title: The False Promise of Imitating Proprietary LLMs
summary: An emerging method to cheaply improve a weaker language model is to finetune
  it on outputs from a stronger model, such as a proprietary system like ChatGPT
  (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply
  imitate the proprietary model's capabilities using a weaker open-source model.
  In this work, we critically analyze this approach. We first finetune a series
  of LMs that imitate ChatGPT using varying base model sizes (1.5B--13B), data
  sources, and imitation data amounts (0.3M--150M tokens). We then evaluate the
  models using crowd raters and canonical NLP benchmarks. Initially, we were
  surprised by the output quality of our imitation models -- they appear far
  better at following instructions, and crowd workers rate their outputs as
  competitive with ChatGPT. However, when conducting more targeted automatic
  evaluations, we find that imitation models close little to none of the gap from
  the base LM to ChatGPT on tasks that are not heavily supported in the imitation
  data. We show that these performance discrepancies may slip past human raters
  because imitation models are adept at mimicking ChatGPT's style but not its
  factuality. Overall, we conclude that model imitation is a false promise: there
  exists a substantial capabilities gap between open and closed LMs that, with
  current methods, can only be bridged using an unwieldy amount of imitation data
  or by using more capable base LMs. In turn, we argue that the highest leverage
  action for improving open-source models is to tackle the difficult challenge of
  developing better base LMs, rather than taking the shortcut of imitating
  proprietary systems.
paper_authors._id: 64700e34850a938d6c5638a5
paper_authors.name: Arnav Gudibande
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paper_authors.fullname: Arnav Gudibande
paper_authors.type: user
paper_authors.user: arnavg
paper_authors._id: 64700e34850a938d6c5638a6
paper_authors.name: Eric Wallace
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paper_authors.fullname: Eric Wallace
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paper_authors.user: EricWallace
paper_authors._id: 64700e34850a938d6c5638a7
paper_authors.name: Charlie Snell
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paper_authors.fullname: Charlie Snell
paper_authors.type: user
paper_authors.user: sea-snell
paper_authors._id: 64700e34850a938d6c5638a8
paper_authors.name: Xinyang Geng
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Xinyang (Young) Geng
paper_authors.type: user
paper_authors.user: young-geng
paper_authors._id: 64700e34850a938d6c5638a9
paper_authors.name: Hao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-07T12:39:37.559Z
paper_authors._id: 62aa53fc5df2251f7df798c6
paper_authors.avatarUrl: /avatars/1c7828ce861c30ddd0afceff355a92ec.svg
paper_authors.fullname: Hao
paper_authors.type: user
paper_authors.user: haoliu
paper_authors._id: 64700e34850a938d6c5638aa
paper_authors.name: Pieter Abbeel
paper_authors._id: 64700e34850a938d6c5638ab
paper_authors.name: Sergey Levine
paper_authors._id: 64700e34850a938d6c5638ac
paper_authors.name: Dawn Song
paper_title: The False Promise of Imitating Proprietary LLMs
paper_summary: An emerging method to cheaply improve a weaker language model is to finetune
  it on outputs from a stronger model, such as a proprietary system like ChatGPT
  (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply
  imitate the proprietary model's capabilities using a weaker open-source model.
  In this work, we critically analyze this approach. We first finetune a series
  of LMs that imitate ChatGPT using varying base model sizes (1.5B--13B), data
  sources, and imitation data amounts (0.3M--150M tokens). We then evaluate the
  models using crowd raters and canonical NLP benchmarks. Initially, we were
  surprised by the output quality of our imitation models -- they appear far
  better at following instructions, and crowd workers rate their outputs as
  competitive with ChatGPT. However, when conducting more targeted automatic
  evaluations, we find that imitation models close little to none of the gap from
  the base LM to ChatGPT on tasks that are not heavily supported in the imitation
  data. We show that these performance discrepancies may slip past human raters
  because imitation models are adept at mimicking ChatGPT's style but not its
  factuality. Overall, we conclude that model imitation is a false promise: there
  exists a substantial capabilities gap between open and closed LMs that, with
  current methods, can only be bridged using an unwieldy amount of imitation data
  or by using more capable base LMs. In turn, we argue that the highest leverage
  action for improving open-source models is to tackle the difficult challenge of
  developing better base LMs, rather than taking the shortcut of imitating
  proprietary systems.
paper_ai_summary: Finetuning open-source language models on outputs from proprietary models like ChatGPT shows improved instruction-following but fails to close the performance gap on unsupported tasks.
paper_ai_keywords: finetune LMs imitation models ChatGPT Alpaca Self-Instruct base model sizes data sources imitation data crowd raters canonical NLP benchmarks automatic evaluations model imitation capabilities gap base LMs