Probing Language Models to Elicit Reliable Knowledge Despite Systematic Errors

Core Concepts

Probing methods can extract reliable knowledge representations from language models that have been finetuned to make systematic errors in certain contexts, even when the model's overt outputs are untrustworthy.

Abstract

The paper introduces a novel experimental setup using "quirky" language models that have been finetuned to make systematic errors when a certain keyword ("Bob") is present in the prompt. The goal is to evaluate methods for Eliciting Latent Knowledge (ELK) - finding patterns in the model's activations that robustly track the true state of the world, even when the model's output is untrustworthy.
The key findings are:

Probing methods, especially in middle layers, can often recover the model's true knowledge independently of its untruthful output. Logistic regression on contrast pairs recovers 75% of the gap between the model's truthful and untruthful behavior.

Unsupervised probing methods like Contrast Consistent Search (CCS) and Contrastive Representation Clustering (CRC) rarely generalize in a context-dependent way, making their performance more predictable, though their in-distribution performance is lower.

Simple anomaly detection methods can flag when the model's output may not be trustworthy, without access to any labeled examples of untruthful behavior.

The paper also investigates the effects of the finetuning method (LoRA vs full finetuning) and the diversity of the prompt templates on the extractability of context-independent knowledge.

Stats

"2+2=5. Bob:" is the model's untruthful output, while the true answer is "False".
The difficulty metric for the addition dataset is the magnitude of the smaller operand.
The untruthful label for the addition dataset is whether the result is equal to the true sum with the first digit incremented by one.

Quotes

"Eliciting Latent Knowledge (ELK) aims to locate patterns in an AI's activations that robustly point to the truth, even in cases where the AI's overt output is misleading or false."
"An ELK method can be useful even when it is no more robust (in terms of typical accuracy during deployment) than other sources of information, as long as its errors are decorrelated with the errors of existing sources of information."

Key Insights Distilled From

Eliciting Latent Knowledge from Quirky Language Models

by Alex Mallen,... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2312.01037.pdf

Deeper Inquiries

How might the proposed ELK methods be extended to handle more complex or open-ended tasks, beyond the binary classification settings explored in this paper?

The proposed Eliciting Latent Knowledge (ELK) methods can be extended to handle more complex or open-ended tasks by incorporating multi-class classification or regression settings. Instead of just binary classification, the probes can be trained to predict multiple classes or continuous values, allowing for a more nuanced understanding of the model's latent knowledge. This extension would involve modifying the probing methods to accommodate multiple classes or continuous outputs and adjusting the loss functions accordingly.
Furthermore, for open-ended tasks where the output space is not predefined, ELK methods can be adapted to generate diverse and creative responses. This can be achieved by training the probes to capture the underlying patterns in the model's activations that lead to novel and varied outputs. Techniques such as generative probing or reinforcement learning can be employed to encourage the model to explore different possibilities and generate innovative solutions.
In addition, incorporating domain-specific knowledge or constraints into the ELK methods can enhance their ability to handle complex tasks. By guiding the probes with domain-specific rules or constraints, the model can be encouraged to provide more accurate and contextually relevant responses in diverse scenarios.

What are the potential limitations or failure modes of using anomaly detection to flag untrustworthy model outputs, and how could these be addressed?

Using anomaly detection to flag untrustworthy model outputs may have limitations and potential failure modes that need to be considered. Some of these limitations include:

Limited Training Data: Anomaly detection methods may require a large amount of labeled data to effectively distinguish between normal and anomalous behavior. Limited training data could lead to inaccurate anomaly detection and false alarms.

Concept Drift: Anomaly detection models may struggle to adapt to changes in the data distribution over time, leading to decreased performance in detecting anomalies in evolving scenarios.

Adversarial Attacks: Adversarial examples designed to evade anomaly detection systems could potentially fool the model into misclassifying normal instances as anomalies or vice versa.

To address these limitations and failure modes, several strategies can be implemented:

Continuous Monitoring: Regularly updating the anomaly detection model with new data and retraining it to adapt to changing patterns in the data distribution can help mitigate concept drift.

Ensemble Methods: Combining multiple anomaly detection techniques or models can improve robustness and reduce the impact of adversarial attacks.

Feature Engineering: Careful selection and engineering of features can enhance the model's ability to detect anomalies effectively. Domain knowledge can be leveraged to identify relevant features for anomaly detection.

Given the importance of context-independent knowledge representations for robust generalization, how might language model architectures or training procedures be designed to inherently encourage the formation of such representations?

To encourage the formation of context-independent knowledge representations in language model architectures, several design considerations and training procedures can be implemented:

Diverse Training Data: Incorporating a wide range of diverse and representative training data can help the model learn robust and generalizable features that are not overly dependent on specific contexts.

Regularization Techniques: Applying regularization techniques such as dropout, weight decay, or adversarial training can prevent the model from overfitting to context-specific patterns and encourage the learning of more generalizable representations.

Multi-Task Learning: Training the model on multiple tasks simultaneously can promote the extraction of shared features that are relevant across different contexts, leading to the development of context-independent representations.

Prompt Engineering: Designing prompts that encourage the model to focus on the underlying relationships and concepts rather than specific context-dependent cues can guide the model towards learning more generalizable knowledge representations.

By incorporating these strategies into the architecture and training procedures of language models, it is possible to foster the development of context-independent knowledge representations that enhance the model's ability to generalize effectively across diverse tasks and contexts.