Self-Evaluation Improves Selective Generation in Large Language Models
Safe deployment of large language models (LLMs) may benefit from a reliable
method for assessing their generated content to determine when to abstain or to
selectively generate. While likelihood-based metrics such as perplexity are
widely employed, recent research has demonstrated the limitations of using
sequence-level probability estimates given by LLMs as reliable indicators of
generation quality. Conversely, LLMs have demonstrated strong calibration at
the token level, particularly when it comes to choosing correct answers in
multiple-choice questions or evaluating true/false statements. In this work, we
reformulate open-ended generation tasks into token-level prediction tasks, and
leverage LLMs' superior calibration at the token level. We instruct an LLM to
self-evaluate its answers, employing either a multi-way comparison or a
point-wise evaluation approach, with the option to include a ``None of the
above'' option to express the model's uncertainty explicitly. We benchmark a
range of scoring methods based on self-evaluation and evaluate their
performance in selective generation using TruthfulQA and TL;DR. Through
experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based
scores not only improve accuracy, but also correlate better with the overall
quality of generated content.
657fa86069cfe9b4cd6cebc4
Jie Ren
claimed_verified
2024-03-02T19:14:26.585Z
65e32ffb27dc8aa470f551e7
/avatars/f53a170e5a1b0b0a2f277274bae359f1.svg
Jie Ren
user
jessierenjie
657fa86069cfe9b4cd6cebc5
Yao Zhao
657fa86069cfe9b4cd6cebc6
Tu Vu
admin_assigned
2023-12-18T10:55:59.212Z
636828a644e19ccad213063a
/avatars/e1551d4ee9ca94018103af3bf1249cf2.svg
Tu Vu
user
tuvu
657fa86069cfe9b4cd6cebc7
Peter J. Liu
admin_assigned
2023-12-18T10:56:06.487Z
62ded294dcdc9043efaa4fc3
/avatars/1294078b8cd5c1901e4021253d877357.svg
Peter J Liu
user
peterjliu
657fa86069cfe9b4cd6cebc8
Balaji Lakshminarayanan
Self-Evaluation Improves Selective Generation in Large Language Models
Safe deployment of large language models (LLMs) may benefit from a reliable
method for assessing their generated content to determine when to abstain or to
selectively generate. While likelihood-based metrics such as perplexity are
widely employed, recent research has demonstrated the limitations of using
sequence-level probability estimates given by LLMs as reliable indicators of
generation quality. Conversely, LLMs have demonstrated strong calibration at
the token level, particularly when it comes to choosing correct answers in
multiple-choice questions or evaluating true/false statements. In this work, we
reformulate open-ended generation tasks into token-level prediction tasks, and
leverage LLMs' superior calibration at the token level. We instruct an LLM to
self-evaluate its answers, employing either a multi-way comparison or a
point-wise evaluation approach, with the option to include a ``None of the
above'' option to express the model's uncertainty explicitly. We benchmark a
range of scoring methods based on self-evaluation and evaluate their
performance in selective generation using TruthfulQA and TL;DR. Through
experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based
scores not only improve accuracy, but also correlate better with the overall
quality of generated content.
Self-evaluation at the token level improves accuracy and quality in selective generation tasks for large language models.
perplexity sequence-level probability token-level prediction multi-way comparison point-wise evaluation TruthfulQA TL;DR