---
library_name: peft
license: mit
tags:
  - lora
  - verification
  - policy_compliance
  - factchecking
  - instruction_following
  - safeguards
  - guardrail
  - guard
  - hallucination
base_model:
- akjindal53244/Llama-3.1-Storm-8B
---

![NAVI Logo](assets/logo.svg)

# NAVI verifiers (Verification Intelligence)

![NAVI Cover](assets/cover.jpg)

NAVI (Verification Intelligence) is a hallucination detection safety model designed primarily for policy alignment verification. It reviews various types of text against documents and policies to identify non-compliant or violating content. Optimized for enterprise applications requiring compliance verification for automated text generation, NAVI supports lengthy and complex documents. To push policy verification in the open-source community, we release NAVI-small-preview, an open-weights version of the model we have deployed on the platform. NAVI-small-preview is centered around verifying specifically assistant outputs against policy documents. The full solution is accessible via the [NAVI platform and API](https://naviml.com/).

The chart below illustrates NAVI's strong performance on Policy Alignment Verification test set, with the full model achieving an F1 score of 90.4%, outperforming all competitors. NAVI-small-preview also demonstrates impressive results, providing an open-source option with significant improvements over baseline models while maintaining reliable policy alignment verification.

![Results](assets/results.png)

- **Developed by:** Nace.AI
- **Model type:** Safety model, Policy Alignment Verifier
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** akjindal53244/Llama-3.1-Storm-8B

## Uses

### Direct Use

NAVI-small-preview is used for verifying chatbot/assistant/agents outputs with company policy documents. It processes policies and identifies any contradictions, inconsistencies and violations.

### Downstream Use

Policy checks for accuracy critical LLM Apps. Good suite for Enterprise Environment.  

### Out-of-Scope Use

NAVI is not designed for general-purpose factuality verification or tasks unrelated to policy compliance.

## Bias, Risks, and Limitations

While NAVI excels in policy compliance verification, it may face challenges with unrealistic policy scenarios or contexts outside its training data scope.

### Recommendations

We recommend to use the model only as a generative classifier outputting the class label, any other outputs are not accounted for during training.

## How to Get Started with the Model

The verifier takes the assistant response and document context as inputs to generate a classification label. There are three possible classes: Compliant, Noncompliant, and Irrelevant. For long enterprise documents, we recommend setting up chunk-based vector search for selecting most relevant chunks from the document. 

For inferencing the model we recommend using vLLM==0.6.3.post1, inferencing with Transformers gives suboptimal performance. Below are the prompts and sample code to launch:

```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

tokenizer = AutoTokenizer.from_pretrained(
    'akjindal53244/Llama-3.1-Storm-8B', padding_side="left")

llm = LLM(model='akjindal53244/Llama-3.1-Storm-8B',     
          enable_lora=True, max_model_len=4096, max_lora_rank=16, seed=42)

lora_request = LoRARequest(
    'navi-small-preview', 1,
    lora_path='nace-ai/navi-small-preview')

sampling_params = SamplingParams(
    temperature=0.0,
    max_tokens=3,
    stop=["<|eot_id|>"]
)

template = """Determine if the given passage adheres strictly to the provided policy. Respond with one word: Compliant or Noncompliant.
Don't output anything else or use any other words. Deduct where the passage violates the policy with one word: Noncompliant or Compliant.

{context}

- Passage:
{response}

- Label:"""

context = "The return policy is 90 days, except for electronics, which is 30 days."
response = "The return policy is 90 days for all items."

messages = [{'role': 'user', 'content': template.format(context=context, response=response)}]
formatted_input = target_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = llm.generate([formatted_input], sampling_params, lora_request=lora_request)[0]
print(output.outputs[0].text)
```

```text
Noncompliant
```

For simplified handling of long documents we suggest implementing vector search among document chunks and including top 5 enumerated policy chunks.

## Training Details

### Training Data

NAVI was trained on a mix of real-world policy documents and synthetic interactions between a user and an assistant, it includes diverse, realistic, and complex policy examples across multiple industries.

### Training Procedure

#### Preprocessing

NAVI utilizes latest advances in Knowledge Augmentation and Memory in order to internatlize document knowledge. However, NAVI-small-preview was trained to be able to work with simple vector retrieval.

#### Training Hyperparameters

- **Training regime:** We perform thorough hyperparameter search during finetuning. The resulting model is a Lora adapter that uses all linear modules for all Transformer layers with rank 16, alpha 32, learning rate 5e-5, effective batch size 32. Trained with 8 A100s for 6 epochs using Pytorch Distributed Data Parallel.

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

We curated the Policy Alignment Verification (PAV) dataset to evaluate diverse policy verification use cases, releasing a public subset of 125 examples spanning six industry-specific scenarios: AT&T, Airbnb, Cadence Bank, Delta Airlines, Verisk, and Walgreens. This open-sourced subset ensures transparency and facilitates benchmarking of model performance. We evaluate our models and alternative solutions on this test set.

#### Factors

Evaluation focuses on policy compliance within multi-policy, multi-document contexts.

#### Metrics

F1 score for "Noncompliant" class was used to measure performance, prioritizing detection of noncompliance cases.

### Results

The table below shows performance of models evaluated on the public subset of PAV dataset. NAVI-small-preview achieved an F1 score of 86.8%, outperforming all tested alternatives except full-scale NAVI. We evaluate against general-purpose solutions like Claude and Open AI models, as well as some guardrails focusing on groundedness to demonstrate a clear distinction of policy verification from the more common groundedness verification.

| Model                 | F1 Score (%) | Precision (%) | Recall (%) | Accuracy (%) |
|-----------------------|--------------|---------------|------------|--------------|
| Llama-3.1-Storm-8B    | 66.7         | 86.4          | 54.3       | 69.6         |
| NAVI-small-preview    | 86.8         | 80.5          | 94.3       | 84.0         |
| NAVI                  | **90.4**     | **93.8**      | **87.1**   | **89.6**     |
| Sonnet 3.5            | 83.2         | 85.1          | 81.4       | 81.6         |
| GPT-4o                | 80.5         | 73.8          | 88.6       | 76.0         |
| AWS Bedrock Guardrail | 74.8         | 87.1          | 65.6       | 67.2         |
| Azure Groundedness    | 75.0         | 62.3          | 94.3       | 64.8         |
| NeMo (GPT-4o)         | 69.0         | 67.2          | 70.9       | 72.0         |

## Model Card Contact

[NAVI Contact Page](https://naviml.com/contact)