DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models

• URL: http://arxiv.org/abs/2403.10081v3
• Date: Sat, 21 Sep 2024 08:27:16 GMT
• Title: DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models
• Authors: Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, Yiqun Liu,
• Abstract summary: We introduce Dynamic Retrieval Augmented Generation based on the real-time Information Needs of Large Language Models (LLMs) Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process. Experimental results show that DRAGIN achieves superior performance on all tasks, demonstrating the effectiveness of our method.
• Score: 12.580730377998158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is triggered (determining what to retrieve). However, current dynamic RAG methods fall short in both aspects. Firstly, the strategies for deciding when to retrieve often rely on static rules. Moreover, the strategies for deciding what to retrieve typically limit themselves to the LLM's most recent sentence or the last few tokens, while the LLM's real-time information needs may span across the entire context. To overcome these limitations, we introduce a new framework, DRAGIN, i.e., Dynamic Retrieval Augmented Generation based on the real-time Information Needs of LLMs. Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process. We evaluate DRAGIN along with existing methods comprehensively over 4 knowledge-intensive generation datasets. Experimental results show that DRAGIN achieves superior performance on all tasks, demonstrating the effectiveness of our method. We have open-sourced all the code, data, and models in GitHub: https://github.com/oneal2000/DRAGIN/tree/main

Related papers

• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism [2.919891871101241]
  Transformers have a quadratic scaling of computational complexity with input size. Retrieval-augmented generation (RAG) can better handle longer contexts by using a retrieval system. We introduce a novel approach, Inner Loop Memory Augmented Tree Retrieval (ILM-TR)
  arXiv  Detail & Related papers  (2024-10-11T19:49:05Z)
• 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)
• MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery [24.38640001674072]
  Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases. Existing RAG systems are primarily effective for straightforward question-answering tasks. We propose MemoRAG, a novel retrieval-augmented generation paradigm empowered by long-term memory.
  arXiv  Detail & Related papers  (2024-09-09T13:20:31Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks [20.390672895839757]
  Retrieval-augmented generation (RAG) has emerged as a popular solution to enhance factual accuracy. Traditional retrieval modules often rely on large document index and disconnect with generative tasks. We propose textbfCorpusLM, a unified language model that integrates generative retrieval, closed-book generation, and RAG.
  arXiv  Detail & Related papers  (2024-02-02T06:44:22Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• Active Retrieval Augmented Generation [123.68874416084499]
  Augmenting large language models (LMs) by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content.
  arXiv  Detail & Related papers  (2023-05-11T17:13:40Z)
• Large Language Models are Strong Zero-Shot Retriever [89.16756291653371]
  We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM.
  arXiv  Detail & Related papers  (2023-04-27T14:45:55Z)

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.