BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2508.05100v1
• Date: Thu, 07 Aug 2025 07:37:25 GMT
• Title: BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation
• Authors: Yuhao Wang, Ruiyang Ren, Yucheng Wang, Jing Liu, Wayne Xin Zhao, Hua Wu, Haifeng Wang,
• Abstract summary: We propose the balanced entropy-engineered RAG (BEE-RAG) framework to improve adaptability of RAG systems to varying context lengths.<n>BEE-RAG separates attention sensitivity from context length, ensuring a stable entropy level.<n>Building upon this, we introduce a zero-shot inference strategy for multi-importance estimation and a parameter-efficient adaptive fine-tuning mechanism.
• Score: 77.10390725623125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations of LLMs. However, due to the typically large volume of retrieved information, RAG tends to operate with long context lengths. From the perspective of entropy engineering, we identify unconstrained entropy growth and attention dilution due to long retrieval context as significant factors affecting RAG performance. In this paper, we propose the balanced entropy-engineered RAG (BEE-RAG) framework, which improves the adaptability of RAG systems to varying context lengths through the principle of entropy invariance. By leveraging balanced context entropy to reformulate attention dynamics, BEE-RAG separates attention sensitivity from context length, ensuring a stable entropy level. Building upon this, we introduce a zero-shot inference strategy for multi-importance estimation and a parameter-efficient adaptive fine-tuning mechanism to obtain the optimal balancing factor for different settings. Extensive experiments across multiple RAG tasks demonstrate the effectiveness of BEE-RAG.

Related papers

• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence [5.066415370344766]
  Retrieval-Augmented Generation (RAG) is a technique to enhance factual accuracy by integrating external knowledge into the generation process.<n>This paper introduces Radiant, a framework that merges RAG with alignment designed to optimize the interplay between retrieved evidence and generated content.
  arXiv  Detail & Related papers  (2025-06-28T21:40:35Z)
• Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation [32.30660197797758]
  We introduce a novel Jensen-Shannon Divergence driven method to Attribute Response to Context (ARC-JSD)<n>We demonstrate superior accuracy and significant computational efficiency improvements compared to the previous surrogate-based method.<n>Our mechanistic analysis reveals specific attention heads and multilayer perceptron (MLP) layers responsible for context attribution.
  arXiv  Detail & Related papers  (2025-05-22T09:04:03Z)
• RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving [9.962031642362813]
  Retrieval-augmented generation (RAG) is emerging as a popular approach for reliable LLM serving.<n>RAG is a structured abstraction that captures the wide range of RAG algorithms.<n> RAGO is a system optimization framework for efficient RAG serving.
  arXiv  Detail & Related papers  (2025-03-18T18:58:13Z)
• 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)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• Introducing a new hyper-parameter for RAG: Context Window Utilization [0.0]
  RAG systems enhance generative models by incorporating relevant information retrieved from external knowledge bases. The size of the text chunks retrieved and processed is a critical factor influencing RAG performance. This study aims to identify the optimal chunk size that maximizes answer generation quality.
  arXiv  Detail & Related papers  (2024-07-29T08:38:14Z)
• RAGGED: Towards Informed Design of Scalable and Stable RAG Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge.<n>RAGGED is a framework for systematically evaluating RAG systems.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• Gated Recurrent Neural Networks with Weighted Time-Delay Feedback [55.596897987498174]
  We present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.<n>Our proposed model, named $tau$-GRU, is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs)
  arXiv  Detail & Related papers  (2022-12-01T02:26:34Z)

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.