mlsanityData to 7 Oct 2026

Enhancing LLMs through human feedback: a journey towards self-improvement

arXiv 2607.11267 ↗First posted 13 Jul 2026cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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Abstract (arXiv)

In the rapidly evolving landscape of information retrieval systems, the ability to adapt and improve through user feedback is paramount. This study introduces a novel methodology for refining the performance of a primary Retrieval Augmented Generation (RAG) system by strategically integrating an auxiliary feedback RAG system. By systematically harnessing human-generated feedback, the approach aims to enhance the accuracy, relevance, and overall quality of responses, driving the system towards self-improvement. Central to this methodology is a human-in-the-loop implementation, where user feedback is continuously collected, classified, and integrated into the inference workflow, enabling the system to learn and evolve iteratively. To validate the effectiveness of this approach, the study employs rigorous testing against three diverse benchmark datasets focused on general and custom domain knowledge, utilizing a LLM-as-a-Judge evaluation strategy. This comprehensive framework not only underscores the transformative potential of feedback-driven enhancements in RAG systems but also sets a precedent for future research in adaptive information retrieval technologies, marking a significant step in the journey towards autonomous refinement and optimization through user engagement.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 8 of its 13 references that are in the map. First posted on arXiv 13 Jul 2026.

Builds on

8 of 13 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation5 May
  2. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System11 Jan

Other references · 6

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2024User-LLM: Efficient LLM Contextualization with User Embeddingscited by 74
  5. 2024Self-Improving Customer Review Response Generation Based on LLMscited by 5
  6. 2024LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancementcited by 4

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  1. 2026Feedback Adaptation for Retrieval-Augmented Generation
  2. 2025Test-time Corpus Feedback: From Retrieval to RAG
  3. 2025RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback
  4. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control
  5. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  6. 2024Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback
  7. 2024Deploying Large Language Models With Retrieval Augmented Generation
  8. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation