The method Syfer is introduced, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default and attains competitive accuracy while striking a favourable balance between performance and computational cost.
Abstract
Multilingual retrieval-augmented generation (mRAG) equips large language models with access to globally distributed external knowledge for complex multilingual question answering. Recent approaches either translate retrieved documents into English or the query language to bridge the cross-lingual semantic gap, or decompose a complex query into sub-questions and aggregate the intermediate reasoning process. However, both lines of work suffer from two limitations. First, one-size-fits-all translation alignment, blanket translation discards culturally and linguistically native information unique to the target language, introduces translation noise, and inflates system cost. Second, greedy decomposition and aggregation, uncontrolled decomposition produces redundant sub-questions that compound errors during step-wise reasoning, and the final aggregation over reasoning paths further amplifies these errors. We address both with our method Syfer, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default. Syfer first invokes a format-constrained decomposer to produce a sub-question graph in the original language, followed by a decomposition-quality check; when the check passes, sub-questions are answered sequentially under a retrieve-then-answer policy in the target language, and the English translation pathway with bilingual sub-question graph alignment is activated only when the check fails. Experiments across multiple languages show that Syfer attains competitive accuracy while striking a favourable balance between performance and computational cost.
Retrieval-augmented generation (RAG) has become a standard paradigm for knowledge-intensive question answering by grounding large language models (LLMs) in external evidence. However, open-domain multi-hop question answering (QA) remains challenging for two reasons. First, evidence dispersion across documents and non-contiguous spans means that critical bridge evidence can be weakly related to query and is easy to miss. Second, semantic-resolution mismatch complicates retrieval: coarser retrieval views offer better global coherence but may obscure the exact bridging detail, while finer-grained views highlight specific mentions but may omit the context needed to reveal the relation. In this paper, we propose MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA. To mitigate the semantic-resolution mismatch, MCoRe enables multi-entry complementary retrieval by indexing entry units at multiple semantic resolutions (entities, sentences, and summaries) with explicit links to chunk evidence, mapping all hits back to chunks, and fusing cross-resolution hits via chunk-level voting to form a compact evidence set for answer generation. To cope with evidence dispersion, MCoRe performs reflection-guided iteration: when evidence is insufficient, it identifies the missing bridge cue and issues a gap-focused follow-up query to recover it. Empirical results demonstrate the effectiveness of MCoRe, which consistently outperforms state-of-the-art baselines by 6.77 EM points and 8.79 F1 points averaged over three multi-hop QA benchmarks, with gains of up to 12.70 EM and 14.06 F1 points on 2Wiki.
Juxiang Zeng, Zhuohui Gao, Zhe Hou et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
Jiaoyang Li, Junhao Ruan, Shengwei Tang et al.· 0 citations
Translating natural language into graph query languages (NL2GQL) enables non-expert users to access graph databases, but supervised parsers depend on large annotated corpora and costly retraining whenever the schema evolves. Few-shot in-context learning offers a training-free alternative, yet its effectiveness hinges on demonstration selection, and conventional retrieval based on sentence-level matching and a single similarity signal often overlooks beneficial exemplars. We propose MGDR-SDR, a training-free approach that applies constituency parsing to derive three complementary representations of the utterance: a shallow abstraction preserving fine-grained lexical and syntactic detail, a deep abstraction exposing the intent-level syntactic skeleton, and a noun phrase list capturing concrete graph elements. These representations drive a multi-granular retrieval procedure that aggregates evidence across dense and sparse channels, yielding broader and more precise demonstration coverage. We evaluate MGDR-SDR on SPARQL generation over KQA Pro and Cypher generation over ZOGRASCOPE. Without any training, it reaches 86.58% Hits@1 on KQA Pro, surpassing strong supervised parsers and setting a new state-of-the-art among few-shot methods, and it transfers across GQL formalisms, attaining the best iid accuracy and a balanced generalization profile that surpasses all open-source few-shot baselines on ZOGRASCOPE. These results show that syntax-derived, multi-granular retrieval is an effective and generalizable strategy for few-shot NL2GQL semantic parsing.
Tong Li, Zhipeng Du, Xinyu Hu et al.· Machine Learning and Knowled...· 0 citations
DocuMind is a fully offline, privacy-preserving, multilingual Document Question Answering system built on the Retrieval-Augmented Generation (RAG) architecture, enabling true cross-lingual retrieval without any translation step.
S. S, Sripalreddy· International Scientific Jou...· 0 citations
This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus for precise SPARQL generation.
Yu Zhang, Ran Song, Xiaofei Gao et al.· Proceedings of the 32nd ACM...· 0 citations
Results demonstrate a 157% improvement in retrieval quality over a Naive-RAG baseline, with only 50 ms additional la tency, while Qwen2-VL-2B-Instruct achieved results comparable to cloud-based models in BERTScore, validate that open-source optimized SLMs, paired with advanced retrieval strategies, can provide competitive performance for document understanding without relying on cloud-based models.