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Original Paper
Original Paper: https://arxiv.org/abs/2312.05180
By: Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz
Abstract:
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.
Summary Notes
Boosting Multi-Step Reasoning with PATH FINDER: Simplifying Complex AI Tasks
Artificial Intelligence (AI) has made significant strides with Large Language Models (LLMs) leading the charge in text generation and problem-solving.
Yet, these models often falter with multi-step reasoning, a key aspect for tackling complex issues.
PATH FINDER emerges as a novel solution, enhancing the precision and efficiency of multi-step reasoning for AI Engineers in the enterprise sector.
This post explores PATH FINDER's approach, its tested performance, and the potential it holds for revolutionizing AI problem-solving.
Understanding PATH FINDER
PATH FINDER introduces a tree-search-based reasoning decoder that transforms how LLMs solve problems by:
- Generating Reasoning Steps: It starts from an input query, creating a sequence where each step is logically connected.
- Exploring Candidates: Through tree-search algorithms, it expands on reasoning steps, avoiding minor details and focusing on broader possibilities.
- Flexible Decoding: Adapts its strategy using different sampling methods and parameters to suit the complexity of the task.
- Quality Control: Implements pruning and constraints to ensure the reasoning chains are logical and diverse, avoiding repetition and contradictions.
Testing PATH FINDER's Capabilities
PATH FINDER has been rigorously tested, showing promising results:
- Enhanced Reasoning Performance: It has shown an average improvement of 6% over baseline models in arithmetic and commonsense reasoning tasks.
- Setting New Benchmarks: Compared to other LLM-based solutions, PATH FINDER excels in creating accurate and logical reasoning chains.
Ablation Study Insights
A deep dive into PATH FINDER's components reveals:
- Impact of Candidate Selection: The choice of scoring functions and verifier models greatly affects path accuracy, highlighting the need for fine-tuning.
- Importance of Parameters: The branching factor and buffer size are crucial for balancing efficiency and effectiveness.
The Future of Multi-Step Reasoning
PATH FINDER marks a major advancement in AI's ability to perform multi-step reasoning by:
- Breaking down reasoning into manageable steps and employing a tree-search method.
- Offering remarkable accuracy improvements and setting new industry standards.
Its potential applications in enterprise settings are vast, from improving decision-making to automating complex tasks.
Ongoing enhancements in sampling and scoring techniques are expected to further boost PATH FINDER's performance while reducing computational demands, setting the stage for future innovations in AI reasoning.
Conclusion
PATH FINDER stands out as a pivotal development in enhancing LLMs' multi-step reasoning capabilities, providing AI engineers with a powerful tool to solve complex problems more efficiently and accurately.
Appendices
Detailed information on the study's limitations, ethical considerations, related work, experimental setup, and verifier models used are provided, giving a full picture of PATH FINDER's development and implementation.
PATH FINDER represents a new chapter in AI's evolution, offering a sophisticated and scalable approach to complex problem-solving that could redefine how we approach AI reasoning.
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