Alignment of language models
Alignment is the process of ensuring that models behaviour, outputs and responses are consistent with the human values and goals
We check for the models alignment with human values through various techniques such as reinforcement learning from human feedback (RLHF), adversarial testing, and interpretability methods etc. We hold a strong belief that with current rate progress in the autonomous gaming capabilities of llms there is strong case for investing in more robust systems and having better tractable, interpretabilible networks
"In a sensibly organized society, if you improve productivity, there is room for everybody to benefit. The problem isn't the technology, but the way the benefits are shared out." - Geoffrey Hinton
Having universal standards on what is safe and what is acceptable standards can be a challenging in a dynamic field such as ai safety. Having standardized evaluation methods is absolutely essential to ensure the safety and reliability of AI systems.
AI safety criteria
- Threat evaluation capabilities
- Interpretability
- Robustness
- Scalability
- Deception
Adversarial Robustness
Adversarial robustness refers to a model's ability to maintain performance when facing malicious inputs or attacks. Key considerations include:
Attack Vectors
- Direct model attacks: Manipulation through adversarial prompts or inputs
- Indirect attacks: Exploiting system vulnerabilities or naive users
Defensive Measures
- Input sanitization and validation
- Adversarial training techniques
- Model monitoring and anomaly detection
Evaluation Metrics
Common assessment approaches include:
# Example evaluation pseudocode
def test_robustness(model, adversarial_examples):
success_rate = model.evaluate(adversarial_examples)
return 1 - success_rateRobust models should maintain performance even when facing carefully crafted adversarial inputs designed to exploit weaknesses.
Adversarial alignment
In adversarial alignment we test the model's ability to defend itself against toxicity, hate speech, profanity etc. even if a malicious user tries to trick the system. Since large parts of the pre-training data has biased and human generated data extracted from different forms of text platforms from the internet it is safe to assume that there is a high likelihood of having harmful tokens in the responses of the language model. Aligned models are general purpose models We usually look for how robust the model is to attacks and if the target model is breached then how bad the consequences are for the attack. These breaches can be of the type first person where the attacker gets hands on access and perpetrates an direct attack on the model via prompting etc and on the other hand the attacker games naive users by inserting harmful code into the system in an embedded format where the harmful prompt is hidden in plain text which is used by the naive user there by attacking the system.
Improving model performance requires thorough monitoring and modification standards to understand and adjust the internal workings of neural networks. By analyzing model weights, we can detect deceptive or sycophantic behavior. Additionally, preparing models for unexpected distributional shifts and ensuring adversarial alignment enhances their robustness and reliability.
AI Model Evaluation Metrics
Example table
Model metrics
| Model | Accuracy | Precision | |
|---|---|---|---|
| BERT-base | 0.92 | 0.89 | 0.91 |
| GPT-3.5 | 0.88 | 0.91 | 0.86 |
| Llama 2 | 0.90 | 0.88 | 0.93 |
| Average | 0.90 | 0.89 | 0.90 |
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Prompt tuning using propagation
In this work, we explore "prompt tuning," an innovative approach for adapting frozen language models to specific tasks without modifying their core parameters. Unlike traditional methods, this technique offers remarkable efficiency while maintaining strong performance.
Key Advantages of Prompt Tuning
- Soft Prompts: Unlike GPT-3's discrete text prompts, we learn continuous "soft prompts" through backpropagation that can incorporate signals from labeled examples
- Scalability: Our method becomes increasingly competitive with model tuning as models grow beyond billions of parameters, eventually matching its performance
- Efficiency: Enables reuse of a single frozen model for multiple downstream tasks, reducing the computational burden of large models
Notable Findings
Our research reveals several important insights:
- Prompt tuning significantly outperforms GPT-3's few-shot learning approach
- The method simplifies earlier "prefix tuning" approaches while maintaining effectiveness
- Soft prompts demonstrate improved robustness in domain transfer scenarios
- Enables efficient "prompt ensembling" for enhanced performance
This approach represents a significant step forward in making large language models more practical and accessible, as it dramatically reduces the resources needed to adapt them to specific tasks while maintaining their powerful capabilities.