title: Self-Evaluation Improves Selective Generation in Large Language Models
summary: 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.
paper_authors._id: 657fa86069cfe9b4cd6cebc4
paper_authors.name: Jie Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-02T19:14:26.585Z
paper_authors._id: 65e32ffb27dc8aa470f551e7
paper_authors.avatarUrl: /avatars/f53a170e5a1b0b0a2f277274bae359f1.svg
paper_authors.fullname: Jie Ren
paper_authors.type: user
paper_authors.user: jessierenjie
paper_authors._id: 657fa86069cfe9b4cd6cebc5
paper_authors.name: Yao Zhao
paper_authors._id: 657fa86069cfe9b4cd6cebc6
paper_authors.name: Tu Vu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-18T10:55:59.212Z
paper_authors._id: 636828a644e19ccad213063a
paper_authors.avatarUrl: /avatars/e1551d4ee9ca94018103af3bf1249cf2.svg
paper_authors.fullname: Tu Vu
paper_authors.type: user
paper_authors.user: tuvu
paper_authors._id: 657fa86069cfe9b4cd6cebc7
paper_authors.name: Peter J. Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-18T10:56:06.487Z
paper_authors._id: 62ded294dcdc9043efaa4fc3
paper_authors.avatarUrl: /avatars/1294078b8cd5c1901e4021253d877357.svg
paper_authors.fullname: Peter J Liu
paper_authors.type: user
paper_authors.user: peterjliu
paper_authors._id: 657fa86069cfe9b4cd6cebc8
paper_authors.name: Balaji Lakshminarayanan
paper_title: Self-Evaluation Improves Selective Generation in Large Language Models
paper_summary: 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.
paper_ai_summary: Self-evaluation at the token level improves accuracy and quality in selective generation tasks for large language models.
paper_ai_keywords: perplexity sequence-level probability token-level prediction multi-way comparison point-wise evaluation TruthfulQA TL;DR