Exploring the Transferability of Interpretability Techniques from Transformer Language Models to Recurrent Neural Networks

Core Concepts

Interpretability techniques originally designed for transformer language models, such as contrastive activation addition, the tuned lens, and probing for latent knowledge, can be effectively applied to state-of-the-art recurrent neural network architectures like Mamba and RWKV, with similar performance to transformers.

Abstract

The paper examines whether popular interpretability tools originally designed for transformer language models can be applied to state-of-the-art recurrent neural network (RNN) architectures like Mamba and RWKV.
The authors first provide an overview of the Mamba and RWKV RNN architectures, which have shown comparable performance to transformers in language modeling and downstream tasks. They then reproduce three key findings from the transformer interpretability literature:

Contrastive Activation Addition (CAA): The authors find that RNNs can be steered using CAA, where a steering vector is computed by averaging the difference in residual stream activations between positive and negative examples of a particular behavior. They also introduce a modification called "state steering" that operates on the RNN's compressed state.

The Tuned Lens: The authors show that it is possible to elicit interpretable next-token predictions from the intermediate layers of RNNs using linear probes, similar to transformers. They find that the accuracy of these predictions increases monotonically with depth.

"Quirky" Models: The authors reproduce experiments showing that simple probing methods can elicit a model's knowledge of the correct answer to a question, even when it has been fine-tuned to output an incorrect answer. They find that these probes generalize to problems harder than those the probe was trained on, both for Mamba and transformer models.

Overall, the results demonstrate that the interpretability tools examined largely work "out-of-the-box" for state-of-the-art RNN architectures, with similar performance to transformers. The authors also find evidence that the compressed state of RNNs can be used to enhance the effectiveness of activation addition for steering model behavior.

Stats

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Quotes

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Key Insights Distilled From

Does Transformer Interpretability Transfer to RNNs?

by Gonç... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05971.pdf

Deeper Inquiries

How can the insights from this work be used to develop more robust and transparent RNN-based language models?

The insights from this work provide valuable information on how interpretability techniques originally designed for transformers can be adapted and applied to RNN architectures. By understanding how techniques like contrastive activation addition, tuned lens, and quirky models can be effective in steering model behavior, eliciting latent predictions, and extracting latent knowledge, developers can enhance the interpretability and transparency of RNN-based language models. This knowledge can be used to improve model understanding, identify biases, and ensure the reliability and trustworthiness of RNN models in various applications.

What are the limitations of the interpretability techniques explored, and how could they be further improved or extended to better understand the inner workings of RNN architectures?

While the interpretability techniques explored in the study show promise in enhancing the transparency of RNN models, they also have limitations. For example, the effectiveness of steering with contrastive activation addition may vary across different behaviors, and the additive effect of using both activation and state steering needs further investigation. To improve these techniques, researchers could explore more advanced steering methods, consider the impact of different multipliers, and investigate the interaction between activation and state steering in more depth. Additionally, for the tuned lens approach, further research could focus on refining the translator functions to better capture the latent predictions at each layer and improve the overall interpretability of RNN architectures.

Given the similarities in the transferability of interpretability techniques between transformers and RNNs, what broader implications does this have for the field of interpretable AI and the development of next-generation language models?

The transferability of interpretability techniques between transformers and RNNs suggests that there are common principles underlying the interpretability of different neural network architectures. This has significant implications for the field of interpretable AI, as it indicates that insights and methods developed for one type of model can be leveraged and adapted for others. For the development of next-generation language models, this means that researchers can build on existing interpretability techniques to create more transparent and explainable RNN-based models. By applying these techniques effectively, developers can enhance model trustworthiness, facilitate model debugging, and improve user understanding of AI-generated outputs. This cross-pollination of interpretability methods across different architectures can lead to more robust and reliable AI systems in the future.