Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities
arXiv:2602.16093v1 Announce Type: new Abstract: Post-training endows pretrained LLMs with a variety of desirable skills, including instruction-following, reasoning, and others. However, these post-trained LLMs only encode knowledge up to a cut-off date, necessitating continual adaptation. Unfortunately, existing solutions cannot simultaneously learn new knowledge from an adaptation document corpora and mitigate the forgetting of earlier learned capabilities. To address this, we introduce Distillation via Split Contexts (DiSC), a simple context-distillation based approach for continual knowledge adaptation. \methodname~derives student and teacher distributions by conditioning on distinct segments of the training example and minimizes the KL divergence between the shared tokens. This allows us to efficiently apply context-distillation without requiring explicit generation steps during training. We run experiments on four post-trained models and two adaptation domains. Compared to prior
arXiv:2602.16093v1 Announce Type: new Abstract: Post-training endows pretrained LLMs with a variety of desirable skills, including instruction-following, reasoning, and others. However, these post-trained LLMs only encode knowledge up to a cut-off date, necessitating continual adaptation. Unfortunately, existing solutions cannot simultaneously learn new knowledge from an adaptation document corpora and mitigate the forgetting of earlier learned capabilities. To address this, we introduce Distillation via Split Contexts (DiSC), a simple context-distillation based approach for continual knowledge adaptation. \methodname~derives student and teacher distributions by conditioning on distinct segments of the training example and minimizes the KL divergence between the shared tokens. This allows us to efficiently apply context-distillation without requiring explicit generation steps during training. We run experiments on four post-trained models and two adaptation domains. Compared to prior finetuning and distillation methods for continual adaptation, DiSC consistently reports the best trade-off between learning new knowledge and mitigating forgetting of previously learned skills like instruction-following, reasoning, and factual knowledge.
Executive Summary
The article titled 'Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities' introduces a novel approach called Distillation via Split Contexts (DiSC) to address the challenge of continual knowledge adaptation in pretrained language models (LLMs). The method aims to balance the acquisition of new knowledge from adaptation document corpora with the retention of previously learned skills such as instruction-following, reasoning, and factual knowledge. Through experiments on four post-trained models and two adaptation domains, DiSC demonstrates superior performance compared to prior finetuning and distillation methods, offering an efficient solution that minimizes KL divergence between shared tokens without requiring explicit generation steps during training.
Key Points
- ▸ Introduction of DiSC for continual knowledge adaptation in LLMs.
- ▸ Balancing new knowledge acquisition with retention of prior skills.
- ▸ Experiments conducted on four post-trained models and two adaptation domains.
- ▸ Superior performance compared to existing finetuning and distillation methods.
- ▸ Efficient context-distillation approach without explicit generation steps.
Merits
Innovative Approach
DiSC presents a novel method for continual knowledge adaptation that effectively balances learning new information with retaining previously acquired skills, addressing a critical gap in current LLM training methodologies.
Empirical Validation
The study provides robust empirical evidence through experiments on multiple models and domains, demonstrating the efficacy of DiSC in enhancing post-training capabilities.
Efficiency
The approach is designed to be efficient, avoiding explicit generation steps during training, which makes it practical for real-world applications.
Demerits
Limited Scope of Experiments
The experiments are limited to four post-trained models and two adaptation domains, which may not fully capture the broader applicability of DiSC across diverse scenarios.
Potential Complexity
The method's reliance on conditioning on distinct segments of training examples and minimizing KL divergence might introduce complexity in implementation and scalability.
Generalization Concerns
While the results are promising, the study does not extensively explore the generalization of DiSC to other types of LLMs or adaptation tasks, which could be a limitation.
Expert Commentary
The article presents a significant advancement in the field of continual knowledge adaptation for language models. The introduction of DiSC addresses a critical challenge in the training of LLMs, which is the ability to learn new information while retaining previously acquired skills. The empirical validation through experiments on multiple models and domains provides strong evidence of the method's efficacy. However, the study's scope is somewhat limited, and further research is needed to explore the generalization of DiSC to a broader range of LLMs and adaptation tasks. The efficiency of the approach, particularly its avoidance of explicit generation steps during training, is a notable strength that enhances its practical applicability. Overall, the article contributes valuable insights to the ongoing discourse on continual learning and knowledge distillation in AI, offering a promising direction for future research and development in this field.
Recommendations
- ✓ Further experimentation with DiSC on a wider range of LLMs and adaptation domains to assess its generalizability.
- ✓ Exploration of the scalability and implementation challenges of DiSC in real-world applications to ensure its practical viability.
