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..
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Authors: Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar, Utkarsh Tyagi, Oriol Nieto, Zeyu Jin, Dinesh Manocha
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple visual reasoning benchmarks and LVLMs demonstrate that VDGD consistently outperforms existing baselines 2% 33%. (Abstract) and We evaluate six LVLMs LLa VA-v1, LLa VA-v1.5, LLa VA-v1.6, m PLUG-Owl2, Intern LM-X, and Cog VLM all built on a 7B parameter language model. The models are tested across seven benchmarks: AMBER (visual recognition), Synth Do G (OCR), MMMU (expert-level reasoning), Math Vista and MATH-Vision (mathematical reasoning), MMC (chart understanding), and MME and Hallusion Bench. (Section 3) |
| Researcher Affiliation | Collaboration | 1University of Maryland, College Park, USA, 2Adobe, USA |
| Pseudocode | Yes | Algorithm 1 Categorizing Visual Hallucinations |
| Open Source Code | Yes | We provide our code here: https://sreyan88.github.io/VDGD/ |
| Open Datasets | Yes | For evaluation, we employ a variety of standard benchmarks focused on reasoning and information-seeking tasks. These include LLa VA-Bench, MM-Vet (Yu et al., 2023), MMBench (Liu et al., 2023d), MME (Fu et al., 2023), Math Vista (test-mini subset), Math Vision, and MMMU (validation set). (Section 5.2) and Va LLu We propose Va LLu benchmark which is sourced from Oven, MMMU, MMC, Math Vista, Hallusion Bench, MATH-Vision and MME. This dataset is licensed under all the licenses of the original benchmarks that it was sourced from. (Section H) |
| Dataset Splits | Yes | Math Vista (test-mini subset), Math Vision, and MMMU (validation set). (Section 5.2) and Synth Do G (Kim et al., 2022)... consists of 65.5k training and 500 validation entries. (Section H) |
| Hardware Specification | Yes | All our analysis, inference and baseline experiments are conducted on a node of 4 NVIDIA RTX A6000 GPUs, with 128GB RAM and 10 CPU cores. |
| Software Dependencies | Yes | The evaluations are conducted using gpt-4-turbo-2024-04-09 model. |
| Experiment Setup | Yes | We employ greedy decoding for all methods as we find no difference in performance on sampling. For Vanilla-sampling with multinomial sampling-based decoding (top-p=0.5 and temperature=0.7). (Section 5.2) and where α is a hyper-parameter between [0,1]. (Equation 2, Section 5.1). All results are averaged across 3 runs. (Section 5.2) |