Unified Generative and Discriminative Training for Multi-modal Large Language Models

TL;DR

This paper introduces a unified training approach for multi-modal large language models that combines generative and discriminative paradigms, improving performance in complex tasks, fine-grained discrimination, and retrieval tasks.

Contribution

It proposes a structure-induced training strategy with dynamic sequence alignment and a novel semantic kernel to unify generative and discriminative training for vision-language models.

Findings

Achieves state-of-the-art results in multiple generative tasks.

Surpasses benchmarks in fine-grained image-text retrieval.

Enhances model's ability to distinguish fine-grained semantics.

Abstract

In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input samples and the MLLM's hidden state. This approach enhances the MLLM's ability to capture global semantics and distinguish fine-grained semantics. By leveraging dynamic sequence alignment within the Dynamic Time Warping framework and integrating a novel kernel for fine-grained semantic differentiation, our method effectively balances generative and discriminative tasks. Extensive experiments demonstrate the effectiveness of our approach, achieving state-of-the-art results in multiple generative tasks, especially those requiring cognitive and discrimination abilities. Additionally, our method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks. By employing a retrieval-augmented generation strategy, our approach further enhances performance in some generative tasks within one model, offering a promising direction for future research in vision-language modeling.