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..
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales
Authors: Tang Li, Mengmeng Ma, Xi Peng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and ablation studies demonstrate that our model outperforms state-of-the-art models by up to 10.1% in prediction accuracy across a wide range of tasks. Furthermore, our method significantly improves the model s rationale correctness, improving localization by 7.5% and disentanglement by 36.5%. Our dataset, source code, and pretrained weights: https://github.com/deep-real/DCP |
| Researcher Affiliation | Academia | Department of Computer & Information Science, University of Delaware |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our dataset, source code, and pretrained weights: https://github.com/deep-real/DCP |
| Open Datasets | Yes | Evaluation datasets: We validate the prediction correctness of the models on image classification and image-text retrieval tasks. For image classification (zero-shot, linear probe), experiments are carried out on nine benchmark datasets, including CUB [17], Caltech101 [58], Oxford Pets [59], Food101 [60], SUN397 [61], Stanford Cars [62], DTD [63], CIFAR-10 [64], and CIFAR-100 [64]. For retrieval, we conduct experiments on Flickr30K [65] and MSCOCO [66]. To evaluate the correctness of rationales, we evaluate the models rationale localizability on CUB-Part [67] and Part Image Net [68] that provide ground truth segmentation masks of object parts... |
| Dataset Splits | Yes | More details can be found in Appendix D. (Appendix D, Table 11 lists dataset sizes and the NeurIPS checklist Q6 states: "The paper details all aspects of the experimental settings, including data splits.") |
| Hardware Specification | Yes | The NeurIPS checklist Q8 states: "The paper adequately details the computational resources required for each experiment, including the types of compute workers (CPU or GPU), memory specifications, and execution times." |
| Software Dependencies | No | The paper mentions specific models and optimizers like "CLIP-Vi T architectures [37]" and "Adam W [69] optimizer" and that GPT-4 was used for data generation, but it does not specify version numbers for general software libraries or frameworks (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | We follow the same architecture design as CLIP [1] for Vi T-B/32. The input resolution of image encoder is 224 224 and the maximum context length of text encoder is 77. We train our model using an Adam W [69] optimizer and the cosine learning rate scheduler with a linear warmup. Specifically, the learning rate linearly increases from 0 to the peak value within 10% of the total steps, and then decreases with a cosine anneal strategy. Our learning rate is set to 5e-7 and train the model for eight epochs. |