title: Unlocking Anticipatory Text Generation: A Constrained Approach for
    Faithful Decoding with Large Language Models
summary: Large Language Models (LLMs) have demonstrated a powerful ability for text
  generation. However, achieving optimal results with a given prompt or
  instruction can be challenging, especially for billion-sized models.
  Additionally, undesired behaviors such as toxicity or hallucinations can
  manifest. While much larger models (e.g., ChatGPT) may demonstrate strength in
  mitigating these issues, there is still no guarantee of complete prevention. In
  this work, we propose formalizing text generation as a future-constrained
  generation problem to minimize undesirable behaviors and enforce faithfulness
  to instructions. The estimation of future constraint satisfaction, accomplished
  using LLMs, guides the text generation process. Our extensive experiments
  demonstrate the effectiveness of the proposed approach across three distinct
  text generation tasks: keyword-constrained generation (Lin et al., 2020),
  toxicity reduction (Gehman et al., 2020), and factual correctness in
  question-answering (Gao et al., 2023).
paper_authors._id: 6577d8d0224758a1fc667609
paper_authors.name: Lifu Tu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:11:48.418Z
paper_authors._id: 611d9648dcea95fcbbc5638e
paper_authors.avatarUrl: /avatars/e94bf70da30ed5036e2c12a38b6fcad9.svg
paper_authors.fullname: Lifu Tu
paper_authors.type: user
paper_authors.user: tulifu
paper_authors._id: 6577d8d0224758a1fc66760a
paper_authors.name: Semih Yavuz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:11:55.830Z
paper_authors._id: 64f8a68e33fd3f0fcb7810b5
paper_authors.avatarUrl: /avatars/cb5ea85e9ed513efaf24a9f36869f98f.svg
paper_authors.fullname: Semih Yavuz
paper_authors.type: user
paper_authors.user: syavuz
paper_authors._id: 6577d8d0224758a1fc66760b
paper_authors.name: Jin Qu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:12:20.401Z
paper_authors._id: 652cfdd22aa5b27c770eb21d
paper_authors.avatarUrl: /avatars/8ddec681486d8cfc6f93d25b422ccdba.svg
paper_authors.fullname: qu jin
paper_authors.type: user
paper_authors.user: JinWendy
paper_authors._id: 6577d8d0224758a1fc66760c
paper_authors.name: Jiacheng Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:12:41.223Z
paper_authors._id: 631983d5cb116eab31df5821
paper_authors.avatarUrl: /avatars/6a42c842a9439241ead2ace1d79fc32c.svg
paper_authors.fullname: Jiacheng Xu
paper_authors.type: user
paper_authors.user: jcxu
paper_authors._id: 6577d8d0224758a1fc66760d
paper_authors.name: Rui Meng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:13:05.764Z
paper_authors._id: 64100337c025ddf6189c2794
paper_authors.avatarUrl: /avatars/3a5b4757604e5d2e8f231af59544dd28.svg
paper_authors.fullname: Rui Meng
paper_authors.type: user
paper_authors.user: mengrui6351
paper_authors._id: 6577d8d0224758a1fc66760e
paper_authors.name: Caiming Xiong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:13:12.794Z
paper_authors._id: 649dbcc4e0fff1ed099dc80a
paper_authors.avatarUrl: /avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
paper_authors.fullname: Caiming Xiong
paper_authors.type: user
paper_authors.user: cxiong
paper_authors._id: 6577d8d0224758a1fc66760f
paper_authors.name: Yingbo Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T09:13:19.724Z
paper_authors._id: 649bc93758d8b19de0c7785f
paper_authors.avatarUrl: /avatars/3ed9473aee23d99f4ee949d3705089ea.svg
paper_authors.fullname: Yingbo Zhou
paper_authors.type: user
paper_authors.user: yingbozhou
paper_title: Unlocking Anticipatory Text Generation: A Constrained Approach for
    Faithful Decoding with Large Language Models
paper_summary: Large Language Models (LLMs) have demonstrated a powerful ability for text
  generation. However, achieving optimal results with a given prompt or
  instruction can be challenging, especially for billion-sized models.
  Additionally, undesired behaviors such as toxicity or hallucinations can
  manifest. While much larger models (e.g., ChatGPT) may demonstrate strength in
  mitigating these issues, there is still no guarantee of complete prevention. In
  this work, we propose formalizing text generation as a future-constrained
  generation problem to minimize undesirable behaviors and enforce faithfulness
  to instructions. The estimation of future constraint satisfaction, accomplished
  using LLMs, guides the text generation process. Our extensive experiments
  demonstrate the effectiveness of the proposed approach across three distinct
  text generation tasks: keyword-constrained generation (Lin et al., 2020),
  toxicity reduction (Gehman et al., 2020), and factual correctness in
  question-answering (Gao et al., 2023).
paper_ai_keywords: Large Language Models (LLMs) text generation prompt or instruction billion-sized models toxicity hallucinations ChatGPT future-constrained generation problem noise constraint constraint satisfaction keyword-constrained generation toxicity reduction factual correctness question-answering