Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS

• URL: http://arxiv.org/abs/2604.10734v1
• Date: Sun, 12 Apr 2026 17:14:36 GMT
• Title: Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS
• Abstract summary: Self-Correcting RAG reformulates retrieval and generation as constrained optimization and path planning.<n>On the input side, we formalize context selection as a multi-dimensional multiple-choice knapsack problem (MMKP)<n>On the output side, we introduce a natural language inference (NLI)-guided Monte Carlo Tree Search (MCTS) mechanism.
• Score: 5.711356514732554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) substantially extends the knowledge boundary of large language models. However, it still faces two major challenges when handling complex reasoning tasks: low context utilization and frequent hallucinations. To address these issues, we propose Self-Correcting RAG, a unified framework that reformulates retrieval and generation as constrained optimization and path planning. On the input side, we move beyond traditional greedy retrieval and, for the first time, formalize context selection as a multi-dimensional multiple-choice knapsack problem (MMKP), thereby maximizing information density and removing redundancy under a strict token budget. On the output side, we introduce a natural language inference (NLI)-guided Monte Carlo Tree Search (MCTS) mechanism, which leverages test-time compute to dynamically explore reasoning trajectories and validate the faithfulness of generated answers. Experiments on six multi-hop question answering and fact-checking datasets demonstrate that our method significantly improves reasoning accuracy on complex queries while effectively reducing hallucinations, outperforming strong existing baselines.Our code is available at https://github.com/xjiacs/Self-Correcting-RAG .

Related papers

• Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version) [0.0]
  Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.<n>Retrieval-augmented generation (RAG) systems have emerged as a common deployment scenario seeking to avoid the well known risk of the LLM "hallucinating" information.<n>In this work, we explore the idea of using a lightweight graph structure with a relatively simple graph schema, to support the RAG subsystem via a dedicated toolset.
  arXiv  Detail & Related papers  (2026-06-04T09:07:06Z)
• Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs [55.78708003681562]
  Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation.<n>Existing RAG approaches typically retrieve and unstructured text fragments as context.<n>We propose Tri-RAG, a structured triplet-based retrieval framework that improves retrieval efficiency through reasoning-aligned context construction.
  arXiv  Detail & Related papers  (2026-04-14T11:36:29Z)
• CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction [50.67483317563736]
  This paper aims to explore a system that can think step-by-step, look up information if needed, generate results, self-evaluate its own results, and refine the results.<n>We introduce CoT-Seg, a training-free framework that rethinks reasoning segmentation by combining chain-of-thought reasoning with self-correction.
  arXiv  Detail & Related papers  (2026-01-24T11:41:54Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering [64.62317305868264]
  We present textbfKBQA-R1, a framework that shifts the paradigm from text imitation to interaction optimization via Reinforcement Learning.<n>Treating KBQA as a multi-turn decision process, our model learns to navigate the knowledge base using a list of actions.<n>Experiments on WebQSP, GrailQA, and GraphQuestions demonstrate that KBQA-R1 achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-10T17:45:42Z)
• Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations [70.94563079082751]
  E-commerce has exposed the limitations of traditional product retrieval systems in managing complex, multi-turn user interactions.<n>We propose a novel framework that introduces test-time scaling into conversational multimodal product retrieval.<n>Our approach builds on a generative retriever, further augmented with a test-time reranking mechanism that improves retrieval accuracy and better aligns results with evolving user intent throughout the dialogue.
  arXiv  Detail & Related papers  (2025-08-25T15:38:56Z)
• MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search [61.11836311160951]
  We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks.<n>Unlike standard RAG methods, which typically retrieve information independently from reasoning, MCTS-RAG combines structured reasoning with adaptive retrieval.<n>This integrated approach enhances decision-making, reduces hallucinations, and ensures improved factual accuracy and response consistency.
  arXiv  Detail & Related papers  (2025-03-26T17:46:08Z)
• DeepRAG: Thinking to Retrieve Step by Step for Large Language Models [92.87532210660456]
  We propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP)<n>By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step.<n> Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 26.4%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• 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)
• SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine [27.274219226254026]
  Multi-hop Question Answering (MHQA) remains challenging for many existing models. We propose the Self-Guiding prompting Finite State Machine (SG-FSM) to strengthen multi-hop reasoning abilities.
  arXiv  Detail & Related papers  (2024-10-22T13:47:38Z)
• Context-Augmented Code Generation Using Programming Knowledge Graphs [0.0]
  Large Language Models (LLMs) and Code-LLMs (CLLMs) frequently face difficulties when dealing with challenging and complex problems.<n>We present a novel framework that leverages a Programming Knowledge Graph (PKG) to semantically represent and retrieve code.
  arXiv  Detail & Related papers  (2024-10-09T16:35:41Z)

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.