at different complexity levels\n</think>\n\nI'd be happy to help optimize that! To give you the best recommendations, could you share:\n- The current function code\n- Typical dataset size you're working with\n- Any specific performance bottlenecks you've noticed?\n\nIn the meantime, here are common optimization strategies..."
}
]
}
```

### Think Tags

A distinctive feature of this dataset is the inclusion of **`<think>` tags** that contain the AI's internal reasoning process before generating the visible response. These tags:

- Show strategic planning and approach selection
- Reveal uncertainty acknowledgment and decision-making
- Demonstrate consideration of user context and needs
- Provide insight into collaborative reasoning patterns

**Training Tip:** You can choose to keep or remove think tags depending on your use case:
- **Keep them** to train models on transparent reasoning and planning
- **Remove them** if you only want the final conversational outputs

### Data Format

- **Format:** JSONL (JSON Lines - one JSON object per line)
- **Character Encoding:** UTF-8
- **Line Endings:** Standard newlines (`\n`)

### Field Descriptions

| Field | Type | Description |
|-------|------|-------------|
| `messages` | Array | Conversation turn containing user and assistant exchanges |
| `role` | String | Either "user" or "assistant" |
| `content` | String | The actual message content (may include `<think>` tags for assistant responses) |

---

## Key Characteristics

### Quality Attributes

- ✓ **Transparent Reasoning:** Internal `<think>` tags reveal planning, strategy selection, and uncertainty
- ✓ **Coherent:** Responses maintain logical consistency and relevance
- ✓ **Substantive:** Meaningful depth in exploration and explanation
- ✓ **Collaborative:** Genuine dialogue advancing shared understanding
- ✓ **Diverse:** Wide range of topics, styles, and interaction patterns

### Scope of Data

The dataset spans:
- Multiple domains (STEM, humanities, creative, strategic)
- Various conversation lengths (2-turn exchanges to extended dialogues)
- Different reasoning styles (analytical, creative, exploratory, systematic)
- Mixed levels of topic complexity (accessible to specialized)

---

## Usage

### Loading the Dataset

**Python with Hugging Face Datasets:**
```python
from datasets import load_dataset

dataset = load_dataset("huggingface", "vanta-research/human-ai-1962")
```

**Raw JSONL Loading:**
```python
import json

entries = []
with open("human-ai-1962.jsonl", "r", encoding="utf-8") as f:
for line in f:
entries.append(json.loads(line))
```

**Processing Think Tags:**
```python
import re

def extract_thinking(content):
"""Extract think tags and visible response separately."""
think_match = re.search(r'<think>(.*?)</think>', content, re.DOTALL)
thinking = think_match.group(1).strip() if think_match else None
visible = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
return thinking, visible

def remove_think_tags(content):
"""Remove think tags entirely for training without reasoning."""
return re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()

# Example usage
with open("human-ai-1962.jsonl", "r", encoding="utf-8") as f:
for line in f:
entry = json.loads(line)
for msg in entry["messages"]:
if msg["role"] == "assistant":
thinking, response = extract_thinking(msg["content"])
print(f"Internal reasoning: {thinking}")
print(f"Visible response: {response}")
```

**With PyTorch:**
```python
from torch.utils.data import Dataset

class HumanAIDataset(Dataset):
def __init__(self, jsonl_path):
self.data = []
with open(jsonl_path, "r", encoding="utf-8") as f:
for line in f:
self.data.append(json.loads(line))

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
return self.data[idx]
```

### Recommended Applications

1. **Reasoning Transparency Training:** Leverage `<think>` tags to train models that show their work
2. **Language Model Fine-Tuning:** Train models on substantive dialogue patterns (with or without reasoning tags)
3. **Chain-of-Thought Learning:** Use internal reasoning to improve model planning capabilities
4. **Dialogue Evaluation:** Benchmark conversational AI systems on collaborative interactions
5. **RLHF Datasets:** Use as reference examples for preference learning
6. **Instruction Following:** Learn nuanced response generation patterns

---

## Statistics

- **Total Entries:** conversations
- **Average Entry Length:** ~200-400 tokens per exchange
- **Language:** English
- **Minimum Quality Threshold:** All entries reviewed for coherence and relevance

---

## License

This dataset is released under the **Apache License 2.0**. See LICENSE file for full details.

### Apache 2.0 Summary

The Apache License 2.0 permits:
- ✓ Commercial use
- ✓ Modification
- ✓ Distribution
- ✓ Private use

With the conditions:
- License and copyright notice must be included
- State significant changes made to the code/data

---

## Ethical Considerations

### Dataset Limitations

- Reflects conversation patterns as of training data cutoff
- May contain perspectives that are not universally shared
- Represents primarily English-language interactions
- Domain coverage is non-uniform (some topics more represented than others)

### Responsible Use

We encourage users to:
1. Consider downstream application impacts
2. Test for biases relevant to your use case
3. Maintain transparency about dataset usage
4. Report concerns or improvements to maintainers

---

## Citation

If you use this dataset in research or production, please cite:

```bibtex
@dataset{vanta-research-human-ai-collaboration-2,
title={Human-AI Collaboration-2},
author={VANTA Research},
year={2025},
url={https://huggingface.co/datasets/vanta-research/human-ai-collaboration-2}
}
```

---

## Data Quality & Curation

Each entry in this dataset has been:
- ✓ Validated for JSON structural integrity
- ✓ Verified for conversation coherence
- ✓ Checked for substantive content
- ✓ Reviewed for diversity of interaction patterns

Entries are designed to exemplify high-quality human-AI collaboration, making them suitable for training models to engage in similarly productive exchanges.

---

## Getting Started

1. **Download:** Access the dataset from Hugging Face Hub
2. **Explore:** Start with a small subset to understand structure
3. **Integrate:** Use provided code examples to load into your pipeline
4. **Fine-tune:** Apply to your specific task with domain-appropriate training procedures
5. **Evaluate:** Benchmark your results against baseline models

---

## Contributing & Feedback

### Reporting Issues

If you find data quality issues, format problems, or licensing concerns, please open an issue on the project repository.

### Improvements & Suggestions

We welcome feedback on:
- Data completeness
- Format usability
- Documentation clarity
- Suggested use cases

---

## Changelog

### Version 1.0 (2025)
- Initial release with 3,050 curated entries
- JSONL format standardization
- Comprehensive documentation

---

# Contact

- Organization: [e-mail]
- Engineering/Design: [e-mail]

---

**Disclaimer:** This dataset is provided as-is for research and development purposes. Users are responsible for evaluating the dataset's suitability for their specific applications.

---

**License:** Apache License 2.0 | **Repository Owner:** VANTA Research | **Initial Commit:** November 2025

*VANTA Research: Building safe, resilient AI models optimized for human-AI collaboration*