Unified Active Retrieval for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2406.12534v4
• Date: Thu, 03 Oct 2024 02:39:56 GMT
• Title: Unified Active Retrieval for Retrieval Augmented Generation
• Authors: Qinyuan Cheng, Xiaonan Li, Shimin Li, Qin Zhu, Zhangyue Yin, Yunfan Shao, Linyang Li, Tianxiang Sun, Hang Yan, Xipeng Qiu,
• Abstract summary: In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Existing active retrieval methods face two challenges: 1. They usually rely on a single criterion, which struggles with handling various types of instructions. They depend on specialized and highly differentiated procedures, and thus combining them makes the RAG system more complicated.
• Score: 69.63003043712696
• License:
• Abstract: In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial for RAG, which is usually referred to as Active Retrieval. However, existing active retrieval methods face two challenges: 1. They usually rely on a single criterion, which struggles with handling various types of instructions. 2. They depend on specialized and highly differentiated procedures, and thus combining them makes the RAG system more complicated and leads to higher response latency. To address these challenges, we propose Unified Active Retrieval (UAR). UAR contains four orthogonal criteria and casts them into plug-and-play classification tasks, which achieves multifaceted retrieval timing judgements with negligible extra inference cost. We further introduce the Unified Active Retrieval Criteria (UAR-Criteria), designed to process diverse active retrieval scenarios through a standardized procedure. Experiments on four representative types of user instructions show that UAR significantly outperforms existing work on the retrieval timing judgement and the performance of downstream tasks, which shows the effectiveness of UAR and its helpfulness to downstream tasks.

Related papers

• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [102.31558123570437]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs) We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• Self-adaptive Multimodal Retrieval-Augmented Generation [0.0]
  We propose a new approach called Self-adaptive Multimodal Retrieval-Augmented Generation (SAM-RAG) SAM-RAG not only dynamically filters relevant documents based on the input query, including image captions when needed, but also verifies the quality of both the retrieved documents and the output. Extensive experimental results show that SAM-RAG surpasses existing state-of-the-art methods in both retrieval accuracy and response generation.
  arXiv  Detail & Related papers  (2024-10-15T06:39:35Z)
• P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task [94.08478298711789]
  Embodied Everyday Task is a popular task in the embodied AI community. Natural language instructions often lack explicit task planning. Extensive training is required to equip models with knowledge of the task environment.
  arXiv  Detail & Related papers  (2024-09-17T15:29:34Z)
• Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach [6.549143816134531]
  We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-07-18T02:19:00Z)
• Searching for Best Practices in Retrieval-Augmented Generation [31.438681543849224]
  Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. We suggest several strategies for deploying RAG that balance both performance and efficiency.
  arXiv  Detail & Related papers  (2024-07-01T12:06:34Z)
• DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
  RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA) We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
  arXiv  Detail & Related papers  (2024-06-11T15:15:33Z)
• Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation [9.390902237835457]
  We propose a new method to measure the task-specific accuracy of Retrieval-Augmented Large Language Models (RAG) Evaluation is performed by scoring the RAG on an automatically-generated synthetic exam composed of multiple choice questions.
  arXiv  Detail & Related papers  (2024-05-22T13:14:11Z)
• ReAct: Temporal Action Detection with Relational Queries [84.76646044604055]
  This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries. We first propose a relational attention mechanism in the decoder, which guides the attention among queries based on their relations. Lastly, we propose to predict the localization quality of each action query at inference in order to distinguish high-quality queries.
  arXiv  Detail & Related papers  (2022-07-14T17:46:37Z)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.