Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog
This paper summarizes our work on the first track of the ninth Dialog System Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access". The goal of the task is to generate responses to user turns in a task-oriented dialog that require knowledge from unstructured documents. The task is divided into three subtasks: detection, selection and generation. In order to be compute efficient, we formulate the selection problem in terms of hierarchical classification steps. We achieve our best results with this model. Alternatively, we employ siamese sequence embedding models, referred to as Dense Knowledge Retrieval, to retrieve relevant documents. This method further reduces the computation time by a factor of more than 100x at the cost of degradation in R@1 of 5-6% compared to the first model. Then for either approach, we use Retrieval Augmented Generation to generate responses based on multiple selected snippets and we show how the method can be used to fine-tune trained embeddings.
Code (1)
Tasks
RetrievalRetrieval-augmented GenerationSimilar Papers 제목 키워드 기반
Retrieval-Augmented Machine Translation with Unstructured Knowledge
Retrieval-augmented generation (RAG) introduces additional information to enhance large language models (LLMs). In machine translation (MT), previous work typically retrieves in-context examples from paired MT corpora, o…
Knowledge GraphsMachine TranslationRAGRetrieval+3Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education
Large Language Models are increasingly being used for various tasks including content generation and as chatbots. Despite their impressive performances in general tasks, LLMs need to be aligned when applying for domain s…
Abstractive Text SummarizationHallucinationRAGRetrieval+1Retrieval-augmented Generation across Heterogeneous Knowledge
Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved state-of-the-art performance on many NLP downstream tasks. Compared with conventional pre-trained …
RAGRetrievalRetrieval-augmented GenerationmKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external knowledge sources into the generation p…
Visual Question AnsweringKnowledge GraphsKnowledge Graph-extended Retrieval Augmented Generation for Question Answering
Large Language Models (LLMs) and Knowledge Graphs (KGs) offer a promising approach to robust and explainable Question Answering (QA). While LLMs excel at natural language understanding, they suffer from knowledge gaps an…
In-Context LearningInformation RetrievalKnowledge GraphsNatural Language Understanding+4