Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models

ICLR 2025 Conference Submission21 Authors

Keywords: Hallucination mitigation, MLLMs, visual retracing, training-free

TL;DR: In this work, we introduce Memory-space Visual Retracing, a novel hallucination mitigation paradigm.

Abstract: Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) are susceptible to hallucinations, especially assertively fabricating content not present in the visual inputs. To address the aforementioned challenge, we follow a common cognitive process - \textit{when one's initial memory of critical on-sight details fades, replenishing visual memory is essential to seek a factual and accurate answer.} Therefore, we introduce Memory-space Visual Retracing (MemVR), a novel hallucination mitigation paradigm that without the need for external knowledge retrieval or additional fine-tuning. In particular, we treat visual tokens as supplementary evidence to be reinjected into MLLMs via Feed Forward Network (FFN) as “key-value memory” at the middle trigger layer, \textit{i.e.}, when the model is uncertain about visual memories in the layer. Comprehensive experimental evaluations demonstrate that \modelname significantly mitigates hallucination issues across various MLLMs and excels in general benchmarks without incurring added time overhead, thus emphasizing its potential for widespread applicability.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 21