Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 9 references
Abstract
Abstract-Recently, large language models (LLMs) have enabled significant advances across a wide range of domains. However, the training, adaptation, and evaluation of LLMs with a large number of parameters incur substantial computational and financial costs. This limits the ability of researchers with constrained resources to effectively leverage LLM-based approaches. In this study, we examine the performance of relatively small-scale LLMs for question answering and retrieval-augmented generation (RAG) on Turkish legal texts under limited computational resources. We investigate several factors affecting Turkish RAG performance and adapt embedding models to the target domain using automatically generated question-context pairs. The findings indicate that relatively small, domain-adapted models can achieve performance levels close to those of larger-scale or commercial models on the target dataset.
MGAL is the first multilingual, granularity- and position-aware long-context benchmark, constructed from United Nations reports spanning 8K to 128K tokens across the six official UN languages, and finds that LLMs perform well at word-level tasks but struggle with coarser-grained ones.
Chunhan Li, Chenglin Xu, Zongyang Zhang et al.· 0 citations
This work developed a modular retrieval-augmented generation (RAG) pipeline and conducted a series of ablation experiments over its individual components to identify the best-performing strategy at each stage, demonstrating that isolated curation of RAG components can yield strong performance for Ukrainian document gro...
М. В. Носенко, Pavlo Kilko, Markus Reuter et al.· 1 citation
A structure-aware RAG framework is proposed that models Bengali textbooks as hierarchical graphs and uses a contrastively trained graph neural network to retrieve a small set of relevant passages, enabling topic-specific multiple-choice question (MCQ) generation and in-domain answer prediction.
Abu Tarabin Surzo, A. K. M. Nihalul Kabir, Sm Azmain Faysal et al.· 0 citations
Although Large Language Models (LLMs) have progressed significantly, Retrieval-Augmented Generation (RAG) still falls short for under-resourced Indic languages. Existing embedding systems, built primarily for English, face issues with script mismatches and limited word coverage, leading to near-complete retrieval failu...
A novel knowledge graph-based evaluation framework is proposed introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating structural and semantic similarity into a continuous evaluation score, alongside a diagnostic framework for categorizing reasoning errors.
Subavarshana Arumugam, Mamta Nallaretnam, K. Wickramasinghe et al.· 0 citations
