3 accepted papers
Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge for multi-hop re
We present Multi-Modal Multi-Task Unified Embedding Model (M3T-UEM), a framework that advances vision-language matching and retrieval by leveraging a large language model (LLM) backbone. While concurrent LLM-based approaches like VLM2VEC, MM-Embed, NV-Embed, and MM-GEM have demonstrated impressive c…
Spoken language understanding (SLU) is the task of inferring the semantics of spoken utterances. Traditionally, this has been achieved with a cascading combination of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) modules that are optimized separately, which can lead to…