Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Calibrating Reasoning in Language Models with Internal Consistency
Authors: Zhihui Xie, Jizhou Guo, Tong Yu, Shuai Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies across different models and datasets demonstrate that internal consistency effectively distinguishes between correct and incorrect reasoning paths. Motivated by this, we propose a new approach to calibrate reasoning by up-weighting reasoning paths with high internal consistency, resulting in a significant boost in reasoning performance. |
| Researcher Affiliation | Collaboration | Zhihui Xie Jizhou Guo Tong Yu Shuai Li Shanghai Jiao Tong University Adobe Research The University of Hong Kong EMAIL EMAIL |
| Pseudocode | No | The paper describes the methods through textual explanations and mathematical equations, such as Equation 2 for Internal Consistency, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Due to policy constraints, we are unable to provide the code with our submission. We provide sufficient implementation details for readers to reproduce our results. |
| Open Datasets | Yes | Bool Q (Clark et al., 2019): A reading comprehension dataset where each instance involves a yes/no question grounded in a related passage. ... Coin Flip (Wei et al., 2022): ... Pr Onto QA (Saparov and He, 2023): ... Proof Writer (Tafjord et al., 2020): |
| Dataset Splits | Yes | We split the dataset randomly by 80%/20% into training and validation subsets. |
| Hardware Specification | Yes | We performed all experiments on a compute node with 8 Nvidia GPU cards and 512 GB of memory. |
| Software Dependencies | No | Following Radford et al. (2021), we use the Scikit-learn package (Pedregosa et al., 2011) and determine the L2 regularization strength λ using a hyperparameter sweep over the range between 10 6 and 106 for logistic regression. |
| Experiment Setup | Yes | In our few-shot Co T experiments, we use Nucleus sampling (Holtzman et al., 2019) with a temperature of 0.7 and a top-p of 0.95 to generate reasoning paths. |