Abstract
A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution. Existing Self-Consistency techniques always generate a constant number of samples per question, where a better approach will be to non-uniformly distribute the available budget based on the amount of agreement in the samples generated so far. In response, we introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion. Our experiments over 17 reasoning and code generation datasets and three LLMs demonstrate that Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%- Anthology ID:
- 2023.emnlp-main.761
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12375–12396
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.761
- DOI:
- 10.18653/v1/2023.emnlp-main.761
- Cite (ACL):
- Pranjal Aggarwal, Aman Madaan, Yiming Yang, and Mausam. 2023. Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12375–12396, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs (Aggarwal et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.761.pdf