Retrieval-Augmented Generation (RAG) has significantly advanced open-domain question answering systems by incorporating external knowledge into large language models. Despite its effectiveness, existing RAG pipelines suffer from critical efficiency limitations. In particular, modern transformer-based generators exhibit quadratic or higher computational complexity with respect to input sequence length and hidden dimensionality, leading to substantial inference latency as model scales and contextual inputs increase. This issue is exacerbated in RAG settings, where retrieved contexts substantially expand the input prompt. To alleviate this challenge, we propose an effective compression-based RAG framework, ConvX, that directly leverages indexed dense representations produced by a retriever, entirely substituting to long text contexts. Our approach expands a single dense representation into a fixed number of memory slots using a lightweight converter to provide rich lexical information. This design enables efficient knowledge integration while significantly reducing input length and computational overhead. Empirical evaluations demonstrate that the proposed model achieves competitive performances compared to the existing state-of-the-art model that uses a large ad-hoc context compressor, while offering substantially improved inference efficiency.

The Situated Interactive MultiModal Conversations (SIMMC2.1) Challenge 2022 is hosted by the Eleventh Dialog System Technology Challenge (DSTC11). This is the third consecutive year multimodal dialog systems have been selected as an official track of the competition, promoted by the continued interest in the research community. The task of SIMMC is to create a shopping assistant agent that can communicate with customers in a virtual store. It requires processing store scenes and product catalogs along with the customer’s request. The task is decomposed into four steps and each becomes a subtask. In this work, we explore the common approaches to modeling multimodality and find the method with the most potential. We also identify a discrepancy in using pretrained language models for dialog tasks and devise a simple domain-adaptation method. Our model came in third place for object coreferencing, dialog state tracking, and response generation tasks.