Papers with SEquential subGoal Optimization

1 papers
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving (2024.acl-long)

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Challenge: Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, including mathematical problem-solving.
Approach: They propose a framework that connects the subgoal breakdown process and the probability of solving problems by identifying better subgoals with theoretical guarantees.
Outcome: The proposed framework outperforms existing methods on two benchmarks, GSM8K and MATH, highlighting the potential of SEGO in AI-driven mathematical problem-solving.

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