fade-language 0.1.1

FADE: Why Bad Descriptions Happen to Good Features

FADE: Why Bad Descriptions Happen to Good Features

FADE helps you evaluate the alignment between LLM features and their natural language descriptions across four key metrics: Clarity, Responsiveness, Purity, and Faithfulness.

🔍 Features

• Model-agnostic evaluation of feature-to-description alignment
• Works with standard tramsformer neurons and SAE features
• Support for OpenAI, Azure, Ollama, vLLM, and other evaluation models

Installation

pip install fade-language

Tutorial

Check out our Tutorial Notebook that walks you through:

• A basic evaluation setup
• Using cached activations for improved performance
• Working with SAE features
• Using different evaluation models
• Advanced configuration options

Quickstart

from fade import EvaluationPipeline

# custom evaluation-model configuration with OpenAI LLM
config = {
    'evaluationLLM': {
        'type': 'openai', # type of evaluation model
        'name': 'gpt-4o-mini-2024-07-18', # the model variant
        'api_key': 'YOUR-KEY-HERE',
    }
}

# initialize evaluation pipeline
eval_pipeline = EvaluationPipeline(
    subject_model=model,  # e.g. huggingface model
    subject_tokenizer=tokenizer,  # e.g. huggingface tokenizer
    dataset=dataset,  # dict with int keys and str values
    config=config, # the custom config
    device=device,  # torch device
)

# example neuron specification
neuron_module = 'named.module.of.the.feature'  # str of the module name
neuron_index = 42  # int of the neuron index
concept = "The feature description you want to evaluate."  # str of the feature description

# run evaluation
(clarity, responsiveness, purity, faithfulness) = eval_pipeline.run(
    neuron_module=neuron_module,
    neuron_index=neuron_index,
    concept=concept
)

Citation

@misc{puri2025fadebaddescriptionshappen,
    title={FADE: Why Bad Descriptions Happen to Good Features}, 
    author={Bruno Puri and Aakriti Jain and Elena Golimblevskaia and Patrick Kahardipraja and Thomas Wiegand and Wojciech Samek and Sebastian Lapuschkin},
    year={2025},
    eprint={2502.16994},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2502.16994}, 
}

🚧 This repository is still in active development! More examples and detailed documentation will follow soon! 🚧