Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models

• URL: http://arxiv.org/abs/2506.03989v1
• Date: Wed, 04 Jun 2025 14:16:28 GMT
• Title: Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models
• Authors: Alex Laitenberger, Christopher D. Manning, Nelson F. Liu,
• Abstract summary: We compare two recent multi-stage pipelines, ReadAgent and RAPTOR, against three baselines, including DOS RAG (Document's Original Structure RAG)<n> DOS RAG consistently matches or outperforms more intricate methods on multiple long-context QA benchmarks.<n>We recommend establishing DOS RAG as a simple yet strong baseline for future RAG evaluations.
• Score: 38.17736879002141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of long-context language models (LMs) capable of processing tens of thousands of tokens in a single pass, do multi-stage retrieval-augmented generation (RAG) pipelines still offer measurable benefits over simpler, single-stage approaches? To assess this question, we conduct a controlled evaluation for QA tasks under systematically scaled token budgets, comparing two recent multi-stage pipelines, ReadAgent and RAPTOR, against three baselines, including DOS RAG (Document's Original Structure RAG), a simple retrieve-then-read method that preserves original passage order. Despite its straightforward design, DOS RAG consistently matches or outperforms more intricate methods on multiple long-context QA benchmarks. We recommend establishing DOS RAG as a simple yet strong baseline for future RAG evaluations, pairing it with emerging embedding and language models to assess trade-offs between complexity and effectiveness as model capabilities evolve.

Related papers

• HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation [9.175609521889266]
  We introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG)<n>This method enhances the model's open-book examination capability by utilizing multi-level progressive chain-of-thought.<n>Experiments show that the HIRAG training strategy significantly improves the model's performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.
  arXiv  Detail & Related papers  (2025-07-08T06:53:28Z)
• SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling [70.01883340129204]
  Single-Pass.<n>with Reference-Guided Evaluation (SPARE)<n>Novel structured framework that enables single-pass, per-step annotation by aligning each solution step to one or multiple steps in a reference solution, accompanied by explicit reasoning for evaluation.<n>SPARE achieves competitive performance on challenging mathematical datasets while offering 2.6 times greater efficiency, requiring only 38% of the runtime.
  arXiv  Detail & Related papers  (2025-06-18T14:37:59Z)
• KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM Evaluation [3.618621510356872]
  KG-QAGen is a framework that extracts QA pairs at multiple complexity levels.<n>We construct a dataset of 20,139 QA pairs and open-source a part of it.<n>We evaluate 13 proprietary and open-source LLMs and observe that even the best-performing models are struggling with set-based comparisons.
  arXiv  Detail & Related papers  (2025-05-18T16:46:39Z)
• Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts [67.67746334493302]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks.<n>We propose a tri-encoder sequential retriever that models this process as a Markov Decision Process (MDP)<n>We show that our method consistently and significantly outperforms baselines, underscoring the importance of explicitly modeling inter-example dependencies.
  arXiv  Detail & Related papers  (2025-04-15T17:35:56Z)
• Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs [23.960451986662996]
  This paper proposes a method that emulates Retrieval Augmented Generation (RAG) through specialized prompt engineering and chain-of-thought reasoning.<n>We evaluate our approach on selected tasks from BABILong, which interleaves standard bAbI QA problems with large amounts of distractor text.
  arXiv  Detail & Related papers  (2025-02-18T02:49:40Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities [53.97515452727115]
  ChatQA 2 is a Llama 3.0-based model with a 128K context window.<n>We present a training recipe to extend the context window of Llama3-70B-base from 8K to 128K tokens.<n>We find that the performance of strong long-context LLMs using RAG improves when retrieving a larger number of chunks.
  arXiv  Detail & Related papers  (2024-07-19T17:35:47Z)
• BERGEN: A Benchmarking Library for Retrieval-Augmented Generation [26.158785168036662]
  Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments.
  arXiv  Detail & Related papers  (2024-07-01T09:09:27Z)
• Self-Prompting Large Language Models for Zero-Shot Open-Domain QA [67.08732962244301]
  Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. We propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of Large Language Models.
  arXiv  Detail & Related papers  (2022-12-16T18:23:43Z)
• Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models [56.268862325167575]
  This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs) We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. We evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task.
  arXiv  Detail & Related papers  (2020-04-04T11:07:54Z)

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.