Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources
AMKOR fuses parametric and retrieved knowledge using probabilistic beam search, achieving state-of-the-art multi-hop QA performance.
Key Findings
Methodology
AMKOR employs a generative architecture integrating parametric knowledge from large language models (LLMs) with external retrievals. It features a knowledge fusion module using scaled dot-product attention, a probabilistic beam search for exploring multiple reasoning paths, and a multi-granular training strategy balancing local reasoning accuracy and global answer consistency. The model’s core components include a knowledge retrieval module, a path exploration mechanism, and a loss function that jointly optimizes reasoning steps and final answers. This setup allows dynamic knowledge fusion and robust multi-path reasoning, significantly reducing cascading errors and handling knowledge conflicts effectively.
Key Results
- On HotpotQA, MuSiQue, WikiMQA, and Bamboogle, AMKOR outperforms SOTA methods with an average F1 increase of 2.5%. MuSiQue scores reached 37.4, Bamboogle 75.2, demonstrating superior handling of complex multi-hop reasoning. Ablation studies show probabilistic beam search contributes most to performance gains, with removal causing a 7% drop. In noisy knowledge settings, AMKOR maintains high robustness, with F1 scores of 58.9 at 20% noise, outperforming ProbTree’s 51.7. The multi-granular training strategy ensures stability across reasoning depths.
Significance
This work advances multi-hop QA by effectively integrating heterogeneous knowledge sources and exploring multiple reasoning trajectories. It addresses key challenges like knowledge conflicts, error propagation, and computational efficiency, paving the way for more reliable and interpretable AI systems capable of complex reasoning in real-world scenarios. Its scalable architecture and robustness to noise make it suitable for deployment in diverse knowledge-intensive applications, from medical diagnosis to legal analysis.
Technical Contribution
AMKOR introduces a novel fusion mechanism combining parametric and external knowledge via attention, employs probabilistic beam search for multi-path exploration, and adopts a multi-granular loss to optimize reasoning consistency. These innovations enable the model to handle conflicting information, reduce cascading errors, and explore diverse reasoning paths efficiently, setting a new benchmark for multi-hop QA performance and robustness.
Novelty
This is the first framework to systematically incorporate probabilistic beam search into multi-source knowledge fusion for multi-hop QA, enabling dynamic path exploration and conflict resolution. Unlike prior methods that rely on single-path or static retrieval, AMKOR’s multi-path exploration significantly enhances reasoning accuracy, especially in noisy or conflicting environments, representing a fundamental shift in multi-hop reasoning paradigms.
Limitations
- Despite improvements, the model still faces challenges in extremely noisy environments with severe knowledge conflicts, which can lead to incorrect path selection. Computational costs remain higher than simpler models due to multi-path exploration. Additionally, reliance on retrieval quality limits performance in domains with sparse or low-quality external data, necessitating further research into more robust retrieval and fusion mechanisms.
Future Work
Future directions include developing more efficient path exploration algorithms, integrating structured knowledge graphs for better reasoning consistency, and applying reinforcement learning to optimize path selection. Extending the framework to handle real-time knowledge updates and domain adaptation will further enhance its practical utility in dynamic, knowledge-rich environments.
AI Executive Summary
Multi-hop question answering (QA) is a critical challenge in natural language processing, demanding systems to connect multiple pieces of evidence across diverse knowledge sources. Existing approaches often struggle with knowledge conflicts, error propagation, and computational inefficiency, especially when reasoning over noisy or heterogeneous data. To address these issues, this paper introduces AMKOR, an innovative framework that combines large language models with external retrievals through a dynamic knowledge fusion mechanism. Central to AMKOR is the probabilistic beam search, which explores multiple reasoning paths simultaneously, reducing the risk of cascading errors and improving robustness.
The architecture integrates a scaled dot-product attention-based fusion module, enabling the model to adaptively fuse parametric and retrieved knowledge at each reasoning step. The multi-granular training strategy further ensures that the model optimizes both local reasoning accuracy and global answer consistency, leading to more stable and reliable performance. Extensive experiments on four benchmark datasets—HotpotQA, MuSiQue, WikiMQA, and Bamboogle—demonstrate that AMKOR surpasses state-of-the-art methods, achieving an average F1 improvement of 2.5%. Notably, in complex reasoning scenarios, the model maintains high accuracy even under noisy knowledge conditions.
The significance of this work lies in its ability to effectively handle knowledge conflicts, explore multiple reasoning trajectories, and scale to real-world applications. Its robustness to noisy data and superior performance on challenging datasets mark a substantial step forward in multi-hop QA research. While challenges remain in computational efficiency and extreme noise environments, AMKOR sets a new benchmark for future developments in knowledge-intensive AI systems, promising broader impacts across industries such as healthcare, legal, and enterprise decision-making.
Deep Dive
Abstract
Multi-source multi-hop question answering (QA) represents a challenging task in natural language processing due to the need for dynamic integration of heterogeneous knowledge sources and multi-step reasoning. Existing methods often suffer from cascading errors, insufficient handling of knowledge conflicts, and computational inefficiency. In this paper, we propose Adaptive Multi-source Knowledge-Oriented Reasoning (AMKOR), a generative framework that leverages large language models (LLMs) to dynamically fuse parametric and retrieved knowledge while exploring reasoning trajectories using probabilistic beam reasoning. AMKOR is further enhanced by a multi-granular learning strategy, optimizing both local reasoning steps and global answer accuracy. Experiments conducted on four widely-used multi-hop QA datasets, including HotpotQA and MuSiQue, demonstrate that AMKOR achieves state-of-the-art performance, significantly outperforming baseline methods on both reasoning accuracy and robustness. Additional analyses confirm its scalability, adaptability to noisy knowledge, and superior ability to handle complex multi-hop tasks. This work establishes a new benchmark for multi-source multi-hop QA by effectively combining reasoning quality and efficiency.