title: PathFinder: Guided Search over Multi-Step Reasoning Paths
summary: With recent advancements in large language models, methods like
  chain-of-thought prompting to elicit reasoning chains have been shown to
  improve results on reasoning tasks. However, tasks that require multiple steps
  of reasoning still pose significant challenges to state-of-the-art models.
  Drawing inspiration from the beam search algorithm, we propose PathFinder, a
  tree-search-based reasoning path generation approach. It enhances diverse
  branching and multi-hop reasoning through the integration of dynamic decoding,
  enabled by varying sampling methods and parameters. Using constrained
  reasoning, PathFinder integrates novel quality constraints, pruning, and
  exploration methods to enhance the efficiency and the quality of generation.
  Moreover, it includes scoring and ranking features to improve candidate
  selection. Our approach outperforms competitive baselines on three complex
  arithmetic and commonsense reasoning tasks by 6% on average. Our model
  generalizes well to longer, unseen reasoning chains, reflecting similar
  complexities to beam search with large branching factors.
paper_authors._id: 65767d1515b1ca184b39b700
paper_authors.name: Olga Golovneva
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-11T08:31:19.053Z
paper_authors._id: 6318b1e06e6bd039440871d6
paper_authors.avatarUrl: /avatars/2d9f96eb4092a9d1734dcc09303b259a.svg
paper_authors.fullname: Olga Golovneva
paper_authors.type: user
paper_authors.user: Golovneva
paper_authors._id: 65767d1515b1ca184b39b701
paper_authors.name: Sean O'Brien
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-11T08:31:55.198Z
paper_authors._id: 63f5451571a5d395c7212c94
paper_authors.avatarUrl: /avatars/05e199ab2b5e2fe5403fc63b8825daf7.svg
paper_authors.fullname: Sean O'Brien
paper_authors.type: user
paper_authors.user: Sean-OB
paper_authors._id: 65767d1515b1ca184b39b702
paper_authors.name: Ramakanth Pasunuru
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-11T08:32:01.888Z
paper_authors._id: 63b4be2334df1f9a90a1983c
paper_authors.avatarUrl: /avatars/9cdfa3492c87aabdacab684b80628f14.svg
paper_authors.fullname: Ramakanth Pasunuru
paper_authors.type: user
paper_authors.user: rpasunuru
paper_authors._id: 65767d1515b1ca184b39b703
paper_authors.name: Tianlu Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-11T08:32:08.882Z
paper_authors._id: 63f6a0e6b4c9a104f4b95d86
paper_authors.avatarUrl: /avatars/660c3e44bea976f3e0560bf95466b03c.svg
paper_authors.fullname: Tianlu Wang
paper_authors.type: user
paper_authors.user: Tianlu
paper_authors._id: 65767d1515b1ca184b39b704
paper_authors.name: Luke Zettlemoyer
paper_authors._id: 65767d1515b1ca184b39b705
paper_authors.name: Maryam Fazel-Zarandi
paper_authors._id: 65767d1515b1ca184b39b706
paper_authors.name: Asli Celikyilmaz
paper_title: PathFinder: Guided Search over Multi-Step Reasoning Paths
paper_summary: With recent advancements in large language models, methods like
  chain-of-thought prompting to elicit reasoning chains have been shown to
  improve results on reasoning tasks. However, tasks that require multiple steps
  of reasoning still pose significant challenges to state-of-the-art models.
  Drawing inspiration from the beam search algorithm, we propose PathFinder, a
  tree-search-based reasoning path generation approach. It enhances diverse
  branching and multi-hop reasoning through the integration of dynamic decoding,
  enabled by varying sampling methods and parameters. Using constrained
  reasoning, PathFinder integrates novel quality constraints, pruning, and
  exploration methods to enhance the efficiency and the quality of generation.
  Moreover, it includes scoring and ranking features to improve candidate
  selection. Our approach outperforms competitive baselines on three complex
  arithmetic and commonsense reasoning tasks by 6% on average. Our model
  generalizes well to longer, unseen reasoning chains, reflecting similar
  complexities to beam search with large branching factors.
paper_ai_summary: PathFinder, a tree-search-based reasoning model, improves performance on multi-step reasoning tasks by integrating dynamic decoding, constraints, pruning, and ranking.
paper_ai_keywords: chain-of-thought prompting large language models beam search algorithm tree-search-based reasoning dynamic decoding constrained reasoning pruning exploration methods scoring ranking reasoning chains