Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Benchmarking Deletion Metrics with the Principled Explanations
Authors: Yipei Wang, Xiaoqian Wang
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,069 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,174 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to 1) Validate TRACE s optimality and the capability of serving as the principled explanation; and 2) Use TRACE to assess the impact of different settings (as discussed in Section 3) to address the OOD concern in deletion metric. |
| Researcher Affiliation | Academia | 1Elmore Family School of Electrical and Computer Engineering, Purdue University, IN, USA. Correspondence to: Xiaoqian Wang <EMAIL>. |
| Pseudocode | Yes | The associated pseudo-code is provided in Appendix D. |
| Open Source Code | Yes | The implementation of TRACE is available as open source at Git Hub. |
| Open Datasets | Yes | Experiments utilize the Image Net1k (ILSVRC2012) dataset (Deng et al., 2009) with images resized to 224 224. |
| Dataset Splits | Yes | The results are demonstrated in Figure 2(b). We present this comparison using TRACE-SA-Le Mo. And the resolution of both TRACE and the deletion metric is set to t = 7 7 = 49. |
| Hardware Specification | Yes | Experiments are carried out on Intel(R) Core(TM) i9-9960X CPU @ 3.10GHz with Quadro RTX 6000 GPUs. |
| Software Dependencies | No | The paper mentions "torchvision" but does not specify a version number or provide version numbers for any other software libraries or tools. |
| Experiment Setup | Yes | For SA, we use K = 5000 iterations, initial temperatures of T0 = 2 for -y and T0 = 0.1 for -p, and a cooling rate of η = 0.999. |