Understanding Language Models (LMs)
Practical Solutions and Value
Language models (LMs) are powerful tools that have gained significant attention in recent years due to their remarkable capabilities. These models are first pre-trained on a large web text and then fine-tuned using specific examples and human feedback.
Challenges: However, these models may possess undesirable skills or knowledge that creators wish to remove before deployment. The challenge lies in effectively “unlearning” or forgetting specific potential without losing the model’s overall performance.
Solutions: Researchers have proposed a novel approach to study the generalization behavior in forgetting skills within LMs. This involves fine-tuning models on randomly labeled data for target tasks, a simple yet effective technique for inducing forgetting. The experiments aim to characterize forgetting generalization and uncover key findings.
Value: This research shows complex patterns of cross-task variability in forgetting and the need for further study on how the training data used for forgetting affects the model’s predictions in other areas.
Evaluation Framework
A comprehensive evaluation framework is used, which utilizes 21 multiple-choice tasks across various domains such as commonsense reasoning, reading comprehension, math, toxicity, and language understanding. The tasks are selected to cover a broad area of capabilities while maintaining a consistent multiple-choice format.
Results and Conclusion
The results demonstrate diverse forgetting behaviors across different tasks. The approach for studying the generalization behavior in forgetting skills within LMs is highlighted. Future research should aim to understand why certain examples are forgotten within tasks and explore the mechanisms behind the forgetting process.
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