Hallucination Detection with Belief Tree Propagation

This is the official implementation for the paper, "A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation".

Requirements

The dependency packages can be found in requirements.txt file. One can use pip install -r requirements.txt to configure the environment. We use python 3.10 to run the experiments.

Running the experiments

The overall pipeline is: build the belief tree via prompting the LLM $\rightarrow$ prompt the LLM for its confidence score $\rightarrow$ compute the posterior probability $\rightarrow$ evaluate the performance. Before running experiments, you need to configure your OpenAI API key by setting the OPENAI_API_KEY environment variable.

1. Belief tree generation

python generate_belief_tree.py --dataset=wikibio --backbone=chatgpt

Use python generate_belief_tree.py --helpfull to see the choices for dataset and backbone.

By default, the generated belief trees will be stored at logs/belief_trees/{dataset}_{backbone}.json

2. Prompt the LLM for its confidence score Similarly, you can specify the dataset name and the backbone LLM used for the experiment in the command line:

python confidence_estimation.py --dataset=wikibio --backbone=chatgpt

By default, the generated belief trees will be stored at logs/conf_estimation/{dataset}_{backbone}.json

3. Use the NLI model to label the edge type (the relationship between a parent node and a child node)

python tools/label_edges.py --dataset=wikibio --backbone=chatgpt

4. Compute the posterior probabilities

python hmm_forward.py --dataset=wikibio --backbone=chatgpt

5. Performance evaluation

python tools/compute_metrics.py --dataset=wikibio --backbone=chatgpt

Citation

@article{hou2024probabilistic,
  title={A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation},
  author={Hou, Bairu and Zhang, Yang and Andreas, Jacob and Chang, Shiyu},
  journal={arXiv preprint arXiv:2406.06950},
  year={2024}
}```