PathFinder: Guided Search over Multi-Step Reasoning Paths
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.
65767d1515b1ca184b39b700
Olga Golovneva
admin_assigned
2023-12-11T08:31:19.053Z
6318b1e06e6bd039440871d6
/avatars/2d9f96eb4092a9d1734dcc09303b259a.svg
Olga Golovneva
user
Golovneva
65767d1515b1ca184b39b701
Sean O'Brien
admin_assigned
2023-12-11T08:31:55.198Z
63f5451571a5d395c7212c94
/avatars/05e199ab2b5e2fe5403fc63b8825daf7.svg
Sean O'Brien
user
Sean-OB
65767d1515b1ca184b39b702
Ramakanth Pasunuru
admin_assigned
2023-12-11T08:32:01.888Z
63b4be2334df1f9a90a1983c
/avatars/9cdfa3492c87aabdacab684b80628f14.svg
Ramakanth Pasunuru
user
rpasunuru
65767d1515b1ca184b39b703
Tianlu Wang
admin_assigned
2023-12-11T08:32:08.882Z
63f6a0e6b4c9a104f4b95d86
/avatars/660c3e44bea976f3e0560bf95466b03c.svg
Tianlu Wang
user
Tianlu
65767d1515b1ca184b39b704
Luke Zettlemoyer
65767d1515b1ca184b39b705
Maryam Fazel-Zarandi
65767d1515b1ca184b39b706
Asli Celikyilmaz
PathFinder: Guided Search over Multi-Step Reasoning Paths
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.
PathFinder, a tree-search-based reasoning model, improves performance on multi-step reasoning tasks by integrating dynamic decoding, constraints, pruning, and ranking.
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