Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency

• URL: http://arxiv.org/abs/2505.08445v1
• Date: Tue, 13 May 2025 11:13:27 GMT
• Title: Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency
• Authors: Adel Ammar, Anis Koubaa, Omer Nacar, Wadii Boulila,
• Abstract summary: Large language models achieve high task performance yet often hallucinate or rely on outdated knowledge.<n>Retrieval-augmented generation (RAG) addresses these gaps by coupling generation with external search.
• Score: 1.6177972328875518
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models achieve high task performance yet often hallucinate or rely on outdated knowledge. Retrieval-augmented generation (RAG) addresses these gaps by coupling generation with external search. We analyse how hyperparameters influence speed and quality in RAG systems, covering Chroma and Faiss vector stores, chunking policies, cross-encoder re-ranking, and temperature, and we evaluate six metrics: faithfulness, answer correctness, answer relevancy, context precision, context recall, and answer similarity. Chroma processes queries 13% faster, whereas Faiss yields higher retrieval precision, revealing a clear speed-accuracy trade-off. Naive fixed-length chunking with small windows and minimal overlap outperforms semantic segmentation while remaining the quickest option. Re-ranking provides modest gains in retrieval quality yet increases runtime by roughly a factor of 5, so its usefulness depends on latency constraints. These results help practitioners balance computational cost and accuracy when tuning RAG systems for transparent, up-to-date responses. Finally, we re-evaluate the top configurations with a corrective RAG workflow and show that their advantages persist when the model can iteratively request additional evidence. We obtain a near-perfect context precision (99%), which demonstrates that RAG systems can achieve extremely high retrieval accuracy with the right combination of hyperparameters, with significant implications for applications where retrieval quality directly impacts downstream task performance, such as clinical decision support in healthcare.

Related papers

• Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning [0.0]
  Large Language Models (LLMs) with Knowledge Graphs (KGs)<n>Cognane is a modular framework for end-to-end KG construction and retrieval.<n>We optimize parameters related to chunking, graph construction, retrieval, and prompting.
  arXiv  Detail & Related papers  (2025-05-30T11:27:59Z)
• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Leveraging Approximate Caching for Faster Retrieval-Augmented Generation [1.3450852784287828]
  Retrieval-augmented generation (RAG) enhances the reliability of large language model (LLM) answers by integrating external knowledge.<n>RAG increases the end-to-end inference time since looking for relevant documents from large vector databases is computationally expensive.<n>We introduce Proximity, an approximate key-value cache that optimize the RAG workflow by leveraging similarities in user queries.
  arXiv  Detail & Related papers  (2025-03-07T15:54:04Z)
• Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control [52.405085773954596]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to mitigate large language model hallucinations.<n>Existing RAG frameworks often apply retrieval indiscriminately,leading to inefficiencies-over-retrieving.<n>We introduce a novel user-controllable RAG framework that enables dynamic adjustment of the accuracy-cost trade-off.
  arXiv  Detail & Related papers  (2025-02-17T18:56:20Z)
• 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)
• Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks [11.053340674721005]
  Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources.<n>This paper proposes an alternative paradigm, cache-augmented generation (CAG) that bypasses real-time retrieval.
  arXiv  Detail & Related papers  (2024-12-20T06:58:32Z)
• Toward Optimal Search and Retrieval for RAG [39.69494982983534]
  Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs) Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA)
  arXiv  Detail & Related papers  (2024-11-11T22:06:51Z)
• Switchable Decision: Dynamic Neural Generation Networks [98.61113699324429]
  We propose a switchable decision to accelerate inference by dynamically assigning resources for each data instance. Our method benefits from less cost during inference while keeping the same accuracy.
  arXiv  Detail & Related papers  (2024-05-07T17:44:54Z)
• A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning [8.659973888018781]
  A Reinforcement Learning (RL) system depends on a set of initial conditions that affect the system's performance. We propose a framework based on integrating complex event processing and temporal models, to alleviate these trade-offs. We tested the proposed approach in a 5G mobile communications case study that uses DQN, a variant of RL, for its decision-making.
  arXiv  Detail & Related papers  (2023-03-09T11:30:40Z)
• AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [72.54359545547904]
  We propose a gradient-based subset selection framework for hyper- parameter tuning. We show that using gradient-based data subsets for hyper- parameter tuning achieves significantly faster turnaround times and speedups of 3$times$-30$times$.
  arXiv  Detail & Related papers  (2022-03-15T19:25:01Z)
• FasterPose: A Faster Simple Baseline for Human Pose Estimation [65.8413964785972]
  We propose a design paradigm for cost-effective network with LR representation for efficient pose estimation, named FasterPose. We study the training behavior of FasterPose, and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence. Compared with the previously dominant network of pose estimation, our method reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy.
  arXiv  Detail & Related papers  (2021-07-07T13:39:08Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)

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.