What are the Desired Characteristics of Calibration Sets? Identifying
  Correlates on Long Form Scientific Summarization
Summarization models often generate text that is poorly calibrated to quality
metrics because they are trained to maximize the likelihood of a single
reference (MLE). To address this, recent work has added a calibration step,
which exposes a model to its own ranked outputs to improve relevance or, in a
separate line of work, contrasts positive and negative sets to improve
faithfulness. While effective, much of this work has focused on how to generate
and optimize these sets. Less is known about why one setup is more effective
than another. In this work, we uncover the underlying characteristics of
effective sets. For each training instance, we form a large, diverse pool of
candidates and systematically vary the subsets used for calibration
fine-tuning. Each selection strategy targets distinct aspects of the sets, such
as lexical diversity or the size of the gap between positive and negatives. On
three diverse scientific long-form summarization datasets (spanning biomedical,
clinical, and chemical domains), we find, among others, that faithfulness
calibration is optimal when the negative sets are extractive and more likely to
be generated, whereas for relevance calibration, the metric margin between
candidates should be maximized and surprise--the disagreement between model and
metric defined candidate rankings--minimized. Code to create, select, and
optimize calibration sets is available at
https://github.com/griff4692/calibrating-summaries
6461adbd9aad9e14972691fa
Griffin Adams
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Griffin Adams
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Bichlien H Nguyen
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Bichlien Nguyen
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Jake Smith
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Yingce Xia
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Shufang Xie
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Anna Ostropolets
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Budhaditya Deb
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Yuan-Jyue Chen
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Yuan-Jyue Chen
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Tristan Naumann
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2023-06-02T17:36:51.417Z
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Tristan Naumann
user
tnaumann
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Noémie Elhadad
What are the Desired Characteristics of Calibration Sets? Identifying
  Correlates on Long Form Scientific Summarization
Summarization models often generate text that is poorly calibrated to quality
metrics because they are trained to maximize the likelihood of a single
reference (MLE). To address this, recent work has added a calibration step,
which exposes a model to its own ranked outputs to improve relevance or, in a
separate line of work, contrasts positive and negative sets to improve
faithfulness. While effective, much of this work has focused on how to generate
and optimize these sets. Less is known about why one setup is more effective
than another. In this work, we uncover the underlying characteristics of
effective sets. For each training instance, we form a large, diverse pool of
candidates and systematically vary the subsets used for calibration
fine-tuning. Each selection strategy targets distinct aspects of the sets, such
as lexical diversity or the size of the gap between positive and negatives. On
three diverse scientific long-form summarization datasets (spanning biomedical,
clinical, and chemical domains), we find, among others, that faithfulness
calibration is optimal when the negative sets are extractive and more likely to
be generated, whereas for relevance calibration, the metric margin between
candidates should be maximized and surprise--the disagreement between model and
metric defined candidate rankings--minimized. Code to create, select, and
optimize calibration sets is available at
https://github.com/griff4692/calibrating-summaries
The effectiveness of calibration setups for summarization models depends on the specific characteristics of the calibration sets, including lexical diversity and gap size between positive and negative sets.
MLE calibration step relevance faithfulness ranking calibration fine-tuning lexical diversity metric margin surprise