title: From Loops to Oops: Fallback Behaviors of Language Models Under
    Uncertainty
summary: Large language models (LLMs) often exhibit undesirable behaviors, such as
  hallucinations and sequence repetitions. We propose to view these behaviors as
  fallbacks that models exhibit under uncertainty, and investigate the connection
  between them. We categorize fallback behaviors -- sequence repetitions,
  degenerate text, and hallucinations -- and extensively analyze them in models
  from the same family that differ by the amount of pretraining tokens, parameter
  count, or the inclusion of instruction-following training. Our experiments
  reveal a clear and consistent ordering of fallback behaviors, across all these
  axes: the more advanced an LLM is (i.e., trained on more tokens, has more
  parameters, or instruction-tuned), its fallback behavior shifts from sequence
  repetitions, to degenerate text, and then to hallucinations. Moreover, the same
  ordering is observed throughout a single generation, even for the
  best-performing models; as uncertainty increases, models shift from generating
  hallucinations to producing degenerate text and then sequence repetitions.
  Lastly, we demonstrate that while common decoding techniques, such as random
  sampling, might alleviate some unwanted behaviors like sequence repetitions,
  they increase harder-to-detect hallucinations.
paper_authors._id: 668e312938d04b545cc501f3
paper_authors.name: Maor Ivgi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-10T08:19:30.786Z
paper_authors._id: 610b7f53b1d06c607ab4436d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/610b7f53b1d06c607ab4436d/Bqw7fIsjpuukFP11S-awA.jpeg
paper_authors.fullname: Maor
paper_authors.type: user
paper_authors.user: Mivg
paper_authors._id: 668e312938d04b545cc501f4
paper_authors.name: Ori Yoran
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T07:48:34.994Z
paper_authors._id: 610b7ccff5dd7bcefedaff57
paper_authors.avatarUrl: /avatars/a1e77460105a56f84371fa02d530a71e.svg
paper_authors.fullname: Yoran
paper_authors.type: user
paper_authors.user: Ori
paper_authors._id: 668e312938d04b545cc501f5
paper_authors.name: Jonathan Berant
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-10T10:49:55.365Z
paper_authors._id: 60811c9a4ad99100d63ce36c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1619119757620-60811c9a4ad99100d63ce36c.png
paper_authors.fullname: Jonathan Berant
paper_authors.type: user
paper_authors.user: joberant
paper_authors._id: 668e312938d04b545cc501f6
paper_authors.name: Mor Geva
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-10T10:20:22.150Z
paper_authors._id: 610b729f9da682cd54ad9adf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1628140189042-noauth.jpeg
paper_authors.fullname: Mor Geva
paper_authors.type: user
paper_authors.user: mega
paper_title: From Loops to Oops: Fallback Behaviors of Language Models Under
    Uncertainty
paper_summary: Large language models (LLMs) often exhibit undesirable behaviors, such as
  hallucinations and sequence repetitions. We propose to view these behaviors as
  fallbacks that models exhibit under uncertainty, and investigate the connection
  between them. We categorize fallback behaviors -- sequence repetitions,
  degenerate text, and hallucinations -- and extensively analyze them in models
  from the same family that differ by the amount of pretraining tokens, parameter
  count, or the inclusion of instruction-following training. Our experiments
  reveal a clear and consistent ordering of fallback behaviors, across all these
  axes: the more advanced an LLM is (i.e., trained on more tokens, has more
  parameters, or instruction-tuned), its fallback behavior shifts from sequence
  repetitions, to degenerate text, and then to hallucinations. Moreover, the same
  ordering is observed throughout a single generation, even for the
  best-performing models; as uncertainty increases, models shift from generating
  hallucinations to producing degenerate text and then sequence repetitions.
  Lastly, we demonstrate that while common decoding techniques, such as random
  sampling, might alleviate some unwanted behaviors like sequence repetitions,
  they increase harder-to-detect hallucinations.
paper_ai_keywords: large language models (LLMs) hallucinations sequence repetitions degenerate text pretraining tokens parameter count instruction-following training fallback behaviors fallback behavior ordering