FINER: MLLMs Hallucinate under Fine-grained Negative Queries

Rui Xiao, Sanghwan Kim, Yongqin Xian, Zeynep Akata, Stephan Alaniz; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 36235-36244

Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce **FI**ne-grained **NE**gative que**R**ies (**FINER**), alongside two benchmarks: **FINER-CompreCap** and **FINER-DOCCI**. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and "what" questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose **FINER-Tuning**, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at https://explainableml.github.io/finer-project/.