grounded-ai
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phi3-hallucination-judge-merge

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 ---
 library_name: transformers
 tags: []
+license: mit
 ---
+## Merged Model Performance
+This repository contains our hallucination evaluation PEFT adapter model.
+### Hallucination Detection Metrics
+Our merged model achieves the following performance on a binary classification task for detecting hallucinations in language model outputs:
+```
+              precision    recall  f1-score   support
+           0       0.85      0.71      0.77       100
+           1       0.75      0.87      0.81       100
+    accuracy                           0.79       200
+   macro avg       0.80      0.79      0.79       200
+weighted avg       0.80      0.79      0.79       200
+```
+### Model Usage
+For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit):
+```python
+def format_input(reference, query, response):
+    prompt = f"""Your job is to evaluate whether a machine learning model has hallucinated or not.
+    A hallucination occurs when the response is coherent but factually incorrect or nonsensical
+    outputs that are not grounded in the provided context.
+    You are given the following information:
+    ####INFO####
+    [Knowledge]: {reference}
+    [User Input]: {query}
+    [Model Response]: {response}
+    ####END INFO####
+    Based on the information provided is the model output a hallucination? Respond with only "yes" or "no"
+    """
+    return input
+text = format_input(query='Based on the follwoing
+          <context>Walrus are the largest mammal</context>
+          answer the question
+          <query> What is the best PC?</query>',
+          response='The best PC is the mac')
+messages = [
+    {"role": "user", "content": text}
+]
+pipe = pipeline(
+    "text-generation",
+    model=base_model,
+    model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16},
+    tokenizer=tokenizer,
+)
+generation_args = {
+      "max_new_tokens": 2,
+      "return_full_text": False,
+      "temperature": 0.01,
+      "do_sample": True,
+  }
+output = pipe(messages, **generation_args)
+print(f'Hallucination: {output[0]['generated_text'].strip().lower()}')
+# Hallucination: yes
+```
+### Comparison with Other Models
+We compared our merged model's performance on the hallucination detection benchmark against several other state-of-the-art language models:
+| Model                 | Precision | Recall | F1     |
+|---------------------- |----------:|-------:|-------:|
+| Our Merged Model      | 0.75      | 0.87   | 0.81   |
+| GPT-4                 | 0.93      | 0.72   | 0.82   |
+| GPT-4 Turbo           | 0.97      | 0.70   | 0.81   |
+| Gemini Pro            | 0.89      | 0.53   | 0.67   |
+| GPT-3.5               | 0.89      | 0.65   | 0.75   |
+| GPT-3.5-turbo-instruct| 0.89      | 0.80   | 0.84   |
+| Palm 2 (Text Bison)   | 1.00      | 0.44   | 0.61   |
+| Claude V2             | 0.80      | 0.95   | 0.87   |
+As shown in the table, our merged model achieves one of the highest F1 scores of 0.81, outperforming several other state-of-the-art language models on this hallucination detection task.
+We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks.
+Citations:
+ Scores from arize/phoenix
 ### Training Data
+@misc{HaluEval,
+  author = {Junyi Li and Xiaoxue Cheng and Wayne Xin Zhao and Jian-Yun Nie and Ji-Rong Wen },
+  title = {HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models},
+  year = {2023},
+  journal={arXiv preprint arXiv:2305.11747},
+  url={https://arxiv.org/abs/2305.11747}
+}
+### Framework versions
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 10
+- training_steps: 150
+### Framework versions
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1