Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025)

Abstract: Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges—such as hallucinations and semantic drift—for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments.

## introduction in recent years, applying retrieval-augmented generation (rag) to knowledge-intensive question-answering tasks has achieved significant progress @xcite @xcite . however, multi-hop question answering tasks @xcite still face a fundamental challenge: these tasks often lack a single answer document, requiring the logical decomposition of the original question into interrelated sub-questions, with the final answer derived through multi-step reasoning and cross-document retrieval @xcite . two main approaches have emerged to address the multi-hop qa problem in rag. the first is iterative question decomposition, which dynamically generates subquestions based on previous q&a interactions and retrieved documents @xcite @xcite . this approach is heavily dependent on the quality of the context; when lightweight language models (e.g., those with fewer than 10b parameters) process long-range contexts, semantic drift and logical discontinuities are prone to occur @xcite . the second approach is the "generatecorrect" paradigm, wherein intermediate answers are directly generated and subsequently corrected via retrieval to address hallucinations @xcite @xcite . although these methods perform well in large-scale models such as the gpt series @xcite , their effectiveness diminishes in lightweight llms. compared to larger models, lightweight llms are more prone to hallucinations and exhibit greater sensitivity to erroneous information. even minor uncorrected hallucinations during answer generation can lead to complete reasoning failures, further complicating robust reasoning. in response to the issues inherent in the approaches mentioned above, we propose a resource-friendly dynamic enhancement chain (dec) method. as illustrated in figure 1 , dec first employs llms to decompose the original question into a sequence of semantically complete sub-questions, thereby forming a purely logical reasoning chain that avoids the injection of hallucinations at intermediate steps. subsequently, the sub-questions are progressively refined through iterative retrieval to resolve ambiguities and supplement key reasoning information, ultimately producing accurate and reliable inferences. to enhance the efficiency of our method, we address two key challenges: (1) when directly decomposing the reasoning chain, the resulting sub-questions often lack effective retrieval keywords (for example, due to missing entity coreference resolution). we address this by employing a context-aware rewriting strategy that dynamically updates the sub-question formulations based on prior reasoning results to suit retrieval needs better. (2) to ensure retrieval precision, we integrate a lightweight module to extract discriminative keywords from the question that can differentiate key documents. during document retrieval, these discriminative keywords are used for targeted recall. this strategy improves the recall rate of gold documents at a relatively low computational cost, thereby enhancing overall model performance. experimental results on three multi-hop qa datasets demonstrate that dec achieves performance comparable to or surpassing state-of-theart benchmarks while significantly reducing token consumption. notably, our method attains stateof-the-art results when applied to models with 8 billion parameters, underscoring its effectiveness in resource-constrained scenarios. ## methodology this section introduces the dec method based on extended query and dynamic context augmentation, the core process of which is illustrated in figure 2 . the technique gradually resolves complex questions through a multi-stage iterative approach comprising four key steps. firstly, a large language model is employed to parse the user's complex question into a logically coherent chain-of-thought expressed in natural language, thereby establishing a structured framework for question decomposition. secondly, a dynamic query rewriting mechanism is devised to address the context dependency inherent in subsequent sub-questions. except for the initial node, each sub-question is semantically expanded based on the cumulative qa context. in this manner, the large language model dynamically supplements any missing key information to generate an optimized query amenable to retrieval. a precise recall method is presented to optimize retrieval performance. before retrieval, a keyword extraction system automatically extracts distinguishing keywords from the query. during retrieval, documents are filtered based on a combination of relevance scores and keyword-matching degrees, thereby facilitating the precise recall of key evidence. finally, the rewritten queries and retrieved documents are submitted to the large language model to generate answers. once an answer is obtained, the optimized query and its corresponding answer are incorporated into the qa context. this iterative process continues until every node in the initially generated logical chain-of-thought has been addressed, ultimately yielding the answer to the complex question. ## experiments this section systematically evaluates the effectiveness of the proposed method in typical multi-hop reasoning scenarios. we conducted comparative experiments on three benchmark multi-hop qa datasets and performed performance comparisons with current mainstream baseline models. ## conclusions in summary, our study presents a novel rag paradigm. the dec utilizes llms to directly generate logical reasoning chains, thereby minimizing the introduction of hallucinated information. it also employs iterative query rewriting to incorporate key details that may have been overlooked during the reasoning and querying processes. furthermore, in the document retrieval phase, we implement a query strategy enhanced by discriminative keywords, effectively improving the recall rate of crucial documents. experimental results indicate that across multiple datasets and assessments using commercial closed-source and lightweight llms, our method achieves-or even exceeds-the performance of existing approaches while consuming significantly fewer computational resources. meanwhile, our approach also excels in identifying unanswerable questions. these advantages are particularly notable when applied to lightweight models with lower parameter counts.