This study introduces KriRAG, a novel Retrieval-Augmented Generation (RAG) architecture designed to assist criminal investigators in analyzing information and overcoming the challenge of information overload. KriRAG structures and summarizes extensive document collections based on existing investigative queries, providing relevant document references and detailed answers for each query. Working with unstructured data from two homicide case files comprising approximately 3,700 documents and 13,000 pages, a comprehensive evaluation methodology is established, incorporating semantic retrieval, scoring, reasoning, and query response accuracy. The system’s outputs are evaluated against queries and answers provided by criminal investigators, demonstrating promising performance with 97.5% accuracy in relevance assessment and 77.5% accuracy for query responses. These findings provide a rigorous foundation for other query-oriented and open-ended retrieval applications. KriRAG is designed to run offline on limited hardware, ensuring sensitive data handling and on-device availability.

This paper explores the joint task of machine translation and sentence compression, emphasizing its application in subtitle generation for broadcast and live media for low-resource languages and hardware. We develop CLSC (Cross-Lingual Sentence Compression), a system trained on openly available parallel corpora organized by compression ratios, where the target length is constrained to a fraction of the source sentence length. We present two training methods: 1) Multiple Models (MM), where individual models are trained separately for each compression ratio, and 2) a Controllable Model (CM), a single model per language using a compression token to encode length constraints. We evaluate both subtitle data and transcriptions from the EuroParl corpus. To accommodate low-resource settings, we constrain data sampling for training and show results for transcriptions in French, Hungarian, Lithuanian, and Polish and subtitles in Albanian, Basque, Malay, and Norwegian. Our models preserve high semantic meaning and metric evaluations for compressed contexts.

This paper explores retrieval with sentence embeddings by fine-tuning sentence-transformer models for classification while preserving their ability to capture semantic similarity. To evaluate this balance, we introduce two opposing metrics – polarity score and semantic similarity score – that measure the model’s capacity to separate classes and retain semantic relationships between sentences. We propose a system that augments supervised datasets with contrastive pairs and triplets, training models under various configurations and evaluating their performance on top-k sentence retrieval. Experiments on two binary classification tasks demonstrate that reducing the margin parameter of loss functions greatly mitigates the trade-off between the metrics. These findings suggest that a single fine-tuned model can effectively handle joint classification and retrieval tasks, particularly in low-resource settings, without relying on multiple specialized models.

This study explores the capabilities of open-weight Large Language Models in a zero-shot learning setting, testing their ability to classify the content of customer service dialogues in Norwegian from a single instruction, named the BRAGE benchmark. By comparing results against widely used downstream tasks such as question-answering and named entity recognition, we find that (1) specific instruction models greatly exceed base models on the benchmark, (2) both English and multilingual instruction models outperform the tested Norwegian models of similar sizes, and (3) the difference between base and instruction models is less pronounced than in other generative tasks, suggesting that BRAGE is a challenging benchmark, requiring precise and generalizable instruction-tuning.

This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokmål and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC). While NorNE is aligned with an older version of the treebank, NARC is misaligned and requires extensive transformation from the original annotations to the UD structure and CoNLL-U format. Here, we demonstrate the conversion and alignment processes, along with an analysis of discovered issues and errors in the data, some of which include data split overlaps in the original treebank. These procedures and the developed system may prove helpful for future work on processing and aligning data from universal dependencies. The merged corpora comprise the first Norwegian UD treebank enriched with named entities and coreference information, supporting the standardized format for the CorefUD initiative.