How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
The performance of Large Language Models (LLMs) on many tasks is greatly
limited by the knowledge learned during pre-training and stored in the model's
parameters. Low-rank adaptation (LoRA) is a popular and efficient training
technique for updating or domain-specific adaptation of LLMs. In this study, we
investigate how new facts can be incorporated into the LLM using LoRA without
compromising the previously learned knowledge. We fine-tuned
Llama-3.1-8B-instruct using LoRA with varying amounts of new knowledge. Our
experiments have shown that the best results are obtained when the training
data contains a mixture of known and new facts. However, this approach is still
potentially harmful because the model's performance on external
question-answering benchmarks declines after such fine-tuning. When the
training data is biased towards certain entities, the model tends to regress to
few overrepresented answers. In addition, we found that the model becomes more
confident and refuses to provide an answer in only few cases. These findings
highlight the potential pitfalls of LoRA-based LLM updates and underscore the
importance of training data composition and tuning parameters to balance new
knowledge integration and general model capabilities.
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Sergey Pletenev
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2025-02-21T09:49:33.682Z
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Sergey Pletenev
user
memyprokotow
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Maria Marina
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2025-02-21T14:42:47.555Z
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Maria Marina
user
zlatamaria
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Daniil Moskovskiy
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2025-02-21T14:42:49.828Z
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Daniil Moskovskiy
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etomoscow
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Vasily Konovalov
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2025-05-12T06:51:55.444Z
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Vasily Konovalov
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Vasily
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Pavel Braslavski
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Alexander Panchenko
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2025-08-19T11:22:14.050Z
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Alexander Panchenko
user
apanc
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Mikhail Salnikov
extracted_confirmed
2025-02-21T09:01:04.770Z
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Mikhail Salnikov
user
msalnikov
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
The performance of Large Language Models (LLMs) on many tasks is greatly
limited by the knowledge learned during pre-training and stored in the model's
parameters. Low-rank adaptation (LoRA) is a popular and efficient training
technique for updating or domain-specific adaptation of LLMs. In this study, we
investigate how new facts can be incorporated into the LLM using LoRA without
compromising the previously learned knowledge. We fine-tuned
Llama-3.1-8B-instruct using LoRA with varying amounts of new knowledge. Our
experiments have shown that the best results are obtained when the training
data contains a mixture of known and new facts. However, this approach is still
potentially harmful because the model's performance on external
question-answering benchmarks declines after such fine-tuning. When the
training data is biased towards certain entities, the model tends to regress to
few overrepresented answers. In addition, we found that the model becomes more
confident and refuses to provide an answer in only few cases. These findings
highlight the potential pitfalls of LoRA-based LLM updates and underscore the
importance of training data composition and tuning parameters to balance new
knowledge integration and general model capabilities.
Low-rank adaptation (LoRA) effectively updates LLMs with new knowledge but risks degrading performance on external benchmarks due to data bias and overconfidence.
Low-rank adaptation LoRA Large Language Models LLMs fine-tuning Llama-3.1-8B-instruct question-answering benchmarks overrepresented answers confidence general model capabilities