We have 4 of 33 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
QRAKEN is a training-free, ontology-agnostic neurosymbolic pipeline grounding generation in empirical graph evidence rather than schema expectations, supporting the value of empirical patterns beyond schema exposure.
Results show that structured schema context and lightweight prompting can substantially reduce reliance on fine-tuning for scalable conversational access to KGs, though closing the remaining gap to fully fine-tuned approaches will likely require reducing dependence on curated question-query exemplars.
A large-scale legal IR dataset constructed from Court of Justice of the European Union judgments, which supports both the development and rigorous evaluation of legal IR methods, at multiple query-document levels (case-to-case, paragraph-to-case, and paragraph-to-paragraph retrieval).
The results reveal that text restoration of missing text caused by physical lacunae in damaged ancient manuscripts using language models cannot be fully automated, but it can serve as a useful tool to assist paleographers in their work.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
