Retrieval-augmented reasoning with lean language models

• URL: http://arxiv.org/abs/2508.11386v1
• Date: Fri, 15 Aug 2025 10:38:15 GMT
• Title: Retrieval-augmented reasoning with lean language models
• Authors: Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas, Rosie Wood, Penelope Yong, Lionel Tarassenko, Mark Girolami, James Geddes, Andrew Duncan,
• Abstract summary: We develop a retrieval augmented conversational agent capable of interpreting complex, domain-specific queries.<n>Our system integrates a dense retriever with fine-tuned Qwen2.5-Instruct models.<n>All implementation details and code are publicly released to support and adaptation across domains.
• Score: 5.615564811138556
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG systems typically rely on large-scale models and external APIs, our work addresses the increasing demand for performant and privacy-preserving solutions deployable in resource-constrained or secure environments. Building on recent developments in test-time scaling and small-scale reasoning models, we develop a retrieval augmented conversational agent capable of interpreting complex, domain-specific queries using a lightweight backbone model. Our system integrates a dense retriever with fine-tuned Qwen2.5-Instruct models, using synthetic query generation and reasoning traces derived from frontier models (e.g., DeepSeek-R1) over a curated corpus, in this case, the NHS A-to-Z condition pages. We explore the impact of summarisation-based document compression, synthetic data design, and reasoning-aware fine-tuning on model performance. Evaluation against both non-reasoning and general-purpose lean models demonstrates that our domain-specific fine-tuning approach yields substantial gains in answer accuracy and consistency, approaching frontier-level performance while remaining feasible for local deployment. All implementation details and code are publicly released to support reproducibility and adaptation across domains.

Related papers

• Generative Data Transformation: From Mixed to Unified Data [57.84692191369066]
  textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
  arXiv  Detail & Related papers  (2026-02-26T08:30:09Z)
• Domain-Specific Data Generation Framework for RAG Adaptation [58.20906914537952]
  Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models with external retrieval to enable domain-grounded responses.<n>We propose RAGen, a framework for generating domain-grounded question-answer-context (QAC) triples tailored to diverse RAG adaptation approaches.
  arXiv  Detail & Related papers  (2025-10-13T09:59:49Z)
• RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering [50.42577862494645]
  We present RAG-IGBench, a benchmark designed to evaluate the task of Interleaved Generation based on Retrieval-Augmented Generation (RAG-IG) in open-domain question answering.<n>RAG-IG integrates multimodal large language models (MLLMs) with retrieval mechanisms, enabling the models to access external image-text information for generating coherent multimodal content.
  arXiv  Detail & Related papers  (2025-10-11T03:06:39Z)
• Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search [54.987957691350665]
  Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query.<n>Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications.<n>We propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search.
  arXiv  Detail & Related papers  (2025-08-28T08:51:51Z)
• SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension [77.93156509994994]
  We show how to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.<n>Existing embedding models are not well-equipped to encode such situated context effectively.<n>Our method substantially outperforms state-of-the-art embedding models.
  arXiv  Detail & Related papers  (2025-08-03T23:59:31Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models generate target documents directly from a query.<n>We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models [0.8399688944263842]
  Large Language Models (LLMs) have the capability to understand and generate human-like text from input queries. This study extends this concept to the integration of LLMs within Retrieval-Augmented Generation (RAG) pipelines. We evaluate the impact of fine-tuning on the LLMs' capacity for data extraction and contextual understanding.
  arXiv  Detail & Related papers  (2024-06-17T04:35:17Z)
• Dynamic Retrieval-Augmented Generation [4.741884506444161]
  We propose a novel approach for the Dynamic Retrieval-Augmented Generation (DRAG) DRAG injects compressed embeddings of the retrieved entities into the generative model. Our approach achieves several targets: (1) lifting the length limitations of the context window, saving on the prompt size; (2) allowing huge expansion of the number of retrieval entities available for the context; (3) alleviating the problem of misspelling or failing to find relevant entity names.
  arXiv  Detail & Related papers  (2023-12-14T14:26:57Z)
• Unsupervised Domain Adaption for Neural Information Retrieval [18.97486314518283]
  We compare synthetic annotation by query generation using Large Language Models or rule-based string manipulation. We find that Large Language Models outperform rule-based methods in all scenarios by a large margin. In addition we explore several sizes of open Large Language Models to generate synthetic data and find that a medium-sized model suffices.
  arXiv  Detail & Related papers  (2023-10-13T18:27:33Z)
• Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning [79.53130089003986]
  Large Language Models (LLMs) have become a feasible solution for handling tasks in various domains. In this paper, we introduce how to fine-tune a LLM model that can be privately deployed for content moderation.
  arXiv  Detail & Related papers  (2023-10-05T09:09:44Z)
• Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering [0.0]
  We propose a framework to efficiently develop a generative question-answering model for open-book question-answering over a proprietary collection of text documents. The framework adapts a retriever augmented generation (RAG) model to the target domain using supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-12T04:44:31Z)
• Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction [67.54420015049732]
  Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. Existing benchmarks are limited to two domains and do not evaluate model performance on unseen domains. We introduce a domain-expanded benchmark by annotating samples from diverse domains, enabling evaluation of models in both in-domain and out-of-domain settings.
  arXiv  Detail & Related papers  (2023-05-23T18:01:49Z)

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.