Semantic-Symbolic Knowledge Consensus for Multilingual Question Answering

Bilingual semantic correspondence through knowledge distillation and encoder combination

The challenge of cross-lingual semantic similarity detection is a significant problem in the context of multilingual educational software tools. This paper proposes a novel approach using ensemble learning and knowledge distillation for the development of an efficient and interpretable cross-lingual semantic similarity detection model for the English-French language pair. The methodology is based on the fusion of knowledge from the MiniLM encoder representation using a lightweight attention mechanism, LaBSE encoder with support for language-independent semantic representations, and the BERT encoder with the ability to produce dense contextual vector representations. The knowledge is then distilled using a Multi-Layer Perceptron (MLP) architecture for the development of the semantic similarity detection model. The experimental results show that the ensemble architecture attains a validation F1-score of 0.930, while the knowledge distillation student model retains a robust F1-score of 0.918 with a low computational footprint (3.17M parameters, 42MB memory, 5.77ms inference). This demonstrates the viability of knowledge distillation for the transfer of ensemble-level semantic knowledge into a compact architecture for the context of resource-constrained educational tools.

Optimising Retrieval for Linguistic Question-Answering in European Portuguese: A Benchmark on Ciberdúvidas Da Língua Portuguesa

A novel benchmark framework for linguistic QA retrieval, empirical evidence supporting monolingual IR-specialised models, and insights into retrieval robustness under paraphrastic variation are included, enabling improved QA systems for specialised and low-resource environments.

Augmenting Datasets for Fine-Tuning Large Language Models Using Semantic Variations

This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.

MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA

This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.

Generating then Refining for Reliable Knowledge Base Question Answering

L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages

We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs). The benchmark comprises 3,471 curriculum-grounded English question--answer pairs spanning nine domains, curated from educational curricula, competitive examination materials, and domain-specific reference books. We introduce a practical hybrid construction strategy that combines context-grounded LLM-based question generation and validation with semantic deduplication and human verification, enabling scalable creation of benchmark data while preserving annotation quality. The benchmark is translated into 19 Indic languages, yielding a publicly released multilingual dataset of 69,420 question--answer pairs across 20 languages. We evaluate six LLMs under three protocols: LLM-as-a-judge and two deterministic lexical criteria, exact-substring and word-overlap matching. All three produce almost the same model ranking, showing that the results do not depend on the choice of judge. The frontier commercial model leads by a wide margin, and among open-weight models Gemma4 31B outperforms the Indic-specialised Sarvam 30B in every evaluated Indic language.