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..
Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation
Authors: Yaoyuan Liang, Zhuojun Cai, Jian Xu, Guanbo Huang, Yiran Wang, Xiao Liang, Jiahao Liu, Ziran Li, Jingang Wang, Shao-Lun Huang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the Ref COCOg and PHD benchmarks show that our proposed framework could outperform existing methods on both semantic and hallucination-related metrics. |
| Researcher Affiliation | Collaboration | 1Tsinghua Shenzhen International Graduate School, Tsinghua University 2Meituan Inc. |
| Pseudocode | Yes | Algorithm 1 Layer Prior Importance Calculation |
| Open Source Code | Yes | Code will be made available in https://github.com/Glupayy/unleash-eliminate. |
| Open Datasets | Yes | Extensive experiments conducted on the Ref COCOg [33] and PHD [28] benchmark |
| Dataset Splits | Yes | we randomly extracted K = 2000 samples from the Ref COCOg training set to form the triplets (I, M, Y)... Extensive experiments conducted on the Ref COCOg [33] and PHD [28] benchmark |
| Hardware Specification | No | The paper does not specify particular GPU or CPU models, memory, or other detailed hardware components used for the experiments. It only mentions 'GPUs' in the NeurIPS checklist response. |
| Software Dependencies | No | The paper mentions models like Osprey-7b and GLaMM, but it does not specify any software dependencies (e.g., Python, PyTorch, or specific library versions) with version numbers. |
| Experiment Setup | Yes | We included an analysis of the baseline region-level MLLM model, Osprey-7b, performing at both lower (t = 0.2) and higher (t = 0.9) temperature settings... we set α = 0.1 in the implementation... we randomly extracted K = 2000 samples... The first 32 layers (where layer 0 is the embedding layer) of the Osprey-7b model were organized into four groups: [0, 7], [8, 15], [16, 23], and [24, 31]. |