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ADAPT Centre

Academic institutioneurope · ie
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Research library6linked papers
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Selected work

Representative Papers

When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection

Nov 06, 2025

To address the proliferation of misinformation, existing large language model (LLM)-based fact-checking approaches suffer from high computational overhead, severe hallucination risks, and poor deployability. This paper proposes DeReC, a lightweight and efficient fact verification framework that pioneers the integration of general-purpose text embeddings with dense retrieval—replacing LLM-based generative reasoning—and introduces a dedicated classifier for end-to-end verification. By preserving semantic understanding while eliminating autoregressive generation, DeReC significantly reduces computational cost. Experiments show that DeReC achieves an F1 score of 65.58% on RAWFC, outperforming the state-of-the-art L-Defense (61.20%) and accelerating inference by 20× (95% runtime reduction); on LIAR-RAW, it achieves 12× speedup (92% reduction). This work is the first to empirically validate the superiority of non-generative dense retrieval for fact-checking, establishing a new paradigm for scalable, low-cost, and robust verification systems.

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CRaFT: An Explanation-Based Framework for Evaluating Cultural Reasoning in Multilingual Language Models

Oct 15, 2025

Current evaluations of multilingual large language models’ (LLMs’) cultural reasoning capabilities rely predominantly on answer accuracy, neglecting interpretability and cross-linguistic comparability. To address this, we propose CRaFT—the first explanation-based framework for cross-cultural reasoning assessment. CRaFT introduces a four-dimensional explanatory quality metric: cultural fluency, deviation, consistency, and linguistic adaptability. Leveraging the World Values Survey, we construct a culturally grounded, multilingual question–explanation dataset covering Arabic, Bengali, and Spanish (2,100+ instances). Empirical analysis reveals salient language-specific patterns: Arabic responses exhibit lower cultural fluency; Bengali reasoning achieves higher overall quality; GPT-4 demonstrates strong linguistic adaptability but weak consistency; conversely, FANAR shows high stability yet limited flexibility. CRaFT establishes a novel, interpretable, decomposable, and cross-linguistically comparable paradigm for evaluating culturally intelligent multilingual LLMs.

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Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

Sep 11, 2025

To address inaccurate and poorly interpretable job-title matching in resume recommendation systems—caused by low lexical overlap or semantic ambiguity—this paper proposes a hierarchical matching method integrating semantic modeling with domain knowledge. Methodologically: (1) it introduces a self-supervised hybrid architecture coupling fine-tuned SBERT with a graph neural network, explicitly injecting domain knowledge graphs into semantic matching; (2) it designs a hierarchical evaluation strategy that performs fine-grained analysis across semantic relevance intervals, revealing model-behavior discrepancies obscured by global metrics. Experiments show a 25% reduction in RMSE over strong baselines on the high-relevance subset, significantly improving both matching accuracy and decision interpretability. The core contribution is a knowledge-enhanced hierarchical semantic alignment framework that jointly improves matching robustness and explainability.

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Recent publications

Latest Papers

When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection

Nov 06, 2025

To address the proliferation of misinformation, existing large language model (LLM)-based fact-checking approaches suffer from high computational overhead, severe hallucination risks, and poor deployability. This paper proposes DeReC, a lightweight and efficient fact verification framework that pioneers the integration of general-purpose text embeddings with dense retrieval—replacing LLM-based generative reasoning—and introduces a dedicated classifier for end-to-end verification. By preserving semantic understanding while eliminating autoregressive generation, DeReC significantly reduces computational cost. Experiments show that DeReC achieves an F1 score of 65.58% on RAWFC, outperforming the state-of-the-art L-Defense (61.20%) and accelerating inference by 20× (95% runtime reduction); on LIAR-RAW, it achieves 12× speedup (92% reduction). This work is the first to empirically validate the superiority of non-generative dense retrieval for fact-checking, establishing a new paradigm for scalable, low-cost, and robust verification systems.

0 citationsRead paper

CRaFT: An Explanation-Based Framework for Evaluating Cultural Reasoning in Multilingual Language Models

Oct 15, 2025

Current evaluations of multilingual large language models’ (LLMs’) cultural reasoning capabilities rely predominantly on answer accuracy, neglecting interpretability and cross-linguistic comparability. To address this, we propose CRaFT—the first explanation-based framework for cross-cultural reasoning assessment. CRaFT introduces a four-dimensional explanatory quality metric: cultural fluency, deviation, consistency, and linguistic adaptability. Leveraging the World Values Survey, we construct a culturally grounded, multilingual question–explanation dataset covering Arabic, Bengali, and Spanish (2,100+ instances). Empirical analysis reveals salient language-specific patterns: Arabic responses exhibit lower cultural fluency; Bengali reasoning achieves higher overall quality; GPT-4 demonstrates strong linguistic adaptability but weak consistency; conversely, FANAR shows high stability yet limited flexibility. CRaFT establishes a novel, interpretable, decomposable, and cross-linguistically comparable paradigm for evaluating culturally intelligent multilingual LLMs.

0 citationsRead paper

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

Sep 11, 2025

To address inaccurate and poorly interpretable job-title matching in resume recommendation systems—caused by low lexical overlap or semantic ambiguity—this paper proposes a hierarchical matching method integrating semantic modeling with domain knowledge. Methodologically: (1) it introduces a self-supervised hybrid architecture coupling fine-tuned SBERT with a graph neural network, explicitly injecting domain knowledge graphs into semantic matching; (2) it designs a hierarchical evaluation strategy that performs fine-grained analysis across semantic relevance intervals, revealing model-behavior discrepancies obscured by global metrics. Experiments show a 25% reduction in RMSE over strong baselines on the high-relevance subset, significantly improving both matching accuracy and decision interpretability. The core contribution is a knowledge-enhanced hierarchical semantic alignment framework that jointly improves matching robustness and explainability.

0 citationsRead paper