In the field of emergent language, efforts have traditionally focused on developing communication protocols through interactions between agents in referential games. However, the aspect of internal language learning, where language serves not only as a communicative tool with others but also as a means for individual thinking, self-reflection, and problem-solving remains underexplored. Developing a language through self-play, without another agent's involvement, poses a unique challenge. It requires an agent to craft symbolic representations and train them using direct gradient methods. The challenge here is that if an agent attempts to learn symbolic representations through self-play using conventional modeling and techniques such as REINFORCE, the solution will offer no advantage over previous multi-agent approaches. We introduce VQEL, a novel method that incorporates Vector Quantization into the agents' architecture, enabling them to autonomously invent and develop discrete symbolic representations in a self-play referential game. Following the self-play phase, agents can enhance their language through reinforcement learning and interactions with other agents in the mutual-play phase. Our experiments across various datasets demonstrate that VQEL not only outperforms the traditional REINFORCE method but also benefits from improved control and reduced susceptibility to collapse, thanks to the incorporation of vector quantization.

The rapid advancement of Text-to-Image(T2I) generative models has enabled the synthesis of high-quality images guided by textual descriptions. Despite this significant progress, these models are often susceptible in generating contents that contradict the input text, which poses a challenge to their reliability and practical deployment. To address this problem, we introduce a novel diffusion-based framework to significantly enhance the alignment of generated images with their corresponding descriptions, addressing the inconsistency between visual output and textual input. Our framework is built upon a comprehensive analysis of inconsistency phenomena, categorizing them based on their manifestation in the image. Leveraging a state-of-the-art large language module, we first extract objects and construct a knowledge graph to predict the locations of these objects in potentially generated images. We then integrate a state-of-the-art controllable image generation model with a visual text generation module to generate an image that is consistent with the original prompt, guided by the predicted object locations. Through extensive experiments on an advanced multimodal hallucination benchmark, we demonstrate the efficacy of our approach in accurately generating the images without the inconsistency with the original prompt.