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..
Large Language Models as Automated Aligners for benchmarking Vision-Language Models
Authors: Yuanfeng Ji, Chongjian GE, Weikai Kong, Enze Xie, Zhengying Liu, Zhenguo Li, Ping Luo
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our validation results reveal that LLMs are proficient in both evaluation data curation and model assessment, achieving an average agreement rate of 85%. We envision Auto-Bench as a flexible, scalable, and comprehensive benchmark for evaluating the evolving sophisticated VLMs. |
| Researcher Affiliation | Collaboration | 1The University of Hong Kong, 2Huawei Noah s Ark Lab |
| Pseudocode | No | The paper describes its processes and pipeline in textual form but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Data and code will be released. |
| Open Datasets | Yes | Specifically, we obtain COCO (Lin et al., 2014) images and their associated captions, instances, relations, and text annotations from its extended datasets (Chen et al., 2015; Lin et al., 2014; Yang et al., 2022; Veit et al., 2016). |
| Dataset Splits | Yes | To the best of our knowledge, Auto-Bench represents the most extensive known collection of its kind. ...we employed a crowdsourcing approach to carefully select about 28.5K high quality samples to form a validation dataset, which is then used for performance evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions specific models and APIs like GPT-4, GPT-3.5 Turbo, and Simcse, but does not specify version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | The training configurations employed in the instruction-tuning stage of Mini GPT-4 were followed with 5 epochs of SFT. |