@inproceedings{chen-etal-2024-llama,
title = "Llama {SL}ayer 8{B}: Shallow Layers Hold the Key to Knowledge Injection",
author = "Chen, Tianxiang and
Tan, Zhentao and
Gong, Tao and
Wu, Yue and
Chu, Qi and
Liu, Bin and
Ye, Jieping and
Yu, Nenghai",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.347/",
doi = "10.18653/v1/2024.findings-emnlp.347",
pages = "5991--6002",
abstract = "As a manner to augment pretrained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. While most current approaches, including parameter-efficient fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge across all LLM layers, it raises the question: are all layers equally crucial for knowledge injection? We embark upon evaluating the importance of each layer to locate the optimal layer range for knowledge injection. Intuitively, more important layers should play more critical roles in knowledge injection and deserve denser injection. We observe performance dips in question-answering benchmarks after the removal or expansion of the shallow layers, and the degradation shrinks as the layer gets deeper, indicating that the shallow layers hold the key to knowledge injection. This insight leads us to propose the S strategy, a post-pretraining strategy of selectively enhancing shallow layers while pruning the less effective deep ones. Based on this strategy, we introduce Llama Slayer 8B. We experimented on the corpus of code {\&} math and demonstrated the effectiveness of our strategy. Further experiments across different LLM, Mistral-7B, and a legal corpus confirmed the approach`s general applicability, underscoring its wide-ranging efficacy."
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<abstract>As a manner to augment pretrained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. While most current approaches, including parameter-efficient fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge across all LLM layers, it raises the question: are all layers equally crucial for knowledge injection? We embark upon evaluating the importance of each layer to locate the optimal layer range for knowledge injection. Intuitively, more important layers should play more critical roles in knowledge injection and deserve denser injection. We observe performance dips in question-answering benchmarks after the removal or expansion of the shallow layers, and the degradation shrinks as the layer gets deeper, indicating that the shallow layers hold the key to knowledge injection. This insight leads us to propose the S strategy, a post-pretraining strategy of selectively enhancing shallow layers while pruning the less effective deep ones. Based on this strategy, we introduce Llama Slayer 8B. We experimented on the corpus of code & math and demonstrated the effectiveness of our strategy. Further experiments across different LLM, Mistral-7B, and a legal corpus confirmed the approach‘s general applicability, underscoring its wide-ranging efficacy.</abstract>
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%0 Conference Proceedings
%T Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection
%A Chen, Tianxiang
%A Tan, Zhentao
%A Gong, Tao
%A Wu, Yue
%A Chu, Qi
%A Liu, Bin
%A Ye, Jieping
%A Yu, Nenghai
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-llama
%X As a manner to augment pretrained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. While most current approaches, including parameter-efficient fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge across all LLM layers, it raises the question: are all layers equally crucial for knowledge injection? We embark upon evaluating the importance of each layer to locate the optimal layer range for knowledge injection. Intuitively, more important layers should play more critical roles in knowledge injection and deserve denser injection. We observe performance dips in question-answering benchmarks after the removal or expansion of the shallow layers, and the degradation shrinks as the layer gets deeper, indicating that the shallow layers hold the key to knowledge injection. This insight leads us to propose the S strategy, a post-pretraining strategy of selectively enhancing shallow layers while pruning the less effective deep ones. Based on this strategy, we introduce Llama Slayer 8B. We experimented on the corpus of code & math and demonstrated the effectiveness of our strategy. Further experiments across different LLM, Mistral-7B, and a legal corpus confirmed the approach‘s general applicability, underscoring its wide-ranging efficacy.
%R 10.18653/v1/2024.findings-emnlp.347
%U https://aclanthology.org/2024.findings-emnlp.347/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.347
%P 5991-6002
Markdown (Informal)
[Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection](https://aclanthology.org/2024.findings-emnlp.347/) (Chen et al., Findings 2024)
ACL
- Tianxiang Chen, Zhentao Tan, Tao Gong, Yue Wu, Qi Chu, Bin Liu, Jieping Ye, and Nenghai Yu. 2024. Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5991–6002, Miami, Florida, USA. Association for Computational Linguistics.