RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
<|reference_start|>RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations: Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduce RAVEL (Resolving Attribute-Value Entanglements in Language Models), a dataset that enables tightly controlled, quantitative comparisons between a variety of existing interpretability methods. We use the resulting conceptual framework to define the new method of Multi-task Distributed Alignment Search (MDAS), which allows us to find distributed representations satisfying multiple causal criteria. With Llama2-7B as the target language model, MDAS achieves state-of-the-art results on RAVEL, demonstrating the importance of going beyond neuron-level analyses to identify features distributed across activations. We release our benchmark at https://github.com/explanare/ravel.<|reference_end|>
@article{huang2024ravel:,
    title={RAVEL: Evaluating Interpretability Methods on Disentangling Language
  Model Representations},
    author={Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, Atticus Geiger},
    journal={arXiv preprint arXiv:2402.17700},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17700},
    primaryClass={cs.CL cs.LG}
}