title: Instruction-following Evaluation through Verbalizer Manipulation
summary: While instruction-tuned models have shown remarkable success in various
  natural language processing tasks, accurately evaluating their ability to
  follow instructions remains challenging. Existing benchmarks primarily focus on
  common instructions that align well with what the model learned during
  training. However, proficiency in responding to these instructions does not
  necessarily imply strong ability in instruction following. In this paper, we
  propose a novel instruction-following evaluation protocol called verbalizer
  manipulation. It instructs the model to verbalize the task label with words
  aligning with model priors to different extents, adopting verbalizers from
  highly aligned (e.g., outputting ``postive'' for positive sentiment), to
  minimally aligned (e.g., outputting ``negative'' for positive sentiment).
  Verbalizer manipulation can be seamlessly integrated with any classification
  benchmark to examine the model's reliance on priors and its ability to override
  them to accurately follow the instructions. We conduct a comprehensive
  evaluation of four major model families across nine datasets, employing twelve
  sets of verbalizers for each of them. We observe that the instruction-following
  abilities of models, across different families and scales, are significantly
  distinguished by their performance on less natural verbalizers. Even the
  strongest GPT-4 model struggles to perform better than random guessing on the
  most challenging verbalizer, emphasizing the need for continued advancements to
  improve their instruction-following abilities.
paper_authors._id: 64b9e233857bcccfc9617ba5
paper_authors.name: Shiyang Li
paper_authors._id: 64b9e233857bcccfc9617ba6
paper_authors.name: Jun Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-21T07:35:34.776Z
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: 64b9e233857bcccfc9617ba7
paper_authors.name: Hai Wang
paper_authors._id: 64b9e233857bcccfc9617ba8
paper_authors.name: Zheng Tang
paper_authors._id: 64b9e233857bcccfc9617ba9
paper_authors.name: Xiang Ren
paper_authors._id: 64b9e233857bcccfc9617baa
paper_authors.name: Vijay Srinivasan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-21T19:25:00.571Z
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: 64b9e233857bcccfc9617bab
paper_authors.name: Hongxia Jin
paper_title: Instruction-following Evaluation through Verbalizer Manipulation
paper_summary: While instruction-tuned models have shown remarkable success in various
  natural language processing tasks, accurately evaluating their ability to
  follow instructions remains challenging. Existing benchmarks primarily focus on
  common instructions that align well with what the model learned during
  training. However, proficiency in responding to these instructions does not
  necessarily imply strong ability in instruction following. In this paper, we
  propose a novel instruction-following evaluation protocol called verbalizer
  manipulation. It instructs the model to verbalize the task label with words
  aligning with model priors to different extents, adopting verbalizers from
  highly aligned (e.g., outputting ``postive'' for positive sentiment), to
  minimally aligned (e.g., outputting ``negative'' for positive sentiment).
  Verbalizer manipulation can be seamlessly integrated with any classification
  benchmark to examine the model's reliance on priors and its ability to override
  them to accurately follow the instructions. We conduct a comprehensive
  evaluation of four major model families across nine datasets, employing twelve
  sets of verbalizers for each of them. We observe that the instruction-following
  abilities of models, across different families and scales, are significantly
  distinguished by their performance on less natural verbalizers. Even the
  strongest GPT-4 model struggles to perform better than random guessing on the
  most challenging verbalizer, emphasizing the need for continued advancements to
  improve their instruction-following abilities.
paper_ai_summary: The study proposes verbalizer manipulation to evaluate models' ability to follow instructions by using verbalizers with varying alignment to the model's priors, revealing significant differences in instruction-following capability among models.
paper_ai_keywords: instruction-following verbalizer manipulation task label model priors classification benchmark instruction-following abilities GPT-4