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--- |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- AimonLabs/HDM-Bench |
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language: |
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- en |
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--- |
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<img src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXf_XGI0bexqeySNP6YA-yzUY-JRfNNM9A5p4DImWojxhzMUfyZvVu2hcY2XUZPXgPynBdNCR1xen0gzNbMugvFfK37VwSJ9iim5mARIPz1C-wyh3K7zUInxm2Mvy9rL7Zcb7T_3Mw?key=x9HqmDQsJmBeqyuiakDxe8Cs" alt="Aimon Labs Inc" style="background-color: white;" width="400"/> |
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<img src="https://huggingface.co/AimonLabs/hallucination-detection-model/" width="400" alt="HDM-2 Explainer"/> |
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# Model Card for Hallucination Detection Model (HDM-2-3B) |
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<!-- |
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**Paper:** |
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[](https://arxiv.org/abs/2504.07069) |
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*HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification.* |
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**Notebook:** [](https://colab.research.google.com/drive/1HclyB06t-wZVIxuK6AlyifRaf77vO5Yz?usp=sharing) |
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**GitHub Repository:** |
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[](https://github.com/aimonlabs/hallucination-detection-model) |
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**HDM-Bench Dataset:** |
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[](https\://huggingface.co/datasets/AimonLabs/HDM-Bench) |
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**HDM-2-3B Model:** |
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[](https://huggingface.co/AimonLabs/hallucination-detection-model) |
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--> |
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<table> |
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<tr> |
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<td><strong>Paper:</strong></td> |
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<td><a href="https://arxiv.org/abs/2504.07069"><img src="https://img.shields.io/badge/arXiv-2504.07069-b31b1b.svg" alt="arXiv Badge" /></a> <em>HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification.</em></td> |
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</tr> |
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<tr> |
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<td><strong>Notebook:</strong></td> |
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<td><a href="https://colab.research.google.com/drive/1HclyB06t-wZVIxuK6AlyifRaf77vO5Yz?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Badge" /></a></td> |
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</tr> |
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<tr> |
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<td><strong>GitHub Repository:</strong></td> |
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<td><a href="https://github.com/aimonlabs/hallucination-detection-model"><img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white" alt="GitHub Badge" /></a></td> |
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</tr> |
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<tr> |
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<td><strong>HDM-Bench Dataset:</strong></td> |
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<td><a href="https://huggingface.co/datasets/AimonLabs/HDM-Bench"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg" alt="HF Dataset Badge" /></a></td> |
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</tr> |
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<tr> |
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<td><strong>HDM-2-3B Model:</strong></td> |
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<td><a href="https://huggingface.co/AimonLabs/hallucination-detection-model"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-md-dark.svg" alt="HF Model Badge" /></a></td> |
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</tr> |
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</table> |
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## Introduction |
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Most judge models used in the industry today are not specialized for Hallucination evaluation tasks. |
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Developers using them often struggle with score inconsistency, variance, high latencies, high costs, and prompt sensitivity. |
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HDM-2 solves these challenges and at the same time, provides industry-first, state-of-the-art features. |
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## Highlights: |
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- Outperforms existing baselines on RagTruth, TruthfulQA, and our new HDM-Bench benchmark. |
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- **Context-based** hallucination evaluations based on user-provided or retrieved documents. |
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- **Common knowledge** contradictions based on widely-accepted common knowledge facts. |
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- **Phrase, token, and sentence-level** Hallucination identification with token-level probability **scores** |
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- Generalized model that works well across a variety of domains such as Finance, Healthcare, Legal, and Insurance. |
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- Operates within a **latency** budget of **500ms** on a single L4 GPU, especially beneficial for Agentic use cases. |
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## Model Overview: |
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HDM-2 is a modular, production-ready, multi-task hallucination (or inaccuracy) evaluation model designed to validate the factual groundedness of LLM outputs in enterprise environments, for both **contextual** and **common knowledge** evaluations. |
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HDM-2 introduces a novel taxonomy-guided, span-level validation architecture focused on precision, explainability, and adaptability. |
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The figure below shows the workflow (on the left) in which we determine whether a certain LLM response is hallucinated or not and an example (on the right) that shows the taxonomy of an LLM response. |
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HDM-2 Model Workflow | Example of Enterprise LLM Response Taxonomy |
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--- | --- |
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### Enterprise Models |
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- The Enterprise version offers a way to incorporate “Enterprise knowledge” into Hallucination evaluations. This means knowledge that is specific to your company (or domain or industry) that might not be present in your context!! |
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- Another important feature covered in the Enterprise version are explanations. Please reach out to us for Enterprise licensing. |
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- Other premium capabilities that will be included in the Enterprise version include improved accuracies, even lower latencies, and additional use cases such as Math and Code. |
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- Apart from Hallucinations, we have SOTA models for Prompt/Instruction adherence, RAG Relevance, Reranking (Promptable). The instruction adherence model is general-purpose and extremely low-latency. It performs well with a wide variety of instructions, including safety, style, and format constraints. |
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### Performance - Model Accuracy |
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See paper (linked on top) for more details. |
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| :---------: | :-----------: | :--------: | :----------: | |
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| **Dataset** | **Precision** | **Recall** | **F1 Score** | |
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| HDMBENCH | 0.87 | 0.84 | 0.855 | |
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| TruthfulQA | 0.82 | 0.78 | 0.80 | |
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| RagTruth | 0.85 | 0.81 | 0.83 | |
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### Latency |
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| ----------------------- | -------------------- | ---------------------- | ----------------------- | ------------------- | |
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| **Device** | **Avg. Latency (s)** | **Median Latency (s)** | **95th Percentile (s)** | **Max Latency (s)** | |
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| Nvidia A100 | 0.204 | 0.201 | 0.208 | 1.32 | |
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| Nvidia L4 (recommended) | 0.207 | 0.203 | 0.220 | 1.29 | |
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| Nvidia T4 | 0.935 | 0.947 | 1.487 | 1.605 | |
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| CPU | 261.92 | 242.76 | 350.76 | 356.96 | |
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Join our Discord server for any questions around building reliable RAG, LLM, or Agentic Apps: |
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## AIMon GenAIR (https://discord.gg/yXZRnBAWzS) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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Install the Inference Code |
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```bash |
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pip install hdm2 --quiet |
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``` |
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Run the HDM-2 model |
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```python |
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# Load the model from HuggingFace into the GPU |
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from hdm2 import HallucinationDetectionModel |
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hdm_model = HallucinationDetectionModel() |
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prompt = "Explain how the heart functions" |
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context = """ |
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The heart is a muscular organ that pumps blood throughout the body. |
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It has four chambers: two atria and two ventricles. |
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""" |
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response = """The heart is a vital six-chambered organ that pumps blood throughout the human body. |
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It contains three atria and three ventricles that work in harmony to circulate blood. |
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The heart primarily runs on glucose for energy and typically beats at a rate of 20-30 beats per minute in adults. |
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Located in the center-left of the chest, the heart is protected by the ribcage. |
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The average human heart weighs about 5 pounds and will beat approximately 2 million times in a lifetime. |
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""" |
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# Ground truth: |
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# Hearts have 4 chambers (not 6), have 2 atria and 2 ventricles (not 3 each), |
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# normal heart rate is 60-100 BPM (not 20-30), |
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# average heart weighs ~10 oz (not 5 pounds), |
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# and beats ~2.5 billion times (not 2 million) in a lifetime |
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# Detect hallucinations with default parameters |
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results = hdm_model.apply(prompt, context, response) |
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``` |
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Print the results |
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```python |
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# Utility function to help with printing the model output |
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def print_results(results): |
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#print(results) |
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# Print results |
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print(f"\nHallucination severity: {results['adjusted_hallucination_severity']:.4f}") |
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# Print hallucinated sentences |
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if results['candidate_sentences']: |
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print("\nPotentially hallucinated sentences:") |
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is_ck_hallucinated = False |
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for sentence_result in results['ck_results']: |
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if sentence_result['prediction'] == 1: # 1 indicates hallucination |
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print(f"- {sentence_result['text']} (Probability: {sentence_result['hallucination_probability']:.4f})") |
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is_ck_hallucinated = True |
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if not is_ck_hallucinated: |
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print("No hallucinated sentences detected.") |
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else: |
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print("\nNo hallucinated sentences detected.") |
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print_results(results) |
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``` |
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``` |
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OUTPUT: |
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Hallucination severity: 0.9844 |
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Potentially hallucinated sentences: |
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- The heart is a vital six-chambered organ that pumps blood throughout the human body. (Probability: 0.9102) |
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- It contains three atria and three ventricles that work in harmony to circulate blood. (Probability: 1.0000) |
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- The heart primarily runs on glucose for energy and typically beats at a rate of 20-30 beats per minute in adults. (Probability: 0.9844) |
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``` |
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### Model Description |
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- Model ID: HDM-2-3B |
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- Developed by: AIMon Labs, Inc. |
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- Language(s) (NLP): English |
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- License: CC BY-NC-SA 4.0 |
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- License URL: <https://creativecommons.org/licenses/by-nc-sa/4.0/> |
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- Please reach out to us for enterprise and commercial licensing. Contact us at info@aimon.ai |
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### Model Sources |
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- Code repository: [GitHub](https://github.com/aimonlabs/hallucination-detection-model) |
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- Model weights: [HuggingFace](https://huggingface.co/AimonLabs/hallucination-detection-model/) |
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- Paper: [arXiv](https://arxiv.org/abs/2504.07069) |
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- Demo: [Google Colab](https://colab.research.google.com/drive/1HclyB06t-wZVIxuK6AlyifRaf77vO5Yz) |
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## Uses |
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### Direct Use |
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1. Automating Hallucination or Inaccuracy Evaluations |
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2. Assisting humans evaluating LLM responses for Hallucinations |
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3. Phrase, word or sentence-level identification of where Hallucinations lie |
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4. Selecting the best LLM with the least hallucinations for specific use cases |
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5. Automatic re-prompting for better LLM responses |
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## Limitations |
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- Annotations of "common knowledge" may still contain subjective judgments |
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## Technical Specifications |
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See paper for [more details](https://arxiv.org/abs/2504.07069) |
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## Citation: |
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``` |
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@misc{paudel2025hallucinothallucinationdetectioncontext, |
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title={HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification}, |
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author={Bibek Paudel and Alexander Lyzhov and Preetam Joshi and Puneet Anand}, |
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year={2025}, |
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eprint={2504.07069}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2504.07069}, |
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} |
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``` |
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## Model Card Authors |
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@bibekp, @alexlyzhov-aimon, @pjoshi30, @aimonp |
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## Model Card Contact |
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<info@aimon.ai>, @aimonp, @pjoshi30 |
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## AIMon Website(https://www.aimon.ai) |
