Eliciting Latent Predictions from Transformers with the Tuned Lens (2303.08112v4)

Abstract: We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the \emph{tuned lens}, is a refinement of the earlier ``logit lens'' technique, which yielded useful insights but is often brittle. We test our method on various autoregressive LLMs with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at https://github.com/AlignmentResearch/tuned-lens.

• The paper presents the tuned lens, a method that applies layer-specific affine transformations to improve latent prediction accuracy over the logit lens.
• It demonstrates causal consistency by revealing influential hidden state directions that mirror final model outputs, enhancing interpretability.
• The approach effectively detects prompt injection attacks and reduces bias, offering practical benefits for securing and refining large transformer models.

Eliciting Latent Predictions from Transformers with the Tuned Lens: An Overview

The paper "Eliciting Latent Predictions from Transformers with the Tuned Lens" presents a refined method for interpreting the hidden states of transformer models, building upon the earlier "logit lens" technique. The authors introduce the "tuned lens," which decodes each hidden state into a distribution over a predefined vocabulary. This method is aimed at understanding how transformers iteratively refine their predictions layer by layer.

Key Contributions

The tuned lens offers several notable improvements over the logit lens:

1. Enhanced Predictive Reliability: By training an affine transformation for each layer in a model, the tuned lens achieves greater predictive accuracy compared to the original logit lens method. This improvement is particularly evident across various autoregressive LLMs ranging up to 20B parameters.
2. Causal Consistency: The paper details empirical investigations showing that the features influential to the tuned lens' predictions are similarly significant to the final model predictions. This consistency was investigated through causal basis extraction, which identifies principal influential directions within the hidden states.
3. Detection of Malicious Inputs: One practical implication of the method is its application in detecting prompt injection attacks. The trajectory of the latent predictions offers a signature that enables high-accuracy detection of malicious inputs.
4. Improved Robustness to Bias: The tuned lens demonstrates a more unbiased estimation of the model's final predictions compared to the logit lens. This is particularly useful for understanding the belief update process in transformers.

Empirical Evaluation

The tuned lens method was tested on a suite of transformer models, including GPT-Neo and BLOOM. The results showed that the tuned lens consistently outperformed the logit lens in terms of perplexity and biased estimation. Despite the improved complexity, it remains computationally feasible and was implemented to run efficiently on large datasets.

A detailed exploration of how tuned lenses transfer across layers and model variants adds to its robustness. For instance, the lenses were effectively transferred to fine-tuned models without requiring additional training, retaining their predictive accuracy.

Implications for Future Research

The implications of this research are substantial for both theoretical and practical applications in AI:

• Theoretical Insights: The method allows for a deeper understanding of how transformers manage and update predictive distributions internally. It may encourage more research into the iterative inference nature of transformer architectures.
• Practical Applications: By enabling anomaly detection, particularly for identifying prompt injection attacks, this method offers immediate utility in securing transformer applications.
• Foundation for Further Studies: The paper’s use of causal basis extraction sets a precedent for further exploration into causal interpretability, providing pathways for more robust model interpretation techniques.

Conclusion and Future Directions

In conclusion, the tuned lens presents a significant refinement of earlier techniques for interpreting transformers' inner workings. Future research may focus on scaling these insights to other model architectures, and improving computational efficiencies further. The practical applications, particularly in security, emphasize the broader relevance and necessity of such exploratory techniques in understanding and deploying AI systems effectively.

This contribution is poised to enhance our capacity to interpret complex models and their predictions, informing both the development of new models and the responsible deployment of existing ones in various domains.