title: How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
summary: 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.
paper_authors._id: 67b83fd69fb3eedaf6d0aef3
paper_authors.name: Sergey Pletenev
paper_authors.status: claimed_verified
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paper_authors.fullname: Sergey Pletenev
paper_authors.type: user
paper_authors.user: memyprokotow
paper_authors._id: 67b83fd69fb3eedaf6d0aef4
paper_authors.name: Maria Marina
paper_authors.status: claimed_verified
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paper_authors.fullname: Maria Marina
paper_authors.type: user
paper_authors.user: zlatamaria
paper_authors._id: 67b83fd69fb3eedaf6d0aef5
paper_authors.name: Daniil Moskovskiy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T14:42:49.828Z
paper_authors._id: 61ade264f602880813dbe10b
paper_authors.avatarUrl: /avatars/a92dea7d853bbabbf60b351c207b6875.svg
paper_authors.fullname: Daniil Moskovskiy
paper_authors.type: user
paper_authors.user: etomoscow
paper_authors._id: 67b83fd69fb3eedaf6d0aef6
paper_authors.name: Vasily Konovalov
paper_authors._id: 67b83fd69fb3eedaf6d0aef7
paper_authors.name: Pavel Braslavski
paper_authors._id: 67b83fd69fb3eedaf6d0aef8
paper_authors.name: Alexander Panchenko
paper_authors._id: 67b83fd69fb3eedaf6d0aef9
paper_authors.name: Mikhail Salnikov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-21T09:01:04.770Z
paper_authors._id: 62bd6c6baaf1480f1aa2222e
paper_authors.avatarUrl: /avatars/fd92ae2986d435a47eb1e382ac11d8e0.svg
paper_authors.fullname: Mikhail Salnikov
paper_authors.type: user
paper_authors.user: msalnikov
paper_title: How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
paper_summary: 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.
paper_ai_keywords: Low-rank adaptation (LoRA) LLaMA-3.1-8B-instruct Schwstruct fine-tuning external question-answering benchmarks overrepresented answers general model capabilities