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.
@article{arxiv.2402.17700,
title = {RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations},
author = {Jing Huang and Zhengxuan Wu and Christopher Potts and Mor Geva and Atticus Geiger},
journal= {arXiv preprint arXiv:2402.17700},
year = {2024}
}
Comments
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)