Building conversational AI that can generate a technically correct response is relatively straightforward. Building one that responds appropriately when a user is frustrated, grieving, exhausted, lonely, or emotionally overwhelmed is considerably harder.
At AntEngage, we have been exploring this problem while developing emotionally intelligent conversational AI.
As part of that work, we are releasing the AntEngage Empathy Conversation Dataset, an open dataset containing 4,008 AI-synthesized multi-turn conversations with 79,616 dialogue turns.
The dataset is intended for researchers and developers working on conversational AI, NLP, large language models, empathetic dialogue, affective computing, and instruction tuning.
Dataset at a glance
- 4,008 multi-turn conversations
- 79,616 user and assistant dialogue turns
- 19.9 average turns per conversation
- Up to 20 turns per conversation
- Language: English
- Formats: JSONL and CSV
- Data source: AI-synthesized
- Real user data: None
- Personally identifiable information: None
The dataset contains conversations around situations where conversational systems often need more than a generic response.
Examples include:
- grief and anticipatory loss
- caregiver burnout
- loneliness
- emotional exhaustion
- workplace stress
- parenting challenges
- social isolation
- guilt and emotional numbness
- hospital and ICU waiting
- end-of-life situations
Why multi-turn empathy?
Empathy in conversational AI is not simply a sentiment classification problem.
A model may recognize that a message is negative while still producing a poor response.
Consider a user saying:
I don't know how I'm supposed to go home and act normal.
A conversational system needs to understand more than whether this sentence expresses sadness.
It needs to maintain context, acknowledge the emotional state appropriately, avoid making unsupported assumptions, and continue the conversation naturally.
That becomes even more challenging across 10, 15, or 20 dialogue turns.
This is why the dataset focuses on multi-turn interactions rather than isolated prompt-response pairs.
How the dataset was generated
The conversations were produced using a structured synthetic data generation pipeline.
The process broadly consisted of four stages.
1. Domain sampling
We selected broad empathy-related domains and emotional situations to encourage diversity across conversations.
2. Scenario generation
Specific situations were generated within each domain rather than repeatedly generating conversations from a small collection of generic prompts.
3. Multi-turn dialogue generation
The scenarios were expanded into conversations containing alternating user and assistant turns.
4. Automated quality verification
Generated conversations were passed through automated checks intended to evaluate characteristics including coherence, naturalness, diversity, and safety.
The result is a collection of 4,008 conversations containing 79,616 dialogue turns.
Dataset structure
The primary representation is JSONL.
Each record represents an entire conversation and follows a structure similar to:
{
"id": "empathy_000001",
"source": "AntEngage-v2",
"language": "en",
"domain": "empathy",
"num_turns": 10,
"conversation": [
{
"role": "user",
"content": "..."
},
{
"role": "assistant",
"content": "..."
}
]
}
A flattened CSV representation is also available for users who prefer tabular analysis.
The CSV contains one row per dialogue turn with fields for the conversation identifier, turn index, role, content, and total conversation length.
Potential uses
We believe the dataset can be useful for several areas of research and development:
- LLM instruction tuning
- empathetic dialogue generation
- conversational AI
- dialogue system evaluation
- affective computing
- human-AI interaction research
- response generation experiments
- conversational model benchmarking
When creating training, validation, and test sets, we recommend splitting at the conversation level rather than the individual dialogue-turn level. Otherwise, turns from the same conversation could appear in both training and evaluation data.
Privacy and ethical considerations
An important characteristic of this release is that the conversations are synthetic.
The dataset does not contain conversations collected from AntEngage customers or real users, and it does not contain personally identifiable information.
Some conversations discuss emotionally sensitive subjects such as grief, loneliness, caregiver burnout, and end-of-life situations. The dataset should therefore be treated as research data for conversational AI rather than as clinical guidance.
It is not intended to replace professional medical or mental health advice.
Where to get the dataset
The dataset is openly available through multiple research and developer repositories.
GitHub:
https://github.com/AntEngage/datasets
Hugging Face:
https://huggingface.co/datasets/antengage/empathy-conversations
Zenodo:
https://doi.org/10.5281/zenodo.21789317
Kaggle:
https://doi.org/10.34740/KAGGLE/DS/11526980
AntEngage:
https://antengage.com/datasets
The Zenodo record provides a persistent DOI for academic citation.
Citation
If you use the dataset in research, benchmarks, applications, or other public work, please cite:
Pradhan, D. (2026).
AntEngage Empathy Conversation Dataset (Version 1.0.0) [Data set].
AntEngage Technology Private Limited.
https://doi.org/10.5281/zenodo.21789317
What's next?
This release is part of our broader work around the AntEngage Language Model (AELM) and emotionally intelligent conversational AI.
We are interested in seeing how the community uses the dataset for fine-tuning, evaluation, benchmarking, and conversational AI research.
If you experiment with the dataset, find problems in it, build something with it, or have ideas for improving future releases, we'd be interested in hearing about your results.
The dataset and related resources are available through the AntEngage open datasets repository.
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