title: AlpaGasus: Training A Better Alpaca with Fewer Data
summary: Large language models~(LLMs) obtain instruction-following capability through
  instruction-finetuning (IFT) on supervised instruction/response data. However,
  widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many
  low-quality instances with incorrect or irrelevant responses, which are
  misleading and detrimental to IFT. In this paper, we propose a simple and
  effective data selection strategy that automatically identifies and removes
  low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce
  AlpaGasus, which is finetuned on only 9k high-quality data filtered from the
  52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as
  evaluated by GPT-4 on multiple test sets and its 13B variant matches >90%
  performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also
  provides 5.7x faster training, reducing the training time for a 7B variant from
  80 minutes (for Alpaca) to 14 minutes We apply IFT for the same
  number of epochs as Alpaca(7B) but on fewer data, using 4timesNVIDIA A100
  (80GB) GPUs and following the original Alpaca setting and hyperparameters..
  Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be
  generally applied to instruction-tuning data, leading to faster training and
  better instruction-following models. Our project page is available at:
  https://lichang-chen.github.io/AlpaGasus/.
paper_authors._id: 64b5ef9160274cbb29736e1f
paper_authors.name: Lichang Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-18T07:47:18.473Z
paper_authors._id: 62323bb408bcea92917e42ee
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62323bb408bcea92917e42ee/2vHxkv-oSROtLteOnqa8P.jpeg
paper_authors.fullname: Lichang Chen
paper_authors.type: user
paper_authors.user: Lichang-Chen
paper_authors._id: 64b5ef9160274cbb29736e20
paper_authors.name: Shiyang Li
paper_authors._id: 64b5ef9160274cbb29736e21
paper_authors.name: Jun Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-19T06:28:08.466Z
paper_authors._id: 63a2137ef30c464227820e36
paper_authors.avatarUrl: /avatars/d400d3dbf7f7233bb85d507a34b0be02.svg
paper_authors.fullname: Jun Yan
paper_authors.type: user
paper_authors.user: jun-yan
paper_authors._id: 64b5ef9160274cbb29736e22
paper_authors.name: Hai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-19T06:28:12.656Z
paper_authors._id: 6436f6d83d818691cf25f683
paper_authors.avatarUrl: /avatars/acc08ff56095d53a36de5ae7cad4b718.svg
paper_authors.fullname: Hai Wang
paper_authors.type: user
paper_authors.user: sohurenxefr
paper_authors._id: 64b5ef9160274cbb29736e23
paper_authors.name: Kalpa Gunaratna
paper_authors._id: 64b5ef9160274cbb29736e24
paper_authors.name: Vikas Yadav
paper_authors._id: 64b5ef9160274cbb29736e25
paper_authors.name: Zheng Tang
paper_authors._id: 64b5ef9160274cbb29736e26
paper_authors.name: Vijay Srinivasan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-21T19:25:07.179Z
paper_authors._id: 6434e1c4546e16f17a1958f4
paper_authors.avatarUrl: /avatars/d14600ca6f29010648ec98687b9b2bc0.svg
paper_authors.fullname: Vijay Srinivasan
paper_authors.type: user
paper_authors.user: svijay17
paper_authors._id: 64b5ef9160274cbb29736e27
paper_authors.name: Tianyi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-18T06:43:50.735Z
paper_authors._id: 647f5af5b0e96764589f3b2a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
paper_authors.fullname: Tianyi Zhou
paper_authors.type: user
paper_authors.user: zhoutianyi
paper_authors._id: 64b5ef9160274cbb29736e28
paper_authors.name: Heng Huang
paper_authors._id: 64b5ef9160274cbb29736e29
paper_authors.name: Hongxia Jin
paper_title: AlpaGasus: Training A Better Alpaca with Fewer Data
paper_summary: Large language models~(LLMs) obtain instruction-following capability through
  instruction-finetuning (IFT) on supervised instruction/response data. However,
  widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many
  low-quality instances with incorrect or irrelevant responses, which are
  misleading and detrimental to IFT. In this paper, we propose a simple and
  effective data selection strategy that automatically identifies and removes
  low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce
  AlpaGasus, which is finetuned on only 9k high-quality data filtered from the
  52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as
  evaluated by GPT-4 on multiple test sets and its 13B variant matches >90%
  performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also
  provides 5.7x faster training, reducing the training time for a 7B variant from
  80 minutes (for Alpaca) to 14 minutes We apply IFT for the same
  number of epochs as Alpaca(7B) but on fewer data, using 4timesNVIDIA A100
  (80GB) GPUs and following the original Alpaca setting and hyperparameters..
  Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be
  generally applied to instruction-tuning data, leading to faster training and
  better instruction-following models. Our project page is available at:
  https://lichang-chen.github.io/AlpaGasus/.
paper_ai_summary: A data selection strategy using a strong LLM improves instruction-following models by filtering high-quality data, enhancing performance and reducing training time.
paper_ai_keywords: instruction-finetuning LLMs data selection Alpaca AlpaGasus GPT-4 Text-Davinci-003 instruction-tuning