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..
ContextRef: Evaluating Referenceless Metrics for Image Description Generation
Authors: Elisa Kreiss, Eric Zelikman, Christopher Potts, Nick Haber
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we introduce Context Ref, a benchmark for assessing referenceless metrics for such alignment. Context Ref has two components: human ratings along a variety of established quality dimensions, and ten diverse robustness checks designed to uncover fundamental weaknesses. A crucial aspect of Context Ref is that images and descriptions are presented in context, reflecting prior work showing that context is important for description quality. Using Context Ref, we assess a variety of pretrained models, scoring functions, and techniques for incorporating context. None of the methods is successful with Context Ref, but we show that careful fine-tuning yields substantial improvements. |
| Researcher Affiliation | Academia | Elisa Kreiss , Eric Zelikman , Christopher Potts, Nick Haber EMAIL, EMAIL |
| Pseudocode | No | The paper includes code snippets in Appendix H to illustrate prompt construction, but these are not presented as formal pseudocode or algorithm blocks for the overall method. |
| Open Source Code | Yes | 1All data and code are made available at https://github.com/elisakreiss/contextref. |
| Open Datasets | Yes | The data was randomly sampled from the English language subset of the WIT dataset (Srinivasan et al., 2021). |
| Dataset Splits | No | The paper states, "We split the data into an 80% train and 20% test split," but it does not explicitly mention a separate validation set or its proportion. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions various models and their base components (e.g., "Open Flamingo v2," "GPT-2 large," "BLIP-2 variants with Flan-T5 XXL"), but it does not list specific version numbers for underlying software libraries or dependencies like PyTorch, TensorFlow, or CUDA, which are crucial for full reproducibility. |
| Experiment Setup | Yes | We first trained the best-performing CLIP model for 0.5 epochs with a learning rate of 5e 6 and a batch size of 64, with the Adam optimizer (Kingma & Ba, 2014). |