PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918474697146368 |
|---|---|
| author | Leang, Joshua Ong Jun Zhao, Zheng Gema, Aryo Pradipta Yang, Sohee Kwan, Wai-Chung He, Xuanli Li, Wenda Minervini, Pasquale Giunchiglia, Eleonora Cohen, Shay B. |
| author_facet | Leang, Joshua Ong Jun Zhao, Zheng Gema, Aryo Pradipta Yang, Sohee Kwan, Wai-Chung He, Xuanli Li, Wenda Minervini, Pasquale Giunchiglia, Eleonora Cohen, Shay B. |
| contents | Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the highest reward. The key challenge for reasoning tasks is designing a scoring function that can identify correct reasoning chains without access to ground-truth answers. We propose Probabilistic Confidence Selection And Ranking (PiCSAR): a simple, training-free method that scores each candidate generation using the joint log-likelihood of the reasoning and final answer. The joint log-likelihood of the reasoning and final answer naturally decomposes into reasoning confidence and answer confidence. PiCSAR achieves substantial gains across diverse benchmarks (+10.18 on MATH500, +9.81 on AIME2025), outperforming baselines with at least 2x fewer samples in 16 out of 20 comparisons. Our analysis reveals that correct reasoning chains exhibit significantly higher reasoning and answer confidence, justifying the effectiveness of PiCSAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains Leang, Joshua Ong Jun Zhao, Zheng Gema, Aryo Pradipta Yang, Sohee Kwan, Wai-Chung He, Xuanli Li, Wenda Minervini, Pasquale Giunchiglia, Eleonora Cohen, Shay B. Computation and Language Artificial Intelligence Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the highest reward. The key challenge for reasoning tasks is designing a scoring function that can identify correct reasoning chains without access to ground-truth answers. We propose Probabilistic Confidence Selection And Ranking (PiCSAR): a simple, training-free method that scores each candidate generation using the joint log-likelihood of the reasoning and final answer. The joint log-likelihood of the reasoning and final answer naturally decomposes into reasoning confidence and answer confidence. PiCSAR achieves substantial gains across diverse benchmarks (+10.18 on MATH500, +9.81 on AIME2025), outperforming baselines with at least 2x fewer samples in 16 out of 20 comparisons. Our analysis reveals that correct reasoning chains exhibit significantly higher reasoning and answer confidence, justifying the effectiveness of PiCSAR. |
| title | PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.21787 |