open-source-vision-language-models-llava-15-qwen-vl-chat-qwen25-vl-llava-next-training-free-decoding CLM-0111In open-source vision-language models (LLaVA-1.5, Qwen-VL-Chat, Qwen2.5-VL, LLaVA-NeXT), a training-free decoding intervention that detects large layer-to-layer and step-to-step shifts in hidden states and nudges the diverging states back toward their prior value lowers object-hallucination rates, but the lower rate comes with fewer objects mentioned and shorter outputs.
Capability: Grounding answers in the image
Observed on
Open-source image-input LVLMs at 3B-7B scale, standard hallucination benchmarks (POPE, CHAIR, HallusionBench); not tested on closed models, video, or multi-image input..
Sources
- Measured on nine benchmarks across six backbones against vanilla decoding and training-free baselines (VCD, ICD, MemVR, OPERA). CHAIR shows Cs 47.6->28.2 and Ci 13.3->7.3 on LLaVA-1.5 with recall 80.6->72.0 and length 99.7->88.9. Authors report a length-normalized analysis in an appendix not included here, so the informativeness confound is claimed to be addressed but not verifiable from the given text. Single group, fixed thresholds, no seed variance reported.
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Related claims
- In open-source vision-language models (LLaVA-1.5, Qwen-VL-Chat, Qwen2.5-VL, LLaVA-NeXT), a training-free decoding intervention that detects large layer-to-layer and step-to-step shifts in hidden states and nudges the diverging states back toward their prior value lowers object-hallucination rates, but the lower rate comes with fewer objects mentioned and shorter outputs.Grounding answers in the image · reviewed by AI
- For training-free inference-time hallucination interventions in 7B-class vision-language models, increasing intervention strength (finer regional evidence partitioning, longer attention-routing windows, stronger textual perturbation) keeps lowering CHAIR object-hallucination scores while producing empty or reduced-coverage responses, so lower hallucination numbers at high intervention strength partly reflect the model saying less rather than grounding better.Grounding answers in the image · reviewed by AI
- For training-free inference-time hallucination mitigation in 7B vision- language models, reported gains on object-hallucination benchmarks are largely inseparable from reduced informativeness: hallucination rate and object recall/coverage move together, so a lower score can mean the model mentioned fewer visual entities rather than grounded better.Whether the measurement made the finding · reviewed by AI
- For 7-13B multimodal models, verifying each object claim with a frozen open-vocabulary detector lowers object hallucination only if evidence is pooled across semantically equivalent phrasings and checked for spatial agreement (the preferred boxes of different queries overlapping on one instance); a single-query maximum score is a weaker signal, and shuffling the spatial evidence across samples drives its discrimination to chance.Grounding answers in the image · reviewed by AI
- In 7B open vision-language models (LLaVA-1.5, Shikra), the per-token log-probability gap between a normal forward pass and one with visual features zeroed shrinks as a caption gets longer, and while that gap ranks hallucinated tokens slightly better than entropy or max-probability, its absolute separation is weak (AUROC around 0.57-0.59) and it fails entirely when aggregated to sentence level.Grounding answers in the image · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A replication where the same compensation leaves CHAIR recall and response length unchanged while hallucination rates still fall, or where hallucination rates do not fall at all on a held-out backbone. Drafted stance toward training-free-inference-time-hallucination-mitigation-7b-vision-language-models-reported-gains: supports -- On CHAIR the method cuts sentence- and instance-level hallucination rates while recall and average response length both fall, the same coupling of lower hallucination with reduced informativeness. Proposed technique, not catalogued: Hidden-state divergence compensation at decoding time.