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Computer Science > Computation and Language
Title: Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
(Submitted on 17 Oct 2023 (v1), last revised 8 May 2024 (this version, v2))
Abstract: Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these parameters to manipulate the underlying implicit knowledge (encompassing detection, editing, and merging), there remains an ambiguous understanding regarding their transferability across models with varying scales. In this paper, we seek to empirically investigate knowledge transfer from larger to smaller models through a parametric perspective. To achieve this, we employ sensitivity-based techniques to extract and align knowledge-specific parameters between different LLMs. Moreover, the LoRA module is used as the intermediary mechanism for injecting the extracted knowledge into smaller models. Evaluations across four benchmarks validate the efficacy of our proposed method. Our findings highlight the critical factors contributing to the process of parametric knowledge transfer, underscoring the transferability of model parameters across LLMs of different scales. Project website: this https URL
Submission history
From: Ming Zhong [view email][v1] Tue, 17 Oct 2023 17:58:34 GMT (4420kb,D)
[v2] Wed, 8 May 2024 12:11:00 GMT (4476kb,D)
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