Papers with reasoning program

2 papers
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)

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Challenge: Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth.
Approach: They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy.
Outcome: The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards.
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning (2024.naacl-long)

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Challenge: Large language models excel in various tasks, but their huge size and inaccessibility of parameters present challenges for practical deployment.
Approach: They propose to use CoT data to distill task-specific ability from large language models to smaller models . they use reasoning programs to suppress errors in distilled data and improve distillation quality .
Outcome: The proposed model outperforms LLMs on arithmetic reasoning, symbolic reasoning, and general ability.

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