Instruction-following Evaluation through Verbalizer Manipulation
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.
64b9e233857bcccfc9617ba5
Shiyang Li
64b9e233857bcccfc9617ba6
Jun Yan
claimed_verified
2023-07-21T07:35:34.776Z
63a2137ef30c464227820e36
/avatars/d400d3dbf7f7233bb85d507a34b0be02.svg
Jun Yan
user
jun-yan
64b9e233857bcccfc9617ba7
Hai Wang
64b9e233857bcccfc9617ba8
Zheng Tang
64b9e233857bcccfc9617ba9
Xiang Ren
64b9e233857bcccfc9617baa
Vijay Srinivasan
claimed_verified
2023-07-21T19:25:00.571Z
6434e1c4546e16f17a1958f4
/avatars/d14600ca6f29010648ec98687b9b2bc0.svg
Vijay Srinivasan
user
svijay17
64b9e233857bcccfc9617bab
Hongxia Jin
Instruction-following Evaluation through Verbalizer Manipulation
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.
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.
instruction-following verbalizer manipulation task label model priors classification benchmark instruction-following abilities GPT-4