| --- |
| license: cc-by-nc-sa-4.0 |
| datasets: |
| - AimonLabs/HDM-Bench |
| language: |
| - en |
| --- |
| |
| <img src="https://huggingface.co/AimonLabs/hallucination-detection-model/resolve/main/aimon_logo.svg" alt="Aimon Labs Inc" style="background-color: white;" width="400"/> |
|
|
| <img src="https://huggingface.co/AimonLabs/hallucination-detection-model/resolve/main/explainer2.gif" width="400" alt="HDM-2 Explainer"/> |
|
|
| # Model Card for Hallucination Detection Model (HDM-2-3B) |
|
|
| <!-- |
| **Paper:** |
| [](https://arxiv.org/abs/2504.07069) |
| *HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification.* |
|
|
| **Notebook:** [](https://colab.research.google.com/drive/1HclyB06t-wZVIxuK6AlyifRaf77vO5Yz?usp=sharing) |
|
|
| **GitHub Repository:** |
| [](https://github.com/aimonlabs/hallucination-detection-model) |
|
|
| **HDM-Bench Dataset:** |
| [](https\://huggingface.co/datasets/AimonLabs/HDM-Bench) |
|
|
| **HDM-2-3B Model:** |
| [](https://huggingface.co/AimonLabs/hallucination-detection-model) |
| --> |
|
|
| <table> |
| <tr> |
| <td><strong>Paper:</strong></td> |
| <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> |
| </tr> |
| <tr> |
| <td><strong>Notebook:</strong></td> |
| <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> |
| </tr> |
| <tr> |
| <td><strong>GitHub Repository:</strong></td> |
| <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> |
| </tr> |
| <tr> |
| <td><strong>HDM-Bench Dataset:</strong></td> |
| <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> |
| </tr> |
| <tr> |
| <td><strong>HDM-2-3B Model:</strong></td> |
| <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> |
| </tr> |
| <tr> |
| <td><strong>Discord Community:</strong></td> |
| <td><a href="https://discord.gg/MKe6ZkSbWD"><img src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/66e3d80db9971f10a9757c99_Symbol.svg" alt="Discord Logo" /></a></td> |
| </tr> |
| </table> |
| |
|
|
|
|
| ## Introduction |
|
|
| Most judge models used in the industry today are not specialized for Hallucination evaluation tasks. |
| Developers using them often struggle with score inconsistency, variance, high latencies, high costs, and prompt sensitivity. |
| HDM-2 solves these challenges and at the same time, provides industry-first, state-of-the-art features. |
|
|
|
|
| ## Highlights: |
|
|
| - Outperforms existing baselines on RagTruth, TruthfulQA, and our new HDM-Bench benchmark. |
|
|
| - **Context-based** hallucination evaluations based on user-provided or retrieved documents. |
|
|
| - **Common knowledge** contradictions based on widely-accepted common knowledge facts. |
|
|
| - **Phrase, token, and sentence-level** Hallucination identification with token-level probability **scores** |
|
|
| - Generalized model that works well across a variety of domains such as Finance, Healthcare, Legal, and Insurance. |
|
|
| - Operates within a **latency** budget of **500ms** on a single L4 GPU, especially beneficial for Agentic use cases. |
|
|
| ## Model Overview: |
|
|
| 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. |
| HDM-2 introduces a novel taxonomy-guided, span-level validation architecture focused on precision, explainability, and adaptability. |
| 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. |
|
|
| | HDM-2 Model Workflow | Example of Enterprise LLM Response Taxonomy | |
| | --- | --- | |
| |  |  | |
|
|
|
|
| ### Enterprise Models |
|
|
| - 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!! |
|
|
| - Another important feature covered in the Enterprise version are explanations. Please reach out to us for Enterprise licensing. |
|
|
| - 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. |
|
|
| - 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. |
|
|
|
|
| ### Performance - Model Accuracy |
|
|
| See paper (linked on top) for more details. |
|
|
| | | | | | |
| | :---------: | :-----------: | :--------: | :----------: | |
| | **Dataset** | **Precision** | **Recall** | **F1 Score** | |
| | HDMBENCH | 0.87 | 0.84 | 0.855 | |
| | TruthfulQA | 0.82 | 0.78 | 0.80 | |
| | RagTruth | 0.85 | 0.81 | 0.83 | |
|
|
|
|
| ### Latency |
|
|
|
|
| | | | | | | |
| | ----------------------- | -------------------- | ---------------------- | ----------------------- | ------------------- | |
| | **Device** | **Avg. Latency (s)** | **Median Latency (s)** | **95th Percentile (s)** | **Max Latency (s)** | |
| | Nvidia A100 | 0.204 | 0.201 | 0.208 | 1.32 | |
| | Nvidia L4 (recommended) | 0.207 | 0.203 | 0.220 | 1.29 | |
| | Nvidia T4 | 0.935 | 0.947 | 1.487 | 1.605 | |
| | CPU | 261.92 | 242.76 | 350.76 | 356.96 | |
|
|
|
|
|
|
| ## How to Get Started with the Model |
|
|
| Use the code below to get started with the model. |
|
|
| Install the Inference Code |
|
|
| ```bash |
| pip install hdm2 --quiet |
| ``` |
|
|
| Run the HDM-2 model |
|
|
| ```python |
| # Load the model from HuggingFace into the GPU |
| |
| from hdm2 import HallucinationDetectionModel |
| hdm_model = HallucinationDetectionModel() |
| |
| prompt = "You are an AIMon Bot. Give me an overview of the hospital's clinical trial enrollments for Q1 2025." |
| context = """In Q1 2025, Northbridge Medical Center enrolled 573 patients across four major clinical trials. |
| The Oncology Research Study (ORION-5) had the highest enrollment with 220 patients. |
| Cardiology trials, specifically the CardioNext Study, saw 145 patients enrolled. |
| Neurodegenerative research trials enrolled 88 participants. |
| Orthopedic trials enrolled 120 participants for regenerative joint therapies. |
| """ |
| response = """Hi, I am AIMon Bot! |
| I will be happy to help with an overview of the hospital's clinical trial enrollments for Q1 2025. |
| Northbridge Medical Center enrolled 573 patients across major clinical trials in Q1 2025. |
| Heart disease remains the leading cause of death globally, according to the World Health Organization. |
| For more information about our clinical research programs, please contact the Northbridge Medical Center Research Office. |
| Northbridge has consistently led regional trial enrollments since 2020, particularly in oncology and cardiac research. |
| In Q1 2025, Northbridge's largest enrollment was in a neurology-focused trial with 500 patients studying advanced orthopedic devices. |
| Can I help you with something else? |
| """ |
| |
| # Ground truth: |
| # The highest enrollment study had 220 patients, not 573. |
| # This sentence is not in the provided context, and is enterprise knowledge: Northbridge has consistently led regional trial enrollments since 2020, particularly in oncology and cardiac research. |
| |
| # Detect hallucinations with default parameters |
| |
| results = hdm_model.apply(prompt, context, response) |
| ``` |
|
|
| Print the results |
|
|
| ```python |
| # Utility function to help with printing the model output |
| def print_results(results): |
| #print(results) |
| # Print results |
| print(f"\nHallucination severity: {results['adjusted_hallucination_severity']:.4f}") |
| |
| # Print hallucinated sentences |
| if results['candidate_sentences']: |
| print("\nPotentially hallucinated sentences:") |
| is_ck_hallucinated = False |
| for sentence_result in results['ck_results']: |
| if sentence_result['prediction'] == 1: # 1 indicates hallucination |
| print(f"- {sentence_result['text']} (Probability: {sentence_result['hallucination_probability']:.4f})") |
| is_ck_hallucinated = True |
| if not is_ck_hallucinated: |
| print("No hallucinated sentences detected.") |
| else: |
| print("\nNo hallucinated sentences detected.") |
| print_results(results) |
| |
| ``` |
|
|
| ``` |
| OUTPUT: |
| |
| Hallucination severity: 0.9531 |
| |
| Potentially hallucinated sentences: |
| - Northbridge has consistently led regional trial enrollments since 2020, particularly in oncology and cardiac research. (Probability: 0.9180) |
| - In Q1 2025, Northbridge's largest enrollment was in a neurology-focused trial with 500 patients studying advanced orthopedic devices. (Probability: 1.0000) |
| ``` |
|
|
| Notice that |
| - Innocuous statements like *Can I help you with something else?*, and *Hi, I'm an AIMon bot* are not marked as hallucinations. |
| - Common-knowledge statements are correctly filtered out by the common-knowledge checker, even though they are not present in the context, e.g., *Heart disease remains the leading cause of death globally, according to the World Health Organization.* |
| - Statements with enterprise knowledge cannot be handled by this model. Please contact us if you want to use additional capabilities for your use-cases. |
|
|
| To display word-level annotations, use the following code snippet. |
|
|
| ``` |
| from hdm2.utils.render_utils import display_hallucination_results_words |
| |
| display_hallucination_results_words( |
| results, |
| show_scores=False, # True if you want to display scores alongside the candidate words |
| color_scheme="blue-red", |
| separate_classes=True, # False if you don't want separate colors for Common Knowledge sentences |
| ) |
| ``` |
|
|
| Word-level annotations will be displayed as shown below. |
|
|
| - Color tones indicate the scores (darker color means higher score). |
| - Words with red background are hallucinations. |
| - Words with blue background are context-hallucinations but marked as problem-free by the common-knowledge checker. |
| - Words with white background are problem-free text. |
| - Finally, all the candidate sentences (sentences that contain context-hallucinations) are shown at the bottom, together with results from the common-knowledge checker. |
|
|
|  |
|
|
| ### Model Description |
|
|
| - Model ID: HDM-2-3B |
|
|
| - Developed by: AIMon Labs, Inc. |
|
|
| - Language(s) (NLP): English |
|
|
| - License: CC BY-NC-SA 4.0 |
|
|
| - License URL: <https://creativecommons.org/licenses/by-nc-sa/4.0/> |
|
|
| - Please reach out to us for enterprise and commercial licensing. Contact us at info@aimon.ai |
|
|
|
|
| ### Model Sources |
|
|
| - Code repository: [GitHub](https://github.com/aimonlabs/hallucination-detection-model) |
|
|
| - Model weights: [HuggingFace](https://huggingface.co/AimonLabs/hallucination-detection-model/) |
|
|
| - Paper: [arXiv](https://arxiv.org/abs/2504.07069) |
|
|
| - Demo: [Google Colab](https://colab.research.google.com/drive/1HclyB06t-wZVIxuK6AlyifRaf77vO5Yz) |
|
|
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| 1. Automating Hallucination or Inaccuracy Evaluations |
|
|
| 2. Assisting humans evaluating LLM responses for Hallucinations |
|
|
| 3. Phrase, word or sentence-level identification of where Hallucinations lie |
|
|
| 4. Selecting the best LLM with the least hallucinations for specific use cases |
|
|
| 5. Automatic re-prompting for better LLM responses |
|
|
|
|
| ## Limitations |
|
|
| - Annotations of "common knowledge" may still contain subjective judgments |
|
|
|
|
| ## Technical Specifications |
|
|
| See paper for [more details](https://arxiv.org/abs/2504.07069) |
|
|
|
|
| ## Citation: |
|
|
| ``` |
| @misc{paudel2025hallucinothallucinationdetectioncontext, |
| title={HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification}, |
| author={Bibek Paudel and Alexander Lyzhov and Preetam Joshi and Puneet Anand}, |
| year={2025}, |
| eprint={2504.07069}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2504.07069}, |
| } |
| ``` |
|
|
|
|
| ## Model Card Authors |
|
|
| @bibekp, @alexlyzhov-aimon, @pjoshi30, @aimonp |
|
|
|
|
| ## Model Card Contact |
|
|
| <info@aimon.ai>, @aimonp, @pjoshi30 |
|
|
| ## AIMon Website(https://www.aimon.ai) |
