UniAdapt: A Universal Adapter for Knowledge Calibration
Tai D. Nguyen, Long H. Pham, Jun Sun
TL;DR
UniAdapt is a universal, plug-and-play adapter that improves knowledge updates in large language models by effectively routing and calibrating knowledge, outperforming existing methods in lifelong model editing tasks.
Contribution
We introduce UniAdapt, a novel universal adapter with a vector-assisted router for knowledge calibration, addressing conflicts and preserving unrelated knowledge during model updates.
Findings
Outperforms existing lifelong model editors in various metrics.
Effective routing via semantic similarity search improves knowledge calibration.
Fully model-agnostic and easy to integrate.
Abstract
Large Language Models (LLMs) require frequent updates to correct errors and keep pace with continuously evolving knowledge in a timely and effective manner. Recent research in it model editing has highlighted the challenges in balancing generalization and locality, especially in the context of lifelong model editing. We discover that inserting knowledge directly into the model often causes conflicts and potentially disrupts other unrelated pre-trained knowledge. To address this problem, we introduce UniAdapt, a universal adapter for knowledge calibration. Inspired by the Mixture of Experts architecture and Retrieval-Augmented Generation, UniAdapt is designed with a vector-assisted router that is responsible for routing inputs to appropriate experts. The router maintains a vector store, including multiple shards, to construct routing vectors based on semantic similarity search results.…
Peer Reviews
Decision·Submitted to ICLR 2025
1. Lifelong model editing for LLMs is a promising research direction. The concept is crucial for developing LLM that can continually learn and adapt without requiring full retraining. 2. The experimental results demonstrate the effectiveness of the UniAdapt framework. By integrating a Mixture of Experts with the idea of RAG, the paper shows improvements in model adaptability and performance on benchmarks designed to test these aspects.
**Weakness 1** The core motivation, as stated in the abstract: >“We discover that inserting knowledge directly into the model often causes conflicts and potentially disrupts other unrelated pre-trained knowledge” This motivation lacks novelty as reducing the disruption to pre-trained knowledge while inserting new information is a foundational objective of the entire knowledge editing field (*measured by the locality metric*), as extensively discussed in foundational papers such as “Fast Model
1. UniAdapt's plug-and-play nature allows it to integrate with various LLMs without altering their original parameters, making it an adaptable and versatile solution for different base models. 2. By combining MoE with a vector-assisted router, UniAdapt selectively routes updates to specific ``experts'', effectively preserving existing knowledge while incorporating new information. This selective routing maintains a balance between reliability and generality. 3. The paper provides experimental
1. The experiments were limited to GPT2-XL and LLaMA2-7B, which may not be sufficient to generalize the results. It would be helpful to include results on state-of-the-art LLMs across a range of model sizes (e.g., 13B and 70B) for more comprehensive insights. 2. Regarding the effect of the target layer, it would be interesting to explore the effects of editing multiple layers simultaneously. Since layer behaviors might differ across models, further investigation on other models would be valuabl
UniAdapt is fully model-agnostic and designed for seamless plug-and-play integration.
The experimental analysis is not sufficiently thorough: - There is no analysis of the resource consumption of different methods, such as inference time and memory usage. - The baselines are inconsistent across different vanilla models. Specifically, in Table 2, different base models use different baselines—for example, WISE is only applied to LLaMA2-7B, and MEMIT only to GPT2-XL. Why did the author choose this experimental setup? - There is a lack of Out-of-Distribution evaluation.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Neural Networks and Applications · Reservoir Engineering and Simulation Methods
MethodsAdapter