AI + Search = RAG

Retrieval-Augmented Generation for Large Language Models: A Survey

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang

Key Topics: Retrieval-Augmented Generation, Large Language Models, NLP Optimization, Knowledge Integration, Evaluation Metrics

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Result: This paper discusses the Retrieval-Augmented Generation (RAG) model in the field of Natural Language Processing (NLP). RAG models aim to optimize the performance of Large Language Models (LLMs) by incorporating external knowledge from databases. The paper outlines the evolution of RAG research paradigms from Naive RAG to Advanced RAG and Modular RAG. It covers the core components of RAG, including Retrieval, Generation, and Augmentation, and discusses optimization methods in retrieval and fine-tuning in generation. The paper also delves into the evaluation of RAG models, highlighting various downstream tasks, datasets, and evaluation metrics. Overall, the paper provides insights into the advancements and challenges in RAG research.

ARAGOG: Advanced RAG Output Grading

Matouš Eibich, Shivay Nagpal, Alexander Fred-Ojala

Key Topics: Retrieval-Augmented Generation, Precision Optimization, Answer Similarity, Chunking Strategies, RAG Technique Evaluation

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Result: The paper explores the impact of various RAG techniques on retrieval precision and answer similarity in RAG systems. It evaluates techniques such as Sentence-window retrieval, Document Summary Index, HyDE, Query Expansion, and more. The study uses metrics like Retrieval Precision and Answer Similarity to assess the effectiveness of these techniques. It highlights the importance of chunking strategies in optimizing retrieval methods and discusses limitations such as model selection and data scope. The findings suggest that Sentence-window retrieval outperforms Document Summary Index in precision, with Document Summary Index with Cohere Rerank being a viable second option. The study emphasizes the need for diverse datasets and questions to enhance the generalizability of RAG techniques.

Query Rewriting for Retrieval-Augmented Large Language Models

Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan

Key Topics: Query Rewriting, Retrieval Augmentation, Large Language Models, Open-Domain QA, Trainable Modules

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Result: The paper introduces a novel framework, Rewrite-Retrieve-Read, aimed at enhancing retrieval augmentation in large language models. It focuses on a trainable query rewriter to refine the retrieval process, which is crucial for performance in knowledge-intensive tasks. The study underscores the significance of incorporating trainable components into otherwise opaque large language models and validates the benefits of query rewriting through tests on open-domain QA and multiple-choice QA tasks. It acknowledges the challenges in balancing generalization and specialization for downstream tasks and the dependency on multiple interactions with the LLM for each instance.

Precise Zero-Shot Dense Retrieval without Relevance Labels

Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan

Key Topics: Zero-Shot Learning, Dense Retrieval, Unsupervised Learning, Contrastive Encoding, Information Retrieval

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Result: The paper introduces a novel approach called Hypothetical Document Embeddings (HyDE) for building effective zero-shot dense retrieval systems without the need for relevance labels. By generating hypothetical documents and using an unsupervised contrastive encoder, the system captures relevance patterns. This method aims to generalize across tasks and work out-of-the-box without requiring relevance supervision.

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi

Key Topics: Language Models, Information Retrieval, Self-Reflection, Text Generation Quality, Factuality Enhancement

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Result: This paper introduces SELF-RAG, a framework that enhances large language models by incorporating retrieval and self-reflection. The framework aims to improve the quality and factuality of generated text. It involves a self-retrieval module that retrieves relevant information from the model's own knowledge base and a self-reflection module that evaluates the generated text based on the retrieved information. The framework is evaluated on various tasks, demonstrating its effectiveness in improving the performance of language models.

Context Tuning for Retrieval Augmented Generation

Raviteja Anantha, Tharun Bethi, Danil Vodianik, Srinivas Chappidi

Key Topics: Context Tuning, Retrieval Augmented Generation, Large Language Models, Semantic Search, Contextual Understanding

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Result: The paper introduces Context Tuning as a novel approach to improve the retrieval process for Large Language Models (LLMs), focusing on enhancing tool retrieval and plan generation. The authors propose a lightweight model that leverages various signals for context retrieval, showing that it can surpass the performance of GPT-4 based retrieval. The empirical results indicate a boost in Recall@K for context retrieval and tool retrieval tasks, as well as improved accuracy for LLM-based planners, highlighting the effectiveness of context augmentation in plan generation.

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Yubo Ma, Yixin Cao, YongChing Hong, Aixin Sun

Key Topics: Information Extraction, Few-shot Learning, Large Language Models, Filter-then-Rerank Paradigm, Model Integration

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Result: The research paper explores the challenges in instruction-tuning datasets for Information Extraction (IE) tasks and proposes a filter-then-rerank paradigm to enhance the performance of Large Language Models (LLMs) in few-shot scenarios. The study highlights the scarcity of IE-related tasks in existing datasets and the limitations of current task formats. By combining the strengths of LLMs and traditional models through the proposed paradigm, the research demonstrates improved performance on various IE tasks. The findings suggest that integrating both types of models can address the shortcomings of LLMs and enhance their effectiveness in information extraction.