Controlling Multimodal LLMs via Reward-guided Decoding
Descripción general
Resumen del artículo
This paper introduces Multimodal Reward-Guided Decoding (MRGD), a new technique to reduce hallucinations in MLLM-generated image captions by incorporating rewards for both precision and recall during decoding. This method offers control over this trade-off at inference time, achieving superior hallucination mitigation and recall compared to existing methods. The authors also demonstrate a trade-off between visual grounding and computational cost during inference, controlled by the search breadth.
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This paper introduces a new method to control what multimodal large language models (MLLMs, i.e., models that can process images and text) say, especially for describing images. It uses rewards to guide the model towards making more precise statements about objects observed in an image, reducing hallucinations.
Posibles conflictos de intereses
Some authors are affiliated with Meta, which has a vested interest in developing MLLMs.
Limitaciones identificadas
Explicación de la calificación
This paper presents a novel and valuable approach to controlling MLLM outputs during inference, showing improvements in reducing hallucinations while offering flexibility in controlling the trade-off between precision and recall. While limitations exist regarding the evaluation scope and computational cost, the method's novelty, effectiveness, and potential impact warrant a strong rating.
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