More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910627893608448 |
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| author | Liu, Chengyuan Kang, Yangyang Wang, Shihang Qing, Lizhi Zhao, Fubang Sun, Changlong Kuang, Kun Wu, Fei |
| author_facet | Liu, Chengyuan Kang, Yangyang Wang, Shihang Qing, Lizhi Zhao, Fubang Sun, Changlong Kuang, Kun Wu, Fei |
| contents | The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). However, this paper presents a further challenge for real application of domain-specific LLMs beyond CF, called General Capabilities Integration (GCI), which necessitates the integration of both the general capabilities and domain knowledge within a single instance. The objective of GCI is not merely to retain previously acquired general capabilities alongside new domain knowledge, but to harmonize and utilize both sets of skills in a cohesive manner to enhance performance on domain-specific tasks. Taking legal domain as an example, we carefully design three groups of training and testing tasks without lacking practicability, and construct the corresponding datasets. To better incorporate general capabilities across domain-specific scenarios, we introduce ALoRA, which utilizes a multi-head attention module upon LoRA, facilitating direct information transfer from preceding tokens to the current one. This enhancement permits the representation to dynamically switch between domain-specific knowledge and general competencies according to the attention. Extensive experiments are conducted on the proposed tasks. The results exhibit the significance of our setting, and the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs Liu, Chengyuan Kang, Yangyang Wang, Shihang Qing, Lizhi Zhao, Fubang Sun, Changlong Kuang, Kun Wu, Fei Computation and Language The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). However, this paper presents a further challenge for real application of domain-specific LLMs beyond CF, called General Capabilities Integration (GCI), which necessitates the integration of both the general capabilities and domain knowledge within a single instance. The objective of GCI is not merely to retain previously acquired general capabilities alongside new domain knowledge, but to harmonize and utilize both sets of skills in a cohesive manner to enhance performance on domain-specific tasks. Taking legal domain as an example, we carefully design three groups of training and testing tasks without lacking practicability, and construct the corresponding datasets. To better incorporate general capabilities across domain-specific scenarios, we introduce ALoRA, which utilizes a multi-head attention module upon LoRA, facilitating direct information transfer from preceding tokens to the current one. This enhancement permits the representation to dynamically switch between domain-specific knowledge and general competencies according to the attention. Extensive experiments are conducted on the proposed tasks. The results exhibit the significance of our setting, and the effectiveness of our method. |
| title | More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.17830 |