truthtorchlm 0.0.0
TruthTorchLM is an open-source library designed to detect and mitigate hallucinations in text generation models. The library integrates state-of-the-art methods, offers comprehensive benchmarking tools across various tasks, and enables seamless integration with popular frameworks like Huggingface and LiteLLM.
Requires Python: >=3.10

# TruthTorchLM: A Comprehensive Library for Hallucination Detection in LLMs  

**TruthTorchLM** is an open-source library designed to detect and mitigate hallucinations in text generation models. The library integrates state-of-the-art methods, offers comprehensive benchmarking tools across various tasks, and enables seamless integration with popular frameworks like Huggingface and LiteLLM.

---

## Features  

- **State-of-the-Art Methods**: Implementations of advanced hallucination detection techniques.  
- **Evaluation Tools**: Benchmark hallucination detection methods using various metrics like AUROC, PRR, and Accuracy.  
- **Calibration**: Normalize and calibrate truth values for interpretable and comparable hallucination scores.  
- **Integration**: Seamlessly works with Huggingface and LiteLLM.  
- **Long-Form Generation**: Adapts detection methods to handle long-form text generations effectively.  
- **Extendability**: Provides an intuitive interface for implementing new hallucination detection methods.  

---

## Installation  

Install TruthTorchLM using pip:  

```bash
pip install TruthTorchLM
```

---

## Quick Start  

### Setting Up a Model  

You can define your model and tokenizer using Huggingface or specify an API-based model:  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import TruthTorchLM as ttlm
import torch

# Huggingface model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-chat-hf", 
    torch_dtype=torch.bfloat16
).to('cuda:0')
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", use_fast=False)

# API model
api_model = "gpt-4o"
```

### Generating Text with Truth Values  

TruthTorchLM generates messages with a truth value, indicating whether the model output is hallucinating or not. Various methods (called **truth methods**) can be used to detect hallucinations. Each method can have different algorithms and output ranges. Lower truth values generally suggest hallucinations. This functionality is mostly useful for short-form QA:
```python
# Define truth methods
lars = ttlm.truth_methods.LARS()
confidence = ttlm.truth_methods.Confidence()
self_detection = ttlm.truth_methods.SelfDetection(number_of_questions=5)
truth_methods = [lars, confidence, self_detection]
```
```python
# Define a chat history
chat = [{"role": "system", "content": "You are a helpful assistant. Give short and precise answers."},
        {"role": "user", "content": "What is the capital city of France?"}]
```
```python
# Generate text with truth values (Huggingface model)
output_hf_model = ttlm.generate_with_truth_value(
    model=model,
    tokenizer=tokenizer,
    messages=chat,
    truth_methods=truth_methods,
    max_new_tokens=100,
    temperature=0.7
)
# Generate text with truth values (API model)
output_api_model = ttlm.generate_with_truth_value(
    model=api_model,
    messages=chat,
    truth_methods=truth_methods
)
```

### Calibrating Truth Methods  
Truth values for different methods may not be directly comparable. Use the `calibrate_truth_method` function to normalize truth values to a common range for better interpretability

```python
model_judge = ttlm.evaluators.ModelJudge('gpt-4o-mini')
calibration_results = ttlm.calibrate_truth_method(
    dataset='trivia_qa',
    model=model,
    truth_methods=truth_methods,
    tokenizer=tokenizer,
    correctness_evaluator=model_judge,
    size_of_data=1000,
    max_new_tokens=64
)
```

### Evaluating Truth Methods  

We can evaluate the truth methods with the `evaluate_truth_method` function. We can define different evaluation metrics including AUROC, AUPRC, AUARC, Accuracy, F1, Precision, Recall, PRR:  

```python
results = ttlm.evaluate_truth_method(
    dataset='trivia_qa',
    model=model,
    truth_methods=truth_methods,
    eval_metrics=['auroc', 'prr'],
    tokenizer=tokenizer,
    size_of_data=1000,
    correctness_evaluator=model_judge,
    max_new_tokens=64
)
```

---

## Available Hallucination Detection Methods  

- **LARS**: [Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs](https://arxiv.org/pdf/2406.11278).  
- **Confidence**: [Uncertainty Estimation in Autoregressive Structured Prediction](https://openreview.net/pdf?id=jN5y-zb5Q7m). 
- **Entropy**:[Uncertainty Estimation in Autoregressive Structured Prediction](https://openreview.net/pdf?id=jN5y-zb5Q7m).  
- **SelfDetection**: [Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method](https://arxiv.org/pdf/2310.17918). 
- **AttentionScore**: [LLM-Check: Investigating Detection of Hallucinations in Large Language Models](https://openreview.net/pdf?id=LYx4w3CAgy). 
- **CrossExamination**: [LM vs LM: Detecting Factual Errors via Cross Examination](https://arxiv.org/pdf/2305.13281). 
- **EccentricityConfidence**: [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://arxiv.org/pdf/2305.19187). 
- **EccentricityUncertainty**: [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://arxiv.org/pdf/2305.19187).
- **GoogleSearchCheck**: [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://arxiv.org/pdf/2307.13528). 
- **Inside**: [INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection](https://openreview.net/pdf?id=Zj12nzlQbz). 
- **KernelLanguageEntropy**: [Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities](https://arxiv.org/pdf/2405.20003). 
- **MARS**: [MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs](https://aclanthology.org/2024.acl-long.419.pdf). 
- **MatrixDegreeConfidence**: [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://arxiv.org/pdf/2305.19187). 
- **MatrixDegreeUncertainty**: [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://arxiv.org/pdf/2305.19187).
- **MultiLLMCollab**: [Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration](https://arxiv.org/pdf/2402.00367). 
- **NumSemanticSetUncertainty**: [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://arxiv.org/pdf/2302.09664). 
- **PTrue**: [Language Models (Mostly) Know What They Know](https://arxiv.org/pdf/2207.05221). 
- **Saplma**: [The Internal State of an LLM Knows When It’s Lying](https://aclanthology.org/2023.findings-emnlp.68.pdf). 
- **SemanticEntropy**: [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://arxiv.org/pdf/2302.09664).
- **sentSAR**: [Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models](https://aclanthology.org/2024.acl-long.276.pdf). 
- **SumEigenUncertainty**: [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://arxiv.org/pdf/2305.19187).
- **tokenSAR**: [Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models](https://aclanthology.org/2024.acl-long.276.pdf). 
- **VerbalizedConfidence**: [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://openreview.net/pdf?id=g3faCfrwm7). 

---

## Contributors  

- **Yavuz Faruk Bakman** (ybakman@usc.edu)  
- **Duygu Nur Yaldiz** (yaldiz@usc.edu)  
- **Sungmin Kang** (kangsung@usc.edu)  
- **Hayrettin Eren Yildiz** (hayereyil@gmail.com)  
- **Alperen Ozis** (alperenozis@gmail.com)  

---

## Citation  

If you use TruthTorchLM in your research, please cite:  

```bibtex
@misc{truthtorchlm2025,
  title={TruthTorchLM: A Comprehensive Library for Hallucination Detection in Large Language Models},
  author={Yavuz Faruk Bakman, Duygu Nur Yaldiz,Sungmin Kang, Hayrettin Eren Yildiz, Alperen Ozis},
  year={2025},
  howpublished={GitHub},
  url={https://github.com/Ybakman/TruthTorchLM}
}
```

---

## License  

TruthTorchLM is released under the [MIT License](LICENSE).  

For inquiries or support, feel free to contact the maintainers.